diff --git a/404.html b/404.html
index 950f637..1c37e2e 100644
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 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
     
     <a class="navbar-brand me-2" href="https://jorainer.github.io/SpectraTutorials/index.html">SpectraTutorials</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
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-    <a class="dropdown-item" href="https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
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 <li class="nav-item">
@@ -85,13 +85,11 @@
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
+  <p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/Abstract-EuroBioc2020.html b/Abstract-EuroBioc2020.html
index 8ffe7a5..87871dc 100644
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+++ b/Abstract-EuroBioc2020.html
@@ -1,16 +1,16 @@
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-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
     
     <a class="navbar-brand me-2" href="index.html">SpectraTutorials</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -24,9 +24,9 @@
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
     <a class="dropdown-item" href="articles/Spectra-backends.html">Spectra: an expandable infrastructure to handle mass spectrometry data</a>
     <a class="dropdown-item" href="articles/Spectra-matching-with-MetaboAnnotation.html">MS/MS Spectra Matching with the `MetaboAnnotation` Package</a>
-    <a class="dropdown-item" href="articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
   </div>
 </li>
 <li class="nav-item">
@@ -63,11 +63,11 @@
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
+  <p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/Abstract-EuroBioc2022.html b/Abstract-EuroBioc2022.html
index d076ec6..0e21ce2 100644
--- a/Abstract-EuroBioc2022.html
+++ b/Abstract-EuroBioc2022.html
@@ -1,16 +1,16 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Abstract • SpectraTutorials</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Abstract"><meta property="og:image" content="https://jorainer.github.io/SpectraTutorials/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Abstract • SpectraTutorials</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Abstract"><meta property="og:image" content="https://jorainer.github.io/SpectraTutorials/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
     
     <a class="navbar-brand me-2" href="index.html">SpectraTutorials</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -24,9 +24,9 @@
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
     <a class="dropdown-item" href="articles/Spectra-backends.html">Spectra: an expandable infrastructure to handle mass spectrometry data</a>
     <a class="dropdown-item" href="articles/Spectra-matching-with-MetaboAnnotation.html">MS/MS Spectra Matching with the `MetaboAnnotation` Package</a>
-    <a class="dropdown-item" href="articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
   </div>
 </li>
 <li class="nav-item">
@@ -63,11 +63,11 @@
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
+  <p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/Abstract-EuroBioc2023.html b/Abstract-EuroBioc2023.html
index 45c6913..396fdae 100644
--- a/Abstract-EuroBioc2023.html
+++ b/Abstract-EuroBioc2023.html
@@ -1,16 +1,16 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Spectra: an expandable infrastructure to handle mass spectrometry data • SpectraTutorials</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Spectra: an expandable infrastructure to handle mass spectrometry data"><meta property="og:image" content="https://jorainer.github.io/SpectraTutorials/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Spectra: an expandable infrastructure to handle mass spectrometry data • SpectraTutorials</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Spectra: an expandable infrastructure to handle mass spectrometry data"><meta property="og:image" content="https://jorainer.github.io/SpectraTutorials/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
     
     <a class="navbar-brand me-2" href="index.html">SpectraTutorials</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -24,9 +24,9 @@
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
     <a class="dropdown-item" href="articles/Spectra-backends.html">Spectra: an expandable infrastructure to handle mass spectrometry data</a>
     <a class="dropdown-item" href="articles/Spectra-matching-with-MetaboAnnotation.html">MS/MS Spectra Matching with the `MetaboAnnotation` Package</a>
-    <a class="dropdown-item" href="articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
   </div>
 </li>
 <li class="nav-item">
@@ -64,11 +64,11 @@
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
+  <p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
 </div>
 
     </footer></div>
diff --git a/apple-touch-icon-120x120.png b/apple-touch-icon-120x120.png
index 19fcf0d..d7905e7 100644
Binary files a/apple-touch-icon-120x120.png and b/apple-touch-icon-120x120.png differ
diff --git a/apple-touch-icon-152x152.png b/apple-touch-icon-152x152.png
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diff --git a/articles/Spectra-backends.html b/articles/Spectra-backends.html
index e090778..120f4be 100644
--- a/articles/Spectra-backends.html
+++ b/articles/Spectra-backends.html
@@ -17,7 +17,7 @@
 <link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
 <script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
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-<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Spectra: an expandable infrastructure to handle mass spectrometry data">
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 <meta property="og:description" content="SpectraTutorials">
 <meta property="og:image" content="https://jorainer.github.io/SpectraTutorials/logo.png">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
@@ -29,11 +29,11 @@
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
     
     <a class="navbar-brand me-2" href="../index.html">SpectraTutorials</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
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@@ -48,9 +48,9 @@
 <li class="active nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
     <a class="dropdown-item" href="../articles/Spectra-backends.html">Spectra: an expandable infrastructure to handle mass spectrometry data</a>
     <a class="dropdown-item" href="../articles/Spectra-matching-with-MetaboAnnotation.html">MS/MS Spectra Matching with the `MetaboAnnotation` Package</a>
-    <a class="dropdown-item" href="../articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
   </div>
 </li>
 <li class="nav-item">
@@ -96,7 +96,7 @@ <h4 data-toc-skip class="author">Johannes
 
     
     
-<p><strong>Last modified:</strong> 2023-12-05 09:09:17.874995<br><strong>Compiled</strong>: Tue Dec 5 09:13:59 2023</p>
+<p><strong>Last modified:</strong> 2024-05-17 14:04:17.420737<br><strong>Compiled</strong>: Fri May 17 14:10:20 2024</p>
 <div class="section level2">
 <h2 id="abstract">Abstract<a class="anchor" aria-label="anchor" href="#abstract"></a>
 </h2>
@@ -106,7 +106,7 @@ <h2 id="abstract">Abstract<a class="anchor" aria-label="anchor" href="#abstract"
 lead to ever growing data sizes and increased number of available
 variables making standard in-memory data handling and processing
 difficult.</p>
-<p>The <em><a href="https://bioconductor.org/packages/3.18/Spectra" class="external-link">Spectra</a></em>
+<p>The <em><a href="https://bioconductor.org/packages/3.19/Spectra" class="external-link">Spectra</a></em>
 package provides a modern infrastructure for MS data handling
 specifically designed to enable extension to additional data resources
 or alternative data representations. These can be realized by extending
@@ -144,15 +144,17 @@ <h3 id="pre-requisites">Pre-requisites<a class="anchor" aria-label="anchor" href
 <h3 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
 </h3>
 <ul>
+<li><p>This version of the tutorial bases on package versions available
+through <strong>Bioconductor release 3.19</strong>.</p></li>
 <li><p>Get the <a href="https://hub.docker.com/r/jorainer/spectra_tutorials" class="external-link">docker
 image</a> of this tutorial with
-<code>docker pull jorainer/spectra_tutorials:latest</code>.</p></li>
+<code>docker pull jorainer/spectra_tutorials:RELEASE_3_19</code>.</p></li>
 <li>
 <p>Start docker using</p>
 <pre><code>docker run \
     -e PASSWORD=bioc \
     -p 8787:8787 \
-    jorainer/spectra_tutorials:latest</code></pre>
+    jorainer/spectra_tutorials:RELEASE_3_19</code></pre>
 </li>
 <li><p>Enter <code>http://localhost:8787</code> in a web browser and log
 in with username <code>rstudio</code> and password
@@ -304,7 +306,7 @@ <h3 id="creating-and-using-spectra-objects">Creating and using <code>Spectra</co
 <code>Spectra</code> object, even if they are not defined (in which case
 missing values are returned). Spectra variables available from a
 <code>Spectra</code> object can be listed with the
-<code>spectraVariables</code> function:</p>
+<code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraVariables()</a></code> function:</p>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' List all available spectra variables</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraVariables</a></span><span class="op">(</span><span class="va">s</span><span class="op">)</span></span></code></pre></div>
@@ -373,7 +375,7 @@ <h3 id="creating-and-using-spectra-objects">Creating and using <code>Spectra</co
 <span><span class="co">## [17] "isolationWindowUpperMz"  "id"                     </span></span>
 <span><span class="co">## [19] "name"</span></span></code></pre>
 <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksVariables</a></span><span class="op">(</span><span class="va">s</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksVariables</a></span><span class="op">(</span><span class="va">s</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## [1] "mz"        "intensity"</span></span></code></pre>
 <p>Spectra are organized linearly, i.e., one after the other. Thus, our
 <code>Spectra</code> has a length of 3 with each element being one
@@ -412,14 +414,14 @@ <h3 id="creating-and-using-spectra-objects">Creating and using <code>Spectra</co
 <span><span class="co">## [15] "isolationWindowLowerMz"  "isolationWindowTargetMz"</span></span>
 <span><span class="co">## [17] "isolationWindowUpperMz"  "id"                     </span></span>
 <span><span class="co">## [19] "name"                    "new_variable"</span></span></code></pre>
-<p>The full peak data can be extracted with the <code>peaksData</code>
+<p>The full peak data can be extracted with the <code><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData()</a></code>
 function, that returns a <code>list</code> of two-dimensional arrays
 with the values of the peak variables. Each list element represents the
 peak data from one spectrum, which is stored in a
 e.g. <code>matrix</code> with columns being the peak variables and rows
 the respective values for each peak. The number of rows of such peak
 variable arrays depends on the number of mass peaks of each spectrum.
-This number can be extracted using <code>lengths</code>:</p>
+This number can be extracted using <code><a href="https://rdrr.io/r/base/lengths.html" class="external-link">lengths()</a></code>:</p>
 <div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Get the number of peaks per spectrum.</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/lengths.html" class="external-link">lengths</a></span><span class="op">(</span><span class="va">s</span><span class="op">)</span></span></code></pre></div>
@@ -428,7 +430,7 @@ <h3 id="creating-and-using-spectra-objects">Creating and using <code>Spectra</co
 subset that to data from the second spectrum.</p>
 <div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Extract the peaks matrix of the second spectrum</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">s</span><span class="op">)</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
+<span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">s</span><span class="op">)</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
 <pre><code><span><span class="co">##          mz intensity</span></span>
 <span><span class="co">## [1,]  83.10     6.685</span></span>
 <span><span class="co">## [2,]  96.12     4.381</span></span>
@@ -452,11 +454,11 @@ <h3 id="creating-and-using-spectra-objects">Creating and using <code>Spectra</co
 <p><img src="Spectra-backends_files/figure-html/unnamed-chunk-13-1.png" width="700"></p>
 <p>Experimental MS data is generally (ideally) stored in files in mzML,
 mzXML or CDF format. These are open file formats that can be read by
-most software. In Bioconductor, the <em><a href="https://bioconductor.org/packages/3.18/mzR" class="external-link">mzR</a></em> package
+most software. In Bioconductor, the <em><a href="https://bioconductor.org/packages/3.19/mzR" class="external-link">mzR</a></em> package
 allows to read/write data in/to these formats. Also <em>Spectra</em>
 allows to represent data from such raw data files. To illustrate this we
 below create a <code>Spectra</code> object for from two mzML files that
-are provided in Bioconductor’s <em><a href="https://bioconductor.org/packages/3.18/msdata" class="external-link">msdata</a></em>
+are provided in Bioconductor’s <em><a href="https://bioconductor.org/packages/3.19/msdata" class="external-link">msdata</a></em>
 package.</p>
 <div class="sourceCode" id="cb33"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Import MS data from 2 mzML files.</span></span>
@@ -566,13 +568,13 @@ <h3 id="use-of-different-backends-with-spectra-why-and-how">Use of different <em
 <p>Examples:</p>
 <ul>
 <li>
-<code>MsBackendMgf</code> (defined in <em><a href="https://bioconductor.org/packages/3.18/MsBackendMgf" class="external-link">MsBackendMgf</a></em>)
+<code>MsBackendMgf</code> (defined in <em><a href="https://bioconductor.org/packages/3.19/MsBackendMgf" class="external-link">MsBackendMgf</a></em>)
 adds support for MS files in MGF file format.</li>
 <li>
-<code>MsBackendMsp</code> (defined in <em><a href="https://bioconductor.org/packages/3.18/MsBackendMsp" class="external-link">MsBackendMsp</a></em>
+<code>MsBackendMsp</code> (defined in <em><a href="https://bioconductor.org/packages/3.19/MsBackendMsp" class="external-link">MsBackendMsp</a></em>
 adds support for MSP files.</li>
 <li>
-<code>MsBackendRawFileReader</code> (defined in <em><a href="https://bioconductor.org/packages/3.18/MsBackendRawFileReader" class="external-link">MsBackendRawFileReader</a></em>)
+<code>MsBackendRawFileReader</code> (defined in <em><a href="https://bioconductor.org/packages/3.19/MsBackendRawFileReader" class="external-link">MsBackendRawFileReader</a></em>)
 adds support for MS data files in Thermo Fisher Scientific’s raw data
 file format.</li>
 </ul>
@@ -591,18 +593,18 @@ <h3 id="use-of-different-backends-with-spectra-why-and-how">Use of different <em
 <p>Examples:</p>
 <ul>
 <li>
-<code>MsBackendMemory</code> (defined in <em><a href="https://bioconductor.org/packages/3.18/Spectra" class="external-link">Spectra</a></em>):
+<code>MsBackendMemory</code> (defined in <em><a href="https://bioconductor.org/packages/3.19/Spectra" class="external-link">Spectra</a></em>):
 keeps all the MS data in memory and is optimized for a fast and
 efficient access to the peaks data matrices (i.e., the <em>m/z</em> and
 intensity values of the spectra).</li>
 <li>
-<code>MsBackendMzR</code> (defined in <em><a href="https://bioconductor.org/packages/3.18/Spectra" class="external-link">Spectra</a></em>):
+<code>MsBackendMzR</code> (defined in <em><a href="https://bioconductor.org/packages/3.19/Spectra" class="external-link">Spectra</a></em>):
 keeps only spectra variables in memory and retrieves the peaks data
 on-the-fly from the original data files upon request. This guarantees a
 lower memory footprint and enables also analysis of large MS
 experiments.</li>
 <li>
-<code>MsBackendSql</code> (defined in the <em><a href="https://bioconductor.org/packages/3.18/MsBackendSql" class="external-link">MsBackendSql</a></em>
+<code>MsBackendSql</code> (defined in the <em><a href="https://bioconductor.org/packages/3.19/MsBackendSql" class="external-link">MsBackendSql</a></em>
 package): all MS data is stored in a SQL database. Has minimal memory
 requirements but any data (whether spectra or peaks variables) need to
 be retrieved from the database. Depending on the SQL database system
@@ -614,13 +616,13 @@ <h3 id="use-of-different-backends-with-spectra-why-and-how">Use of different <em
 change the backend for a <code>Spectra</code> object.</p>
 <div class="sourceCode" id="cb45"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Change the backend to MsBackendMemory</span></span>
-<span><span class="va">s_mem</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">setBackend</a></span><span class="op">(</span><span class="va">s_mzr</span>, <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/MsBackend.html" class="external-link">MsBackendMemory</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span><span class="va">s_mem</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/backendInitialize.html" class="external-link">setBackend</a></span><span class="op">(</span><span class="va">s_mzr</span>, <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/MsBackend.html" class="external-link">MsBackendMemory</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p>With the call above we loaded all MS data from the original data
 files into memory. As a third option we next store the full MS data into
 a SQL database by using/changing to a <code>MsBackendOfflineSql</code>
-backend defined by the <em><a href="https://bioconductor.org/packages/3.18/MsBackendSql" class="external-link">MsBackendSql</a></em>
+backend defined by the <em><a href="https://bioconductor.org/packages/3.19/MsBackendSql" class="external-link">MsBackendSql</a></em>
 package.</p>
-<p>For the <code>setBackend</code> call we need to provide the
+<p>For the <code><a href="https://rdrr.io/pkg/ProtGenerics/man/backendInitialize.html" class="external-link">setBackend()</a></code> call we need to provide the
 connection information for the database that should contain the data.
 This includes the database driver (parameter <code>drv</code>, depending
 on the database system), the database name (parameter
@@ -638,12 +640,12 @@ <h3 id="use-of-different-backends-with-spectra-why-and-how">Use of different <em
 <code class="sourceCode R"><span><span class="co">#' Change the backend to a SQL representation of the data.</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/RforMassSpectrometry/MsBackendSql" class="external-link">MsBackendSql</a></span><span class="op">)</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://rsqlite.r-dbi.org" class="external-link">RSQLite</a></span><span class="op">)</span></span>
-<span><span class="va">s_db</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">setBackend</a></span><span class="op">(</span><span class="va">s_mzr</span>, <span class="fu"><a href="https://rdrr.io/pkg/MsBackendSql/man/MsBackendOfflineSql.html" class="external-link">MsBackendOfflineSql</a></span><span class="op">(</span><span class="op">)</span>, drv <span class="op">=</span> <span class="fu"><a href="https://rsqlite.r-dbi.org/reference/SQLite.html" class="external-link">SQLite</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span><span class="va">s_db</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/backendInitialize.html" class="external-link">setBackend</a></span><span class="op">(</span><span class="va">s_mzr</span>, <span class="fu"><a href="https://rdrr.io/pkg/MsBackendSql/man/MsBackendOfflineSql.html" class="external-link">MsBackendOfflineSql</a></span><span class="op">(</span><span class="op">)</span>, drv <span class="op">=</span> <span class="fu"><a href="https://rsqlite.r-dbi.org/reference/SQLite.html" class="external-link">SQLite</a></span><span class="op">(</span><span class="op">)</span>,</span>
 <span>                   dbname <span class="op">=</span> <span class="st">"ms_backend_sql_test.sqlite"</span>,</span>
 <span>                   BPPARAM <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocParallel/man/SerialParam-class.html" class="external-link">SerialParam</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p><em>Note</em>: a more efficient way to import MS data from data files
-into a SQL database is the <code>createMsBackendSqlDatabase</code> from
-the <em>MsBackendSql</em> package. Also, for larger data sets it is
+into a SQL database is the <code><a href="https://rdrr.io/pkg/MsBackendSql/man/MsBackendSql.html" class="external-link">createMsBackendSqlDatabase()</a></code>
+from the <em>MsBackendSql</em> package. Also, for larger data sets it is
 suggested to use more advanced and powerful SQL database systems
 (e.g. MySQL/MariaDB SQL databases).</p>
 <p>We have now 3 <code>Spectra</code> objects representing the same MS
@@ -666,7 +668,7 @@ <h3 id="use-of-different-backends-with-spectra-why-and-how">Use of different <em
 and is thus ideal to represent even very large MS experiments.</p>
 <p>We next evaluate the performance to extract spectra variables. We
 compare the time needed to extract the retention times from the 3
-<code>Spectra</code> objects using the <code>microbenchmark</code>
+<code>Spectra</code> objects using the <code><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark()</a></code>
 function. With <code>register(SerialParam())</code> we globally disable
 parallel processing for <em>Spectra</em> to ensure the results to be
 independent that.</p>
@@ -681,10 +683,10 @@ <h3 id="use-of-different-backends-with-spectra-why-and-how">Use of different <em
 <span>    times <span class="op">=</span> <span class="fl">13</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Unit: microseconds</span></span>
-<span><span class="co">##          expr      min       lq       mean   median       uq       max neval</span></span>
-<span><span class="co">##  rtime(s_mem)   12.292   13.395   19.64815   19.877   26.169    31.269    13</span></span>
-<span><span class="co">##  rtime(s_mzr)  385.288  407.640  451.69762  419.863  445.912   836.380    13</span></span>
-<span><span class="co">##   rtime(s_db) 6983.250 7037.401 8782.54131 7179.236 7268.913 27920.037    13</span></span></code></pre>
+<span><span class="co">##          expr      min       lq     mean   median       uq       max neval</span></span>
+<span><span class="co">##  rtime(s_mem)   11.382   11.993   21.114   15.088   30.166    33.803    13</span></span>
+<span><span class="co">##  rtime(s_mzr)  373.938  401.499  477.649  437.677  467.292   894.068    13</span></span>
+<span><span class="co">##   rtime(s_db) 6960.776 7191.957 7600.690 7263.411 7319.565 11846.993    13</span></span></code></pre>
 <p>Highest performance can be seen for the <code>Spectra</code> object
 with the <code>MsBackendMemory</code> backend, followed by the
 <code>Spectra</code> object with the <code>MsBackendMzR</code> backend,
@@ -700,20 +702,20 @@ <h3 id="use-of-different-backends-with-spectra-why-and-how">Use of different <em
 <div class="sourceCode" id="cb55"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Compare performance to extract the peaks data.</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">s_mem</span><span class="op">)</span>,</span>
-<span>    <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">s_mzr</span><span class="op">)</span>,</span>
-<span>    <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">s_db</span><span class="op">)</span>,</span>
+<span>    <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">s_mem</span><span class="op">)</span>,</span>
+<span>    <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">s_mzr</span><span class="op">)</span>,</span>
+<span>    <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">s_db</span><span class="op">)</span>,</span>
 <span>    times <span class="op">=</span> <span class="fl">7</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Unit: microseconds</span></span>
 <span><span class="co">##              expr        min          lq        mean     median          uq</span></span>
-<span><span class="co">##  peaksData(s_mem)    380.319    485.0095    704.9917    780.716    806.9495</span></span>
-<span><span class="co">##  peaksData(s_mzr) 487238.872 492587.2645 524201.2064 496422.977 500894.0630</span></span>
-<span><span class="co">##   peaksData(s_db)  89571.778  97788.3475 185583.6216 102420.344 210867.2850</span></span>
+<span><span class="co">##  peaksData(s_mem)    322.071    514.7805    618.1203    631.118    746.0615</span></span>
+<span><span class="co">##  peaksData(s_mzr) 506008.305 507137.8500 544841.2223 518705.426 519729.7670</span></span>
+<span><span class="co">##   peaksData(s_db)  98960.197 107680.4265 230061.3011 315034.282 322345.5675</span></span>
 <span><span class="co">##         max neval</span></span>
-<span><span class="co">##    1189.989     7</span></span>
-<span><span class="co">##  698783.941     7</span></span>
-<span><span class="co">##  489781.964     7</span></span></code></pre>
+<span><span class="co">##     851.969     7</span></span>
+<span><span class="co">##  735439.591     7</span></span>
+<span><span class="co">##  336382.641     7</span></span></code></pre>
 <p>The <code>MsBackendMemory</code> outperforms the two other backends
 also in this comparison. Both the <code>MsBackendMzR</code> and
 <code>MsBackendOfflineSql</code> need to import this data, the
@@ -731,10 +733,10 @@ <h3 id="use-of-different-backends-with-spectra-why-and-how">Use of different <em
 <span>    <span class="va">s_db</span><span class="op">[</span><span class="va">idx</span><span class="op">]</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Unit: microseconds</span></span>
-<span><span class="co">##        expr     min       lq     mean  median       uq      max neval</span></span>
-<span><span class="co">##  s_mem[idx] 230.711 238.1690 246.4851 245.468 251.5495  314.927   100</span></span>
-<span><span class="co">##  s_mzr[idx] 642.047 664.8100 683.6366 676.962 690.1570 1084.352   100</span></span>
-<span><span class="co">##   s_db[idx] 133.279 141.9445 163.7531 147.515 153.4465 1633.125   100</span></span></code></pre>
+<span><span class="co">##        expr     min       lq     mean   median       uq      max neval</span></span>
+<span><span class="co">##  s_mem[idx] 229.318 242.9035 251.5439 250.0265 255.5270  348.079   100</span></span>
+<span><span class="co">##  s_mzr[idx] 655.824 674.9290 698.9652 688.8905 707.5500 1134.296   100</span></span>
+<span><span class="co">##   s_db[idx] 135.533 148.0010 169.9489 153.9575 161.9975 1632.686   100</span></span></code></pre>
 <p>Here, the <code>MsBackendOfflineSql</code> has a clear advantage over
 the two other backends, because it only needs to subset the integer
 vector of primary keys (the only data it keeps in memory), while the
@@ -769,8 +771,8 @@ <h3 id="data-manipulation-and-the-lazy-evaluation-queue">Data manipulation and t
 manipulation of <em>m/z</em> or intensity values to be directly written
 back to these original data files. Also other backends, specifically
 those that provide access to spectral reference databases (such as the
-<code>MsBackendMassbankSql</code> from the <em><a href="https://bioconductor.org/packages/3.18/MsBackendMassbank" class="external-link">MsBackendMassbank</a></em>
-package or the <code>MsBackendCompDb</code> from the <em><a href="https://bioconductor.org/packages/3.18/CompoundDb" class="external-link">CompoundDb</a></em>
+<code>MsBackendMassbankSql</code> from the <em><a href="https://bioconductor.org/packages/3.19/MsBackendMassbank" class="external-link">MsBackendMassbank</a></em>
+package or the <code>MsBackendCompDb</code> from the <em><a href="https://bioconductor.org/packages/3.19/CompoundDb" class="external-link">CompoundDb</a></em>
 package), are not supposed to allow any changes to the information
 stored in these databases.</p>
 <p>Data manipulations are however an integral part of any data analysis
@@ -1027,8 +1029,8 @@ <h3 id="data-manipulation-and-the-lazy-evaluation-queue">Data manipulation and t
 <span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Unit: milliseconds</span></span>
 <span><span class="co">##          expr      min       lq     mean   median       uq      max neval</span></span>
-<span><span class="co">##   rtime(s_db) 6.867144 6.935762 7.046637 6.957643 7.034997 8.032556    17</span></span>
-<span><span class="co">##  rtime(s_db2) 2.967924 3.004833 3.158521 3.054445 3.088198 4.590840    17</span></span></code></pre>
+<span><span class="co">##   rtime(s_db) 7.126546 7.206836 7.404466 7.287036 7.411337 8.363142    17</span></span>
+<span><span class="co">##  rtime(s_db2) 3.103891 3.168001 3.411078 3.298283 3.410914 4.949414    17</span></span></code></pre>
 <p><em>Note</em>: the <code>MsBackendCached</code> class from the
 <em>Spectra</em> package provides the internal
 <code>data.frame</code>-based caching mechanism described above. The
@@ -1089,7 +1091,7 @@ <h3 id="data-manipulation-and-the-lazy-evaluation-queue">Data manipulation and t
 <code>Spectra</code>’s (peaks) data manipulation functions. This
 mechanism is implemented directly for the <code>Spectra</code> class and
 backends thus do not have to implement their own.</p>
-<p>When calling <code>filterIntensity</code> above, the data was
+<p>When calling <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">filterIntensity()</a></code> above, the data was
 actually not modified, but the function to filter the peaks data was
 added to this processig queue. The function along with all possibly
 defined parameters was added as a <code>ProcessingStep</code>
@@ -1116,7 +1118,7 @@ <h3 id="data-manipulation-and-the-lazy-evaluation-queue">Data manipulation and t
 <span><span class="co">##         return(x)</span></span>
 <span><span class="co">##     x[which(between(x[, "intensity"], intensity)), , drop = FALSE]</span></span>
 <span><span class="co">## }</span></span>
-<span><span class="co">## &lt;bytecode: 0x55a2b582b590&gt;</span></span>
+<span><span class="co">## &lt;bytecode: 0x55d51d043af0&gt;</span></span>
 <span><span class="co">## &lt;environment: namespace:Spectra&gt;</span></span></code></pre>
 <p>and all of its parameters with:</p>
 <div class="sourceCode" id="cb87"><pre class="downlit sourceCode r">
@@ -1128,7 +1130,7 @@ <h3 id="data-manipulation-and-the-lazy-evaluation-queue">Data manipulation and t
 <span><span class="co">## $msLevel</span></span>
 <span><span class="co">## [1] 1</span></span></code></pre>
 <p>Each time peaks data is accessed (like with the
-<code>intensity</code> call in the example below), the
+<code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">intensity()</a></code> call in the example below), the
 <code>Spectra</code> object will first request the <em>raw</em> peaks
 data from the backend, check its own processing queue and, if that is
 not empty, apply each of the contained cached processing steps to the
@@ -1173,26 +1175,27 @@ <h3 id="data-manipulation-and-the-lazy-evaluation-queue">Data manipulation and t
 <span>    <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">intensity</a></span><span class="op">(</span><span class="va">s_mem</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span>
 <span>    times <span class="op">=</span> <span class="fl">7</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Unit: milliseconds</span></span>
-<span><span class="co">##                    expr       min        lq       mean     median         uq</span></span>
-<span><span class="co">##  intensity(s_mem[1:10])  3.542345  3.699177   3.906552   3.882108   3.977156</span></span>
-<span><span class="co">##  intensity(s_mem)[1:10] 99.563948 99.721442 103.931024 100.194454 108.821023</span></span>
+<span><span class="co">##                    expr        min         lq       mean     median         uq</span></span>
+<span><span class="co">##  intensity(s_mem[1:10])   3.338558   3.469012   3.768346   3.688882   4.095887</span></span>
+<span><span class="co">##  intensity(s_mem)[1:10] 101.128773 102.028226 137.921287 104.121477 108.341070</span></span>
 <span><span class="co">##         max neval</span></span>
-<span><span class="co">##    4.568749     7</span></span>
-<span><span class="co">##  110.673833     7</span></span></code></pre>
+<span><span class="co">##    4.221185     7</span></span>
+<span><span class="co">##  339.460163     7</span></span></code></pre>
 <p>Next to the number of common peaks data manipulation methods that are
 already implemented for <code>Spectra</code>, and that all make use of
-this processing queue, there is also the <code>addProcessing</code>
+this processing queue, there is also the <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">addProcessing()</a></code>
 function that allows to apply any user-provided function to the peaks
 data (using the same lazy evaluation mechanism). As a simple example we
 define below a function that <em>scales</em> all intensities in a
 spectrum such that the total intensity sum per spectrum is 1. Functions
-for <code>addProcessing</code> are expected to take a peaks array as
+for <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">addProcessing()</a></code> are expected to take a peaks array as
 input and should again return a peaks array as their result. Note also
 that the <code>...</code> in the function definition below is required,
 because internally additional parameters, such as the spectrum’s MS
 level, are by default passed along to the function (see also the
-<code>addProcessing</code> documentation entry in <code><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">?Spectra</a></code>
-for more information and more advanced examples).</p>
+<code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">addProcessing()</a></code> documentation entry in
+<code><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">?Spectra</a></code> for more information and more advanced
+examples).</p>
 <div class="sourceCode" id="cb93"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Define a function that scales intensities</span></span>
 <span><span class="va">scale_sum</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span> <span class="op">{</span></span>
@@ -1221,7 +1224,7 @@ <h3 id="data-manipulation-and-the-lazy-evaluation-queue">Data manipulation and t
 (quantitative) MS1 data (as in our example here).</p>
 <p>Finally, since through this lazy evaluation mechanism we are not
 changing actual peaks data, we can also <em>undo</em> data
-manipulations. A simple <code>reset</code> call on a
+manipulations. A simple <code><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">reset()</a></code> call on a
 <code>Spectra</code> object will restore the data in the object to its
 initial state (in fact it simply clears the processing queue).</p>
 <div class="sourceCode" id="cb100"><pre class="downlit sourceCode r">
@@ -1269,7 +1272,7 @@ <h3 id="implementing-your-own-msbackend">Implementing your own <code>MsBackend</
 <h3 id="final-words">Final words<a class="anchor" aria-label="anchor" href="#final-words"></a>
 </h3>
 <ul>
-<li>The <em><a href="https://bioconductor.org/packages/3.18/Spectra" class="external-link">Spectra</a></em>
+<li>The <em><a href="https://bioconductor.org/packages/3.19/Spectra" class="external-link">Spectra</a></em>
 package provides a powerful infrastructure to handle and process MS data
 in R:
 <ul>
@@ -1293,7 +1296,7 @@ <h2 id="acknowledgments">Acknowledgments<a class="anchor" aria-label="anchor" hr
 <div class="section level2">
 <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
 </h2>
-<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">
 <div id="ref-gattoMSnbaseEfficientElegant2020a" class="csl-entry">
 Gatto, Laurent, Sebastian Gibb, and Johannes Rainer. 2020.
 <span>“<span>MSnbase</span>, <span>Efficient</span> and <span>Elegant
@@ -1311,13 +1314,11 @@ <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="a
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
+  <p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
 </div>
 
     </footer>
diff --git a/articles/Spectra-matching-with-MetaboAnnotation.html b/articles/Spectra-matching-with-MetaboAnnotation.html
index ffcb606..5a9ae79 100644
--- a/articles/Spectra-matching-with-MetaboAnnotation.html
+++ b/articles/Spectra-matching-with-MetaboAnnotation.html
@@ -17,7 +17,7 @@
 <link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
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+    <a class="dropdown-item" href="../articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
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     <a class="dropdown-item" href="../articles/Spectra-matching-with-MetaboAnnotation.html">MS/MS Spectra Matching with the `MetaboAnnotation` Package</a>
-    <a class="dropdown-item" href="../articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
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@@ -96,7 +96,7 @@ <h4 data-toc-skip class="author">Johannes
 
     
     
-<p><strong>Last modified:</strong> 2023-12-05 09:09:17.874995<br><strong>Compiled</strong>: Tue Dec 5 09:14:26 2023</p>
+<p><strong>Last modified:</strong> 2024-05-17 14:04:17.420737<br><strong>Compiled</strong>: Fri May 17 14:10:45 2024</p>
 <div class="section level2">
 <h2 id="overview">Overview<a class="anchor" aria-label="anchor" href="#overview"></a>
 </h2>
@@ -119,7 +119,7 @@ <h3 id="convenient-spectra-matching-using-metaboannotation">Convenient spectra m
 <p>In this example use case we match experimental MS2 spectra from a DDA
 experiment on a pesticide mix against reference spectra from MassBank.
 Below we load the experimental data file which is distributed
-<em>via</em> the <code>msdata</code> R package.</p>
+<em>via</em> the <em>msdata</em> R package.</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/RforMassSpectrometry/Spectra" class="external-link">Spectra</a></span><span class="op">)</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://rapporter.github.io/pander/" class="external-link">pander</a></span><span class="op">)</span></span>
@@ -144,8 +144,9 @@ <h3 id="convenient-spectra-matching-using-metaboannotation">Convenient spectra m
 <span><span class="co">#' Remove peaks with a single peak</span></span>
 <span><span class="va">pest</span> <span class="op">&lt;-</span> <span class="va">pest</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/lengths.html" class="external-link">lengths</a></span><span class="op">(</span><span class="va">pest</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fl">1</span><span class="op">]</span></span></code></pre></div>
 <p>This leads to a data set consisting of 2451 spectra. We next connect
-to a MassBank database (running within this docker image) and create a
-<code>Spectra</code> object representing that data.</p>
+to a MassBank database (release 2023.11, running within this docker
+image) and create a <code>Spectra</code> object representing that
+data.</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://rmariadb.r-dbi.org" class="external-link">RMariaDB</a></span><span class="op">)</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/RforMassSpectrometry/MsBackendMassbank" class="external-link">MsBackendMassbank</a></span><span class="op">)</span></span>
@@ -154,98 +155,128 @@ <h3 id="convenient-spectra-matching-using-metaboannotation">Convenient spectra m
 <span><span class="va">con</span> <span class="op">&lt;-</span> <span class="fu">dbConnect</span><span class="op">(</span><span class="fu"><a href="https://rmariadb.r-dbi.org/reference/dbConnect-MariaDBDriver-method.html" class="external-link">MariaDB</a></span><span class="op">(</span><span class="op">)</span>, user <span class="op">=</span> <span class="st">"massbank"</span>, dbname <span class="op">=</span> <span class="st">"MassBank"</span>,</span>
 <span>                 host <span class="op">=</span> <span class="st">"localhost"</span>, pass <span class="op">=</span> <span class="st">"massbank"</span><span class="op">)</span></span>
 <span><span class="va">mbank</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">Spectra</a></span><span class="op">(</span><span class="va">con</span>, source <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/MsBackendMassbank/man/MsBackendMassbankSql.html" class="external-link">MsBackendMassbankSql</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p>Alternatively, if no MySQL database system is available or if this
-tutorial can not be run within <em>docker</em>, an SQLite database
-version of the MassBank data (in the format required for
-<code>MsBackendMassbankSql</code> is available <a href="https://github.com/jorainer/SpectraTutorials/releases/tag/2021.03" class="external-link">here</a>.
-After downloading the database <em>MassbankSql-2021-03.db</em> to the
-current R workspace, the resource can be loaded with:</p>
+<p>Alternatively, MassBank spectral libraries are also provided through
+Bioconductor’s <em>AnnotationHub</em> and can be conveniently loaded
+from there. The code below first loads the <em>AnnotationHub</em>
+resource and then loads the data for one MassBank release. The database
+will be cached locally, avoiding thus to re-download the data for future
+use.</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co">## Alternative to the MySQL server - assuming the SQLite database was</span></span>
-<span><span class="co">## stored to the R working directory.</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://rsqlite.r-dbi.org" class="external-link">RSQLite</a></span><span class="op">)</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/RforMassSpectrometry/MsBackendMassbank" class="external-link">MsBackendMassbank</a></span><span class="op">)</span></span>
-<span><span class="va">con</span> <span class="op">&lt;-</span> <span class="fu">dbConnect</span><span class="op">(</span><span class="fu"><a href="https://rsqlite.r-dbi.org/reference/SQLite.html" class="external-link">SQLite</a></span><span class="op">(</span><span class="op">)</span>, <span class="st">"MassbankSql-2021-03.db"</span><span class="op">)</span></span>
-<span><span class="va">mbank</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">Spectra</a></span><span class="op">(</span><span class="va">con</span>, source <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/MsBackendMassbank/man/MsBackendMassbankSql.html" class="external-link">MsBackendMassbankSql</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="co">#' Load the AnnotationHub resource</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">AnnotationHub</span><span class="op">)</span></span>
+<span><span class="va">ah</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/AnnotationHub/man/AnnotationHub-class.html" class="external-link">AnnotationHub</a></span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co">#' List available MassBank release</span></span>
+<span><span class="fu"><a href="https://rdrr.io/pkg/AnnotationHub/man/AnnotationHub-class.html" class="external-link">query</a></span><span class="op">(</span><span class="va">ah</span>, <span class="st">"MassBank"</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## AnnotationHub with 6 records</span></span>
+<span><span class="co">## # snapshotDate(): 2024-04-29</span></span>
+<span><span class="co">## # $dataprovider: MassBank</span></span>
+<span><span class="co">## # $species: NA</span></span>
+<span><span class="co">## # $rdataclass: CompDb</span></span>
+<span><span class="co">## # additional mcols(): taxonomyid, genome, description,</span></span>
+<span><span class="co">## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,</span></span>
+<span><span class="co">## #   rdatapath, sourceurl, sourcetype </span></span>
+<span><span class="co">## # retrieve records with, e.g., 'object[["AH107048"]]' </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">##              title                                </span></span>
+<span><span class="co">##   AH107048 | MassBank CompDb for release 2021.03  </span></span>
+<span><span class="co">##   AH107049 | MassBank CompDb for release 2022.06  </span></span>
+<span><span class="co">##   AH111334 | MassBank CompDb for release 2022.12.1</span></span>
+<span><span class="co">##   AH116164 | MassBank CompDb for release 2023.06  </span></span>
+<span><span class="co">##   AH116165 | MassBank CompDb for release 2023.09  </span></span>
+<span><span class="co">##   AH116166 | MassBank CompDb for release 2023.11</span></span></code></pre>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#' Load the MassBank release 2023.11 and get a Spectra object</span></span>
+<span><span class="va">mbank</span> <span class="op">&lt;-</span> <span class="va">ah</span><span class="op">[[</span><span class="st">"AH116166"</span><span class="op">]</span><span class="op">]</span> <span class="op">|&gt;</span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">Spectra</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p>It is suggested to apply the same processing applied to the query
+spectra also to the target (reference) spectra. Thus we below filter
+also the reference spectra from MassBank with the same intensity
+filter.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#' Remove low intensity peaks and subsequently remove</span></span>
+<span><span class="co">#' spectra with less than 2 peaks</span></span>
+<span><span class="va">mbank</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">filterIntensity</a></span><span class="op">(</span><span class="va">mbank</span>, intensity <span class="op">=</span> <span class="va">low_int</span><span class="op">)</span></span>
+<span><span class="va">mbank</span> <span class="op">&lt;-</span> <span class="va">mbank</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/lengths.html" class="external-link">lengths</a></span><span class="op">(</span><span class="va">mbank</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fl">1</span><span class="op">]</span></span></code></pre></div>
 <p>We could now directly calculate similarities between the 2451
-experimental (query) MS2 spectra and the 86576 MassBank reference
-(target) spectra using the <code>compareSpectra</code> method, but this
-would be computationally very intense because a similarity score would
-be calculated between each query and each target spectrum. As
-alternative we use here the <code>matchSpectra</code> function from the
-<a href="https://github.com/RforMassSpectrometry/MetaboAnnotation" class="external-link"><code>MetaboAnnotation</code></a>
+experimental (query) MS2 spectra and the 103525 MassBank reference
+(target) spectra using the <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">compareSpectra()</a></code> method, but
+this would be computationally very intense because a similarity score
+would be calculated between each query and each target spectrum. As
+alternative we use here the <code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/matchSpectra.html" class="external-link">matchSpectra()</a></code> function from
+the <em><a href="https://bioconductor.org/packages/3.19/MetaboAnnotation" class="external-link">MetaboAnnotation</a></em>
 package that allows to restrict similarity calculations between query
-and target spectra with similar m/z of their precursor ion or have a
-similar retention time.</p>
+and target spectra with similar <em>m/z</em> of their precursor ion (or
+considering also a similar retention time if available for reference
+spectra).</p>
 <p>Below we create a <code>CompareSpectraParam</code> object setting
 parameter <code>requirePrecursor = TRUE</code> (to restrict similarity
 calculations only to query and target spectra with a similar precursor
-m/z) and <code>ppm = 10</code> (m/z difference between the query and
-target precursor has to be within 10 ppm). Parameter
+<em>m/z</em>) and <code>ppm = 10</code> (<em>m/z</em> difference between
+the query and target precursor has to be within 10 ppm). Parameter
 <code>THRESHFUN</code> enables to define a <em>threshold function</em>
 that defines which spectra are considered matching. With the function
 used below only MS2 spectra with a similarity (calculated with the
 default <em>dotproduct</em> function) larger or equal to 0.8 are
 considered matching.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/RforMassSpectrometry/MetaboAnnotation" class="external-link">MetaboAnnotation</a></span><span class="op">)</span></span>
 <span><span class="va">prm</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/MetaboAnnotation/man/CompareSpectraParam.html" class="external-link">CompareSpectraParam</a></span><span class="op">(</span>ppm <span class="op">=</span> <span class="fl">10</span>, requirePrecursor <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>                           THRESHFUN <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/which.html" class="external-link">which</a></span><span class="op">(</span><span class="va">x</span> <span class="op">&gt;=</span> <span class="fl">0.8</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p>We next call <code>matchSpectra</code> with this parameter object and
-pass <code>pest</code> and <code>mbank</code> as query and target
-<code>Spectra</code>, respectively. This takes approximately 1 minute to
-complete, which is not tremendously fast, but still much faster than a
-pairwise comparison between all query and target spectra would be.</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<span>                           THRESHFUN <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/which.html" class="external-link">which</a></span><span class="op">(</span><span class="va">x</span> <span class="op">&gt;=</span> <span class="fl">0.8</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<p>We next call <code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/matchSpectra.html" class="external-link">matchSpectra()</a></code> with this parameter object
+and pass <code>pest</code> and <code>mbank</code> as query and target
+<code>Spectra</code>, respectively.</p>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">mtch</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/MetaboAnnotation/man/matchSpectra.html" class="external-link">matchSpectra</a></span><span class="op">(</span><span class="va">pest</span>, <span class="va">mbank</span>, param <span class="op">=</span> <span class="va">prm</span><span class="op">)</span></span>
 <span><span class="va">mtch</span></span></code></pre></div>
 <pre><code><span><span class="co">## Object of class MatchedSpectra </span></span>
-<span><span class="co">## Total number of matches: 78 </span></span>
-<span><span class="co">## Number of query objects: 2451 (29 matched)</span></span>
-<span><span class="co">## Number of target objects: 86576 (47 matched)</span></span></code></pre>
+<span><span class="co">## Total number of matches: 113 </span></span>
+<span><span class="co">## Number of query objects: 2451 (39 matched)</span></span>
+<span><span class="co">## Number of target objects: 103525 (69 matched)</span></span></code></pre>
 <p>As a result we get a <code>MatchedSpectra</code> object that contains
 the query and target spectra as well as the matching result (i.e. the
 information which query spectrum matches with which target spectrum
-based on what similarity score). We can use the <code>query</code> and
-<code>target</code> functions to access the query and target
-<code>Spectra</code> objects and <code>matches</code> to extract the
+based on what similarity score). We can use the <code><a href="https://rdrr.io/pkg/AnnotationHub/man/AnnotationHub-class.html" class="external-link">query()</a></code> and
+<code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">target()</a></code> functions to access the query and target
+<code>Spectra</code> objects and <code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">matches()</a></code> to extract the
 matching information. Below we display the first 6 rows of that
 matrix.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">matches</a></span><span class="op">(</span><span class="va">mtch</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##   query_idx target_idx     score</span></span>
-<span><span class="co">## 1       163      31941 0.9264249</span></span>
-<span><span class="co">## 2       163      32022 0.9264249</span></span>
-<span><span class="co">## 3       163      32214 0.9264249</span></span>
-<span><span class="co">## 4       163      32508 0.9264249</span></span>
-<span><span class="co">## 5       163      32667 0.9264249</span></span>
-<span><span class="co">## 6       163      32900 0.9264249</span></span></code></pre>
-<p>Functions <code>whichQuery</code> and <code>whichTarget</code> return
-the (unique) indices of the query and target spectra that could be
-matched.</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<span><span class="co">## 1       433      97386 0.9471924</span></span>
+<span><span class="co">## 2       433      97387 0.9258791</span></span>
+<span><span class="co">## 3       433      97388 0.8188538</span></span>
+<span><span class="co">## 4       435      97386 0.9745761</span></span>
+<span><span class="co">## 5       435      97387 0.8573438</span></span>
+<span><span class="co">## 6       493      68124 0.9089015</span></span></code></pre>
+<p>Functions <code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">whichQuery()</a></code> and <code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">whichTarget()</a></code>
+return the (unique) indices of the query and target spectra that could
+be matched.</p>
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">whichQuery</a></span><span class="op">(</span><span class="va">mtch</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">##  [1]  163  320  420  433  493  496  497  571  682  685  686  805  806  809  810</span></span>
-<span><span class="co">## [16]  819  829  983 1095 1454 1457 1706 1830 1834 1839 1906 2047 2048 2050</span></span></code></pre>
-<p>As we can see only few of the query spectra (29 of the 2451 spectra)
+<pre><code><span><span class="co">##  [1]  433  435  493  496  497  571  682  685  686  805  807  809  810  819  829</span></span>
+<span><span class="co">## [16]  983 1095 1127 1266 1281 1283 1454 1457 1480 1706 1828 1830 1834 1839 1874</span></span>
+<span><span class="co">## [31] 1882 1901 1906 2038 2040 2043 2050 2124 2309</span></span></code></pre>
+<p>As we can see only few of the query spectra (39 of the 2451 spectra)
 could be matched. This is in part because for a large proportion spectra
-in MassBank no precursor m/z is available and with
+in MassBank no precursor <em>m/z</em> is available and with
 <code>requirePrecursor = TRUE</code> these are not considered in the
 similarity calculation. Setting <code>requirePrecursor = FALSE</code>
 would calculate a similarity between all spectra (even those with
-missing precursor information) but calculations can take up to several
-hours.</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+missing precursor information) but calculations would take much
+longer.</p>
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">precursorMz</a></span><span class="op">(</span><span class="va">mbank</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## [1] 24498</span></span></code></pre>
+<pre><code><span><span class="co">## [1] 23519</span></span></code></pre>
 <p>The <code>MatchedSpectra</code> object inherits much of the
-functionality of a <code>Spectra</code> object.
-<code>spectraVariables</code> returns for example all the available
-<em>spectra variables</em>, from both the query as well as the target
-<code>Spectra</code>. The variable names of the latter are prefixed with
-<code>target_</code> to discriminate them from the variable names of the
-query.</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+functionality of a <code>Spectra</code> object. The
+<code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraVariables()</a></code> function returns for example all the
+available <em>spectra variables</em>, from both the query as well as the
+target <code>Spectra</code>. The variable names of the latter are
+prefixed with <code>target_</code> to discriminate them from the
+variable names of the query.</p>
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraVariables</a></span><span class="op">(</span><span class="va">mtch</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##  [1] "msLevel"                        "rtime"                         </span></span>
 <span><span class="co">##  [3] "acquisitionNum"                 "scanIndex"                     </span></span>
@@ -264,37 +295,40 @@ <h3 id="convenient-spectra-matching-using-metaboannotation">Convenient spectra m
 <span><span class="co">## [29] "injectionTime"                  "filterString"                  </span></span>
 <span><span class="co">## [31] "spectrumId"                     "ionMobilityDriftTime"          </span></span>
 <span><span class="co">## [33] "scanWindowLowerLimit"           "scanWindowUpperLimit"          </span></span>
-<span><span class="co">## [35] "target_msLevel"                 "target_rtime"                  </span></span>
-<span><span class="co">## [37] "target_acquisitionNum"          "target_scanIndex"              </span></span>
-<span><span class="co">## [39] "target_dataStorage"             "target_dataOrigin"             </span></span>
-<span><span class="co">## [41] "target_centroided"              "target_smoothed"               </span></span>
-<span><span class="co">## [43] "target_polarity"                "target_precScanNum"            </span></span>
-<span><span class="co">## [45] "target_precursorMz"             "target_precursorIntensity"     </span></span>
-<span><span class="co">## [47] "target_precursorCharge"         "target_collisionEnergy"        </span></span>
-<span><span class="co">## [49] "target_isolationWindowLowerMz"  "target_isolationWindowTargetMz"</span></span>
-<span><span class="co">## [51] "target_isolationWindowUpperMz"  "target_spectrum_id"            </span></span>
-<span><span class="co">## [53] "target_spectrum_name"           "target_date"                   </span></span>
-<span><span class="co">## [55] "target_authors"                 "target_license"                </span></span>
-<span><span class="co">## [57] "target_copyright"               "target_publication"            </span></span>
-<span><span class="co">## [59] "target_splash"                  "target_compound_id"            </span></span>
-<span><span class="co">## [61] "target_adduct"                  "target_ionization"             </span></span>
-<span><span class="co">## [63] "target_ionization_voltage"      "target_fragmentation_mode"     </span></span>
-<span><span class="co">## [65] "target_collision_energy_text"   "target_instrument"             </span></span>
-<span><span class="co">## [67] "target_instrument_type"         "target_formula"                </span></span>
-<span><span class="co">## [69] "target_exactmass"               "target_smiles"                 </span></span>
-<span><span class="co">## [71] "target_inchi"                   "target_inchikey"               </span></span>
-<span><span class="co">## [73] "target_cas"                     "target_pubchem"                </span></span>
-<span><span class="co">## [75] "target_synonym"                 "target_precursor_mz_text"      </span></span>
-<span><span class="co">## [77] "target_compound_name"           "score"</span></span></code></pre>
-<p>We can access spectra individual variables using <code>$</code> and
+<span><span class="co">## [35] ".original_query_index"          "target_msLevel"                </span></span>
+<span><span class="co">## [37] "target_rtime"                   "target_acquisitionNum"         </span></span>
+<span><span class="co">## [39] "target_scanIndex"               "target_dataStorage"            </span></span>
+<span><span class="co">## [41] "target_dataOrigin"              "target_centroided"             </span></span>
+<span><span class="co">## [43] "target_smoothed"                "target_polarity"               </span></span>
+<span><span class="co">## [45] "target_precScanNum"             "target_precursorMz"            </span></span>
+<span><span class="co">## [47] "target_precursorIntensity"      "target_precursorCharge"        </span></span>
+<span><span class="co">## [49] "target_collisionEnergy"         "target_isolationWindowLowerMz" </span></span>
+<span><span class="co">## [51] "target_isolationWindowTargetMz" "target_isolationWindowUpperMz" </span></span>
+<span><span class="co">## [53] "target_compound_id"             "target_formula"                </span></span>
+<span><span class="co">## [55] "target_exactmass"               "target_smiles"                 </span></span>
+<span><span class="co">## [57] "target_inchi"                   "target_inchikey"               </span></span>
+<span><span class="co">## [59] "target_cas"                     "target_pubchem"                </span></span>
+<span><span class="co">## [61] "target_name"                    "target_spectrum_id"            </span></span>
+<span><span class="co">## [63] "target_spectrum_name"           "target_date"                   </span></span>
+<span><span class="co">## [65] "target_authors"                 "target_license"                </span></span>
+<span><span class="co">## [67] "target_copyright"               "target_publication"            </span></span>
+<span><span class="co">## [69] "target_splash"                  "target_precursorMz_text"       </span></span>
+<span><span class="co">## [71] "target_adduct"                  "target_ionization"             </span></span>
+<span><span class="co">## [73] "target_ionization_voltage"      "target_fragmentation_mode"     </span></span>
+<span><span class="co">## [75] "target_collisionEnergy_text"    "target_instrument"             </span></span>
+<span><span class="co">## [77] "target_instrument_type"         "target_original_spectrum_id"   </span></span>
+<span><span class="co">## [79] "target_predicted"               "target_msms_mz_range_min"      </span></span>
+<span><span class="co">## [81] "target_msms_mz_range_max"       "target_synonym"                </span></span>
+<span><span class="co">## [83] "score"</span></span></code></pre>
+<p>We can access individual spectra variables using <code>$</code> and
 the variable name, or multiple variables with the
-<code>spectraData</code> function. Below we extract the retention time,
-the precursor m/z of the query spectrum, the precursor m/z of the target
-spectrum as well as the similarity score from the object using the
-<code>spectraData</code> function.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraData()</a></code> function. Below we extract the retention
+time, the precursor <em>m/z</em> of the query spectrum, the precursor
+<em>m/z</em> of the target spectrum as well as the similarity score from
+the object using the <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraData()</a></code> function.</p>
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraData</a></span><span class="op">(</span><span class="va">mtch</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"rtime"</span>, <span class="st">"precursorMz"</span>, <span class="st">"target_precursorMz"</span>, <span class="st">"score"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## DataFrame with 2500 rows and 4 columns</span></span>
+<pre><code><span><span class="co">## DataFrame with 2525 rows and 4 columns</span></span>
 <span><span class="co">##          rtime precursorMz target_precursorMz     score</span></span>
 <span><span class="co">##      &lt;numeric&gt;   &lt;numeric&gt;          &lt;numeric&gt; &lt;numeric&gt;</span></span>
 <span><span class="co">## 1        7.216    137.9639                 NA        NA</span></span>
@@ -303,456 +337,653 @@ <h3 id="convenient-spectra-matching-using-metaboannotation">Convenient spectra m
 <span><span class="co">## 4       23.085    207.0294                 NA        NA</span></span>
 <span><span class="co">## 5       27.385    121.0990                 NA        NA</span></span>
 <span><span class="co">## ...        ...         ...                ...       ...</span></span>
-<span><span class="co">## 2496   895.182    137.9850                 NA        NA</span></span>
-<span><span class="co">## 2497   895.472     56.0495                 NA        NA</span></span>
-<span><span class="co">## 2498   896.252    142.9611                 NA        NA</span></span>
-<span><span class="co">## 2499   896.662     53.0129                 NA        NA</span></span>
-<span><span class="co">## 2500   898.602     91.5022                 NA        NA</span></span></code></pre>
+<span><span class="co">## 2521   895.182    137.9850                 NA        NA</span></span>
+<span><span class="co">## 2522   895.472     56.0495                 NA        NA</span></span>
+<span><span class="co">## 2523   896.252    142.9611                 NA        NA</span></span>
+<span><span class="co">## 2524   896.662     53.0129                 NA        NA</span></span>
+<span><span class="co">## 2525   898.602     91.5022                 NA        NA</span></span></code></pre>
 <p>The returned <code>DataFrame</code> contains the matching information
 for the full data set, i.e. of each query spectrum and hence, returns
 <code>NA</code> values for query spectra that could not be matched with
 a target spectrum. Note also that query spectra matching multiple target
 spectra are represented by multiple rows (one for each matching target
 spectrum).</p>
-<p>Here we’re only interested in query spectra for which a match was
-found and thus we subset the <code>MatchedSpectra</code> to query
-spectra with a matching target spectrum.</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">mtch</span> <span class="op">&lt;-</span> <span class="va">mtch</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">whichQuery</a></span><span class="op">(</span><span class="va">mtch</span><span class="op">)</span><span class="op">]</span></span></code></pre></div>
+<p>Here we’re only interested in query spectra matching at least one
+target spectrum and hence subset the matching result to these using the
+<code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">whichQuery()</a></code> function.</p>
+<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">mtch</span> <span class="op">&lt;-</span> <span class="va">mtch</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">whichQuery</a></span><span class="op">(</span><span class="va">mtch</span><span class="op">)</span><span class="op">]</span></span>
+<span><span class="va">mtch</span></span></code></pre></div>
+<pre><code><span><span class="co">## Object of class MatchedSpectra </span></span>
+<span><span class="co">## Total number of matches: 113 </span></span>
+<span><span class="co">## Number of query objects: 39 (39 matched)</span></span>
+<span><span class="co">## Number of target objects: 103525 (69 matched)</span></span></code></pre>
 <p>Subsetting of <code>MatchedSpectra</code> is always relative to the
-query, i.e. subsetting an object with an index <code>4</code> would
-restrict the object to only the matching results for the 4th query
-spectrum.</p>
-<p>We now extract the matching information after subsetting:</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+query, i.e. <code>mtch[4]</code> would subset the object to the matching
+results for the 4th query spectrum.</p>
+<p>We now extract the matching information on the data subset:</p>
+<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraData</a></span><span class="op">(</span><span class="va">mtch</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"rtime"</span>, <span class="st">"precursorMz"</span>, <span class="st">"target_precursorMz"</span>, <span class="st">"score"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## DataFrame with 78 rows and 4 columns</span></span>
+<pre><code><span><span class="co">## DataFrame with 113 rows and 4 columns</span></span>
 <span><span class="co">##         rtime precursorMz target_precursorMz     score</span></span>
 <span><span class="co">##     &lt;numeric&gt;   &lt;numeric&gt;          &lt;numeric&gt; &lt;numeric&gt;</span></span>
-<span><span class="co">## 1     173.184     305.156            305.157  0.926425</span></span>
-<span><span class="co">## 2     173.184     305.156            305.157  0.926425</span></span>
-<span><span class="co">## 3     173.184     305.156            305.157  0.926425</span></span>
-<span><span class="co">## 4     173.184     305.156            305.157  0.926425</span></span>
-<span><span class="co">## 5     173.184     305.156            305.157  0.926425</span></span>
+<span><span class="co">## 1     326.703     235.143            235.144  0.947192</span></span>
+<span><span class="co">## 2     326.703     235.143            235.144  0.925879</span></span>
+<span><span class="co">## 3     326.703     235.143            235.144  0.818854</span></span>
+<span><span class="co">## 4     327.113     235.144            235.144  0.974576</span></span>
+<span><span class="co">## 5     327.113     235.144            235.144  0.857344</span></span>
 <span><span class="co">## ...       ...         ...                ...       ...</span></span>
-<span><span class="co">## 74    527.419     279.156            279.159  0.814331</span></span>
-<span><span class="co">## 75    527.419     279.156            279.159  0.822396</span></span>
-<span><span class="co">## 76    570.096     373.039            373.041  0.934031</span></span>
-<span><span class="co">## 77    570.506     373.042            373.041  0.888829</span></span>
-<span><span class="co">## 78    570.825     373.040            373.041  0.827892</span></span></code></pre>
-<p>We can also return the compound names for the matching spectra.</p>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/pander/man/pandoc.table.return.html" class="external-link">pandoc.table</a></span><span class="op">(</span>style <span class="op">=</span> <span class="st">"rmarkdown"</span>,</span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraData</a></span><span class="op">(</span><span class="va">mtch</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"rtime"</span>, <span class="st">"target_compound_name"</span>,</span>
-<span>                                      <span class="st">"score"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span><span class="co">## 109   565.646     425.214            425.215  0.947080</span></span>
+<span><span class="co">## 110   565.646     425.214            425.215  0.853670</span></span>
+<span><span class="co">## 111   570.825     373.040            373.041  0.879824</span></span>
+<span><span class="co">## 112   596.584     313.039            313.039  0.818335</span></span>
+<span><span class="co">## 113   873.144     136.112            136.112  0.804731</span></span></code></pre>
+<p>We can also return the compound names for the matching spectra.
+Depending on whether the annotations are retrieved from the MassBank
+MySQL database or the version from <em>AnnotationHub</em> we need to use
+a different spectra variable name for the compound name.</p>
+<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#' Get the name of the column containing the compound name</span></span>
+<span><span class="va">name_col</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/sets.html" class="external-link">intersect</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraVariables</a></span><span class="op">(</span><span class="va">mtch</span><span class="op">)</span>,</span>
+<span>                      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"target_name"</span>, <span class="st">"target_compound_name"</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="fu"><a href="https://rdrr.io/pkg/pander/man/pandoc.table.return.html" class="external-link">pandoc.table</a></span><span class="op">(</span>style <span class="op">=</span> <span class="st">"rmarkdown"</span>,</span>
+<span>    <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraData</a></span><span class="op">(</span><span class="va">mtch</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"rtime"</span>, <span class="va">name_col</span>, <span class="st">"score"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
 <th align="center">rtime</th>
-<th align="center">target_compound_name</th>
+<th align="center">target_name</th>
 <th align="center">score</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="center">173.2</td>
-<td align="center">Hexaethylene glycol</td>
-<td align="center">0.9264</td>
-</tr>
-<tr class="even">
-<td align="center">173.2</td>
-<td align="center">Hexaethylene glycol</td>
-<td align="center">0.9264</td>
-</tr>
-<tr class="odd">
-<td align="center">173.2</td>
-<td align="center">Hexaethylene glycol</td>
-<td align="center">0.9264</td>
-</tr>
-<tr class="even">
-<td align="center">173.2</td>
-<td align="center">Hexaethylene glycol</td>
-<td align="center">0.9264</td>
-</tr>
-<tr class="odd">
-<td align="center">173.2</td>
-<td align="center">Hexaethylene glycol</td>
-<td align="center">0.9264</td>
-</tr>
-<tr class="even">
-<td align="center">173.2</td>
-<td align="center">Hexaethylene glycol</td>
-<td align="center">0.9264</td>
-</tr>
-<tr class="odd">
-<td align="center">266.4</td>
-<td align="center">Triacetin</td>
-<td align="center">0.8046</td>
+<td align="center">326.7</td>
+<td align="center">Lenacil</td>
+<td align="center">0.9472</td>
 </tr>
 <tr class="even">
-<td align="center">320</td>
-<td align="center">SPI_270.2429_14.9</td>
-<td align="center">0.9656</td>
+<td align="center">326.7</td>
+<td align="center">Lenacil</td>
+<td align="center">0.9259</td>
 </tr>
 <tr class="odd">
 <td align="center">326.7</td>
 <td align="center">Lenacil</td>
-<td align="center">0.8067</td>
+<td align="center">0.8189</td>
 </tr>
 <tr class="even">
-<td align="center">326.7</td>
+<td align="center">327.1</td>
 <td align="center">Lenacil</td>
-<td align="center">0.8225</td>
+<td align="center">0.9746</td>
 </tr>
 <tr class="odd">
-<td align="center">326.7</td>
+<td align="center">327.1</td>
 <td align="center">Lenacil</td>
-<td align="center">0.8205</td>
+<td align="center">0.8573</td>
 </tr>
 <tr class="even">
 <td align="center">338.5</td>
 <td align="center">Azaconazole</td>
-<td align="center">0.8943</td>
+<td align="center">0.9089</td>
 </tr>
 <tr class="odd">
-<td align="center">338.9</td>
+<td align="center">338.5</td>
 <td align="center">Azaconazole</td>
-<td align="center">0.8179</td>
+<td align="center">0.8028</td>
 </tr>
 <tr class="even">
 <td align="center">338.9</td>
 <td align="center">Azaconazole</td>
-<td align="center">0.905</td>
+<td align="center">0.9226</td>
 </tr>
 <tr class="odd">
 <td align="center">339.3</td>
 <td align="center">Azaconazole</td>
-<td align="center">0.9017</td>
+<td align="center">0.9168</td>
 </tr>
 <tr class="even">
 <td align="center">353.5</td>
 <td align="center">Fosthiazate</td>
-<td align="center">0.8962</td>
+<td align="center">0.9396</td>
 </tr>
 <tr class="odd">
-<td align="center">353.5</td>
-<td align="center">Fosthiazate</td>
-<td align="center">0.8689</td>
-</tr>
-<tr class="even">
 <td align="center">361.7</td>
 <td align="center">Azaconazole</td>
-<td align="center">0.9002</td>
+<td align="center">0.9157</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td align="center">362.3</td>
 <td align="center">Azaconazole</td>
-<td align="center">0.8949</td>
+<td align="center">0.9083</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td align="center">362.6</td>
 <td align="center">Azaconazole</td>
-<td align="center">0.8925</td>
+<td align="center">0.906</td>
+</tr>
+<tr class="even">
+<td align="center">377.7</td>
+<td align="center">Triphenylphosphine oxide</td>
+<td align="center">0.9148</td>
 </tr>
 <tr class="odd">
 <td align="center">377.7</td>
-<td align="center">triphenylphosphineoxide</td>
-<td align="center">0.9025</td>
+<td align="center">Triphenylphosphine oxide</td>
+<td align="center">0.8734</td>
 </tr>
 <tr class="even">
 <td align="center">377.7</td>
 <td align="center">triphenylphosphineoxide</td>
-<td align="center">0.8801</td>
+<td align="center">0.8809</td>
 </tr>
 <tr class="odd">
 <td align="center">377.7</td>
+<td align="center">triphenylphosphineoxide</td>
+<td align="center">0.9269</td>
+</tr>
+<tr class="even">
+<td align="center">377.7</td>
 <td align="center">Triphenylphosphine oxide</td>
-<td align="center">0.8563</td>
+<td align="center">0.8352</td>
+</tr>
+<tr class="odd">
+<td align="center">378.5</td>
+<td align="center">Dimethachlor</td>
+<td align="center">0.8459</td>
 </tr>
 <tr class="even">
-<td align="center">378.1</td>
-<td align="center">N,N-Dimethyldodecylamine</td>
-<td align="center">0.8195</td>
+<td align="center">378.5</td>
+<td align="center">Dimethachlor</td>
+<td align="center">0.8423</td>
 </tr>
 <tr class="odd">
 <td align="center">378.9</td>
 <td align="center">Dimethachlor</td>
-<td align="center">0.8086</td>
+<td align="center">0.8784</td>
 </tr>
 <tr class="even">
 <td align="center">378.9</td>
 <td align="center">Dimethachlor</td>
-<td align="center">0.8623</td>
+<td align="center">0.8729</td>
 </tr>
 <tr class="odd">
 <td align="center">378.9</td>
 <td align="center">Dimethachlor</td>
-<td align="center">0.8447</td>
+<td align="center">0.9108</td>
 </tr>
 <tr class="even">
 <td align="center">378.9</td>
 <td align="center">Dimethachlor</td>
-<td align="center">0.8627</td>
+<td align="center">0.8111</td>
 </tr>
 <tr class="odd">
-<td align="center">378.9</td>
-<td align="center">Dimethachlor</td>
-<td align="center">0.8391</td>
+<td align="center">379</td>
+<td align="center">Triphenylphosphine oxide</td>
+<td align="center">0.8393</td>
 </tr>
 <tr class="even">
 <td align="center">379</td>
 <td align="center">Triphenylphosphine oxide</td>
-<td align="center">0.8081</td>
+<td align="center">0.8555</td>
 </tr>
 <tr class="odd">
 <td align="center">379</td>
 <td align="center">triphenylphosphineoxide</td>
-<td align="center">0.8743</td>
+<td align="center">0.8498</td>
 </tr>
 <tr class="even">
 <td align="center">379</td>
 <td align="center">triphenylphosphineoxide</td>
-<td align="center">0.8491</td>
+<td align="center">0.8962</td>
 </tr>
 <tr class="odd">
 <td align="center">379</td>
 <td align="center">Triphenylphosphine oxide</td>
-<td align="center">0.8851</td>
+<td align="center">0.8577</td>
 </tr>
 <tr class="even">
 <td align="center">382</td>
 <td align="center">Dimethachlor</td>
-<td align="center">0.844</td>
+<td align="center">0.8823</td>
 </tr>
 <tr class="odd">
 <td align="center">382</td>
 <td align="center">Dimethachlor</td>
-<td align="center">0.8923</td>
+<td align="center">0.8785</td>
 </tr>
 <tr class="even">
 <td align="center">382</td>
 <td align="center">Dimethachlor</td>
-<td align="center">0.8596</td>
+<td align="center">0.9024</td>
 </tr>
 <tr class="odd">
 <td align="center">382</td>
 <td align="center">Dimethachlor</td>
-<td align="center">0.8922</td>
-</tr>
-<tr class="even">
-<td align="center">382</td>
-<td align="center">Dimethachlor</td>
-<td align="center">0.8547</td>
-</tr>
-<tr class="odd">
-<td align="center">384.5</td>
-<td align="center">Dimethachlor</td>
-<td align="center">0.8457</td>
+<td align="center">0.8017</td>
 </tr>
 <tr class="even">
 <td align="center">384.5</td>
 <td align="center">Dimethachlor</td>
-<td align="center">0.8966</td>
+<td align="center">0.8924</td>
 </tr>
 <tr class="odd">
 <td align="center">384.5</td>
 <td align="center">Dimethachlor</td>
-<td align="center">0.8625</td>
+<td align="center">0.8038</td>
 </tr>
 <tr class="even">
 <td align="center">384.5</td>
 <td align="center">Dimethachlor</td>
-<td align="center">0.8963</td>
+<td align="center">0.8879</td>
 </tr>
 <tr class="odd">
 <td align="center">384.5</td>
 <td align="center">Dimethachlor</td>
-<td align="center">0.8573</td>
+<td align="center">0.9096</td>
 </tr>
 <tr class="even">
 <td align="center">405.1</td>
 <td align="center">Cyproconazole</td>
-<td align="center">0.8145</td>
+<td align="center">0.8112</td>
 </tr>
 <tr class="odd">
 <td align="center">405.1</td>
 <td align="center">Cyproconazole</td>
-<td align="center">0.8357</td>
+<td align="center">0.8098</td>
 </tr>
 <tr class="even">
 <td align="center">405.1</td>
-<td align="center">Cyproconazole (CP)</td>
-<td align="center">0.8262</td>
+<td align="center">Cyproconazole</td>
+<td align="center">0.8109</td>
+</tr>
+<tr class="odd">
+<td align="center">414.6</td>
+<td align="center">Tris(1-chloro-2-propyl) phosphate</td>
+<td align="center">0.8293</td>
+</tr>
+<tr class="even">
+<td align="center">414.6</td>
+<td align="center">Tris(1-chloro-2-propyl) phosphate</td>
+<td align="center">0.8132</td>
 </tr>
 <tr class="odd">
 <td align="center">414.6</td>
 <td align="center">Tris(1-chloro-2-propyl)phosphate</td>
-<td align="center">0.8613</td>
+<td align="center">0.8901</td>
 </tr>
 <tr class="even">
-<td align="center">452.9</td>
-<td align="center">Flufenacet</td>
-<td align="center">0.8464</td>
+<td align="center">414.6</td>
+<td align="center">Tris(1-chloro-2-propyl)phosphate</td>
+<td align="center">0.8457</td>
 </tr>
 <tr class="odd">
-<td align="center">452.9</td>
-<td align="center">Flufenacet</td>
-<td align="center">0.9146</td>
+<td align="center">414.6</td>
+<td align="center">Tris(1-chloro-2-propyl)phosphate</td>
+<td align="center">0.9992</td>
 </tr>
 <tr class="even">
-<td align="center">452.9</td>
-<td align="center">Flufenacet</td>
-<td align="center">0.8042</td>
+<td align="center">419.8</td>
+<td align="center">Fenamiphos</td>
+<td align="center">0.8033</td>
 </tr>
 <tr class="odd">
-<td align="center">452.9</td>
-<td align="center">Flufenacet</td>
-<td align="center">0.9388</td>
+<td align="center">431.5</td>
+<td align="center">Tris(1-chloro-2-propyl) phosphate</td>
+<td align="center">0.8146</td>
 </tr>
 <tr class="even">
-<td align="center">452.9</td>
-<td align="center">Flufenacet</td>
-<td align="center">0.8483</td>
+<td align="center">431.5</td>
+<td align="center">Tris(1-chloro-2-propyl)phosphate</td>
+<td align="center">0.9011</td>
+</tr>
+<tr class="odd">
+<td align="center">435</td>
+<td align="center">Fluopicolide</td>
+<td align="center">0.8542</td>
+</tr>
+<tr class="even">
+<td align="center">435.4</td>
+<td align="center">Fluopicolide</td>
+<td align="center">0.8085</td>
 </tr>
 <tr class="odd">
 <td align="center">452.9</td>
 <td align="center">Flufenacet</td>
-<td align="center">0.8517</td>
+<td align="center">0.8079</td>
 </tr>
 <tr class="even">
 <td align="center">452.9</td>
 <td align="center">Flufenacet</td>
-<td align="center">0.865</td>
+<td align="center">0.8949</td>
 </tr>
 <tr class="odd">
 <td align="center">452.9</td>
 <td align="center">Flufenacet</td>
-<td align="center">0.863</td>
+<td align="center">0.8898</td>
 </tr>
 <tr class="even">
 <td align="center">452.9</td>
 <td align="center">Flufenacet</td>
-<td align="center">0.8011</td>
+<td align="center">0.8909</td>
 </tr>
 <tr class="odd">
-<td align="center">453.3</td>
+<td align="center">452.9</td>
 <td align="center">Flufenacet</td>
-<td align="center">0.9033</td>
+<td align="center">0.9051</td>
 </tr>
 <tr class="even">
 <td align="center">453.3</td>
 <td align="center">Flufenacet</td>
-<td align="center">0.9066</td>
+<td align="center">0.8634</td>
 </tr>
 <tr class="odd">
 <td align="center">453.3</td>
 <td align="center">Flufenacet</td>
-<td align="center">0.8807</td>
+<td align="center">0.9009</td>
 </tr>
 <tr class="even">
 <td align="center">453.3</td>
 <td align="center">Flufenacet</td>
-<td align="center">0.8828</td>
+<td align="center">0.9508</td>
 </tr>
 <tr class="odd">
 <td align="center">453.3</td>
 <td align="center">Flufenacet</td>
-<td align="center">0.9333</td>
+<td align="center">0.9487</td>
 </tr>
 <tr class="even">
 <td align="center">453.3</td>
 <td align="center">Flufenacet</td>
-<td align="center">0.9288</td>
+<td align="center">0.8492</td>
 </tr>
 <tr class="odd">
-<td align="center">453.3</td>
-<td align="center">Flufenacet</td>
-<td align="center">0.8397</td>
+<td align="center">455.3</td>
+<td align="center">Dimoxystrobin</td>
+<td align="center">0.8179</td>
+</tr>
+<tr class="even">
+<td align="center">490.7</td>
+<td align="center">Triisobutyl phosphate</td>
+<td align="center">0.8834</td>
+</tr>
+<tr class="odd">
+<td align="center">490.7</td>
+<td align="center">Triisobutyl phosphate</td>
+<td align="center">0.8854</td>
+</tr>
+<tr class="even">
+<td align="center">490.7</td>
+<td align="center">Triisobutyl phosphate</td>
+<td align="center">0.8253</td>
+</tr>
+<tr class="odd">
+<td align="center">490.7</td>
+<td align="center">Triisobutyl phosphate</td>
+<td align="center">0.8514</td>
+</tr>
+<tr class="even">
+<td align="center">490.7</td>
+<td align="center">Tributyl phosphate</td>
+<td align="center">0.9988</td>
+</tr>
+<tr class="odd">
+<td align="center">490.7</td>
+<td align="center">Tributyl phosphate</td>
+<td align="center">0.9891</td>
+</tr>
+<tr class="even">
+<td align="center">490.7</td>
+<td align="center">Tributyl phosphate</td>
+<td align="center">0.944</td>
+</tr>
+<tr class="odd">
+<td align="center">490.7</td>
+<td align="center">Tributyl phosphate</td>
+<td align="center">0.8639</td>
+</tr>
+<tr class="even">
+<td align="center">490.7</td>
+<td align="center">Triisobutyl phosphate</td>
+<td align="center">0.8746</td>
+</tr>
+<tr class="odd">
+<td align="center">490.7</td>
+<td align="center">Triisobutyl phosphate</td>
+<td align="center">0.9747</td>
 </tr>
 <tr class="even">
 <td align="center">490.7</td>
-<td align="center">Tri-isobutylphosphate</td>
-<td align="center">0.8907</td>
+<td align="center">Triisobutyl phosphate</td>
+<td align="center">0.9926</td>
 </tr>
 <tr class="odd">
 <td align="center">490.7</td>
-<td align="center">Tri-isobutyl phosphate</td>
-<td align="center">0.8871</td>
+<td align="center">Triisobutyl phosphate</td>
+<td align="center">0.961</td>
+</tr>
+<tr class="even">
+<td align="center">490.7</td>
+<td align="center">Triisobutyl phosphate</td>
+<td align="center">0.8165</td>
+</tr>
+<tr class="odd">
+<td align="center">506.7</td>
+<td align="center">Didecyl-dimethylammonium</td>
+<td align="center">0.9766</td>
+</tr>
+<tr class="even">
+<td align="center">506.7</td>
+<td align="center">Didecyl-dimethylammonium</td>
+<td align="center">0.9109</td>
+</tr>
+<tr class="odd">
+<td align="center">506.7</td>
+<td align="center">Didecyl-dimethylammonium</td>
+<td align="center">0.9941</td>
 </tr>
 <tr class="even">
 <td align="center">506.8</td>
 <td align="center">Diflufenican</td>
-<td align="center">0.848</td>
+<td align="center">0.8661</td>
 </tr>
 <tr class="odd">
 <td align="center">506.8</td>
 <td align="center">Diflufenican</td>
-<td align="center">0.8604</td>
+<td align="center">0.877</td>
 </tr>
 <tr class="even">
+<td align="center">506.8</td>
+<td align="center">Diflufenican</td>
+<td align="center">0.9272</td>
+</tr>
+<tr class="odd">
+<td align="center">506.8</td>
+<td align="center">Diflufenican</td>
+<td align="center">0.814</td>
+</tr>
+<tr class="even">
+<td align="center">506.8</td>
+<td align="center">Diflufenican</td>
+<td align="center">0.826</td>
+</tr>
+<tr class="odd">
 <td align="center">507.3</td>
 <td align="center">Diflufenican</td>
-<td align="center">0.8416</td>
+<td align="center">0.8385</td>
+</tr>
+<tr class="even">
+<td align="center">507.3</td>
+<td align="center">Diflufenican</td>
+<td align="center">0.843</td>
 </tr>
 <tr class="odd">
 <td align="center">507.3</td>
 <td align="center">Diflufenican</td>
-<td align="center">0.8524</td>
+<td align="center">0.9183</td>
+</tr>
+<tr class="even">
+<td align="center">507.3</td>
+<td align="center">Diflufenican</td>
+<td align="center">0.9113</td>
+</tr>
+<tr class="odd">
+<td align="center">507.7</td>
+<td align="center">Diflufenican</td>
+<td align="center">0.8424</td>
 </tr>
 <tr class="even">
 <td align="center">507.7</td>
 <td align="center">Diflufenican</td>
-<td align="center">0.8591</td>
+<td align="center">0.8732</td>
 </tr>
 <tr class="odd">
 <td align="center">507.7</td>
 <td align="center">Diflufenican</td>
-<td align="center">0.8707</td>
+<td align="center">0.9041</td>
 </tr>
 <tr class="even">
-<td align="center">527.4</td>
+<td align="center">507.7</td>
+<td align="center">Diflufenican</td>
+<td align="center">0.8026</td>
+</tr>
+<tr class="odd">
+<td align="center">511</td>
+<td align="center">Diflufenican</td>
+<td align="center">0.835</td>
+</tr>
+<tr class="even">
+<td align="center">512.1</td>
+<td align="center">Diflufenican</td>
+<td align="center">0.8813</td>
+</tr>
+<tr class="odd">
+<td align="center">512.1</td>
+<td align="center">Diflufenican</td>
+<td align="center">0.8439</td>
+</tr>
+<tr class="even">
+<td align="center">512.1</td>
+<td align="center">Diflufenican</td>
+<td align="center">0.8797</td>
+</tr>
+<tr class="odd">
+<td align="center">512.1</td>
+<td align="center">Diflufenican</td>
+<td align="center">0.8105</td>
+</tr>
+<tr class="even">
+<td align="center">512.1</td>
+<td align="center">Diflufenican</td>
+<td align="center">0.8389</td>
+</tr>
+<tr class="odd">
+<td align="center">526.4</td>
 <td align="center">Dibutyl phthalate</td>
-<td align="center">0.818</td>
+<td align="center">0.87</td>
+</tr>
+<tr class="even">
+<td align="center">526.4</td>
+<td align="center">Dibutyl phthalate</td>
+<td align="center">0.9468</td>
+</tr>
+<tr class="odd">
+<td align="center">526.4</td>
+<td align="center">Diisobutyl phthalate</td>
+<td align="center">0.8584</td>
+</tr>
+<tr class="even">
+<td align="center">526.4</td>
+<td align="center">Diisobutyl phthalate</td>
+<td align="center">0.9351</td>
 </tr>
 <tr class="odd">
+<td align="center">526.4</td>
+<td align="center">Diisobutyl phthalate</td>
+<td align="center">0.8683</td>
+</tr>
+<tr class="even">
 <td align="center">527.4</td>
 <td align="center">Dibutyl phthalate</td>
-<td align="center">0.8847</td>
+<td align="center">0.8506</td>
+</tr>
+<tr class="odd">
+<td align="center">527.4</td>
+<td align="center">Diisobutyl phthalate</td>
+<td align="center">0.8409</td>
 </tr>
 <tr class="even">
 <td align="center">527.4</td>
-<td align="center">Di-n-butyl phthalate</td>
-<td align="center">0.8143</td>
+<td align="center">Dibutyl phthalate</td>
+<td align="center">0.818</td>
 </tr>
 <tr class="odd">
-<td align="center">527.4</td>
-<td align="center">Di-n-butyl phthalate</td>
-<td align="center">0.8224</td>
+<td align="center">563.6</td>
+<td align="center">Tributyl acetylcitrate</td>
+<td align="center">0.8066</td>
 </tr>
 <tr class="even">
-<td align="center">570.1</td>
-<td align="center">Proquinazid</td>
-<td align="center">0.934</td>
+<td align="center">564.6</td>
+<td align="center">Tributyl acetylcitrate</td>
+<td align="center">0.9893</td>
 </tr>
 <tr class="odd">
-<td align="center">570.5</td>
-<td align="center">Proquinazid</td>
-<td align="center">0.8888</td>
+<td align="center">564.6</td>
+<td align="center">Tributyl acetylcitrate</td>
+<td align="center">0.9963</td>
+</tr>
+<tr class="even">
+<td align="center">565.6</td>
+<td align="center">Tributyl acetylcitrate</td>
+<td align="center">0.9506</td>
+</tr>
+<tr class="odd">
+<td align="center">565.6</td>
+<td align="center">Tributyl acetylcitrate</td>
+<td align="center">0.9471</td>
 </tr>
 <tr class="even">
+<td align="center">565.6</td>
+<td align="center">Tributyl acetylcitrate</td>
+<td align="center">0.8537</td>
+</tr>
+<tr class="odd">
 <td align="center">570.8</td>
 <td align="center">Proquinazid</td>
-<td align="center">0.8279</td>
+<td align="center">0.8798</td>
+</tr>
+<tr class="even">
+<td align="center">596.6</td>
+<td align="center">Spirodiclofen-enol</td>
+<td align="center">0.8183</td>
+</tr>
+<tr class="odd">
+<td align="center">873.1</td>
+<td align="center">Trimethylphenylammonium</td>
+<td align="center">0.8047</td>
 </tr>
 </tbody>
 </table>
-<p>The <code>matchSpectra</code> enables thus to perform convenient
-spectra matching between MS data represented as <code>Spectra</code>
-objects. As a result, a <code>MatchedSpectra</code> object is returned
-that, in addition to the matching results, contains also the query and
-target spectra. Pre-filtering the spectra prior to the actual spectra
-similarity calculation can reduce the running time of a
-<code>matchSpectra</code> call but might also miss some potential
-matches. Note that in addition to the precursor m/z-based pre-filter
-also retention time pre-filtering would be available (see
+<p>We can also visually inspect the matches using mirror plots. Below we
+create mirror plots between the first query spectrum with all matching
+reference spectra. For better visualization we scale the peak
+intensities of all spectra to a total intensity sum of one by setting
+<code>scalePeaks = TRUE</code>.</p>
+<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#' plot the results for the first query spectrum</span></span>
+<span><span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/spectra-plotting.html" class="external-link">plotSpectraMirror</a></span><span class="op">(</span><span class="va">mtch</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>, ppm <span class="op">=</span> <span class="fl">10</span>, scalePeaks <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<p><img src="Spectra-matching-with-MetaboAnnotation_files/figure-html/unnamed-chunk-16-1.png" width="700"></p>
+<p>Spectra matching results could also be manually, and interactively,
+evaluated and validated with the <code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/validateMatchedSpectra.html" class="external-link">validateMatchedSpectra()</a></code>
+function.</p>
+<p>Summarizing, the <code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/matchSpectra.html" class="external-link">matchSpectra()</a></code> function enables thus a
+convenient spectra matching between MS data represented as
+<code>Spectra</code> objects. As a result, a <code>MatchedSpectra</code>
+object is returned that, in addition to the matching results, contains
+also the query and target spectra. Pre-filtering the spectra prior to
+the actual spectra similarity calculation can reduce the running time of
+a <code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/matchSpectra.html" class="external-link">matchSpectra()</a></code> call but might also miss some potential
+matches. Note that in addition to the precursor <em>m/z</em>-based
+pre-filter also retention time pre-filtering would be available (see
 <code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/matchSpectra.html" class="external-link">?matchSpectra</a></code> for more information). Also, a more advanced
 matching approach would be available with the
 <code>MatchForwardReverseParam</code> that calculates in addition to the
@@ -767,23 +998,24 @@ <h3 id="ms2-spectra-matching-in-an-xcms-workflow">MS2 spectra matching in an <co
 performed in MS1 while the MS2 data is used for identification of the
 features. Quantification of MS1 data requires a chromatographic peak
 detection step which can be performed using the functionality from the
-<em><a href="https://bioconductor.org/packages/3.18/xcms" class="external-link">xcms</a></em>
+<em><a href="https://bioconductor.org/packages/3.19/xcms" class="external-link">xcms</a></em>
 package. Below we load thus the <code>xcms</code> package and import the
-full MS data using the <code>readMSData</code> function.</p>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+full MS data using the <code>readMsExperiment</code> function.</p>
+<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/sneumann/xcms" class="external-link">xcms</a></span><span class="op">)</span></span>
-<span><span class="va">pest_all</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://lgatto.github.io/MSnbase/reference/readMSData.html" class="external-link">readMSData</a></span><span class="op">(</span><span class="va">fl</span>, mode <span class="op">=</span> <span class="st">"onDisk"</span><span class="op">)</span></span></code></pre></div>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/RforMassSpectrometry/MsExperiment" class="external-link">MsExperiment</a></span><span class="op">)</span></span>
+<span><span class="va">pest_all</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/readMsExperiment.html" class="external-link">readMsExperiment</a></span><span class="op">(</span><span class="va">fl</span><span class="op">)</span></span></code></pre></div>
 <p>We next perform the chromatographic peak detection using the
 <em>centWave</em> algorithm (see the <em>LC-MS/MS data analysis with
 xcms</em> vignette from the <code>xcms</code> package for details on the
 chromatographic peak detection settings).</p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">cwp</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/findChromPeaks-centWave.html" class="external-link">CentWaveParam</a></span><span class="op">(</span>snthresh <span class="op">=</span> <span class="fl">5</span>, noise <span class="op">=</span> <span class="fl">100</span>, ppm <span class="op">=</span> <span class="fl">10</span>,</span>
 <span>                     peakwidth <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">30</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">pest_all</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/findChromPeaks.html" class="external-link">findChromPeaks</a></span><span class="op">(</span><span class="va">pest_all</span>, param <span class="op">=</span> <span class="va">cwp</span><span class="op">)</span></span></code></pre></div>
 <p>In total 99 chromatographic peaks have been identified. Below we
 display the first 6 of them.</p>
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">pest_all</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##            mz    mzmin    mzmax      rt   rtmin   rtmax       into      intb</span></span>
 <span><span class="co">## CP01 142.9926 142.9921 142.9931 130.615 125.856 134.241  1113.8028  1106.229</span></span>
@@ -800,37 +1032,40 @@ <h3 id="ms2-spectra-matching-in-an-xcms-workflow">MS2 spectra matching in an <co
 <span><span class="co">## CP05  784.9196 114      1</span></span>
 <span><span class="co">## CP06 1006.9720 110      1</span></span></code></pre>
 <p>We can now extract all MS2 spectra for each chromatographic peak with
-the <code>chromPeakSpectra</code> function. This function identifies all
-MS2 spectra recorded by the instrument with a retention time within the
-retention time and with a precursor m/z within the m/z boundaries of the
-chromatographic peak. By setting <code>return.type = "Spectra"</code> we
-ensure that the data is being returned in the newer <code>Spectra</code>
-format hence enabling the simplified spectra matching with the
-functionality presented here.</p>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+the <code><a href="https://rdrr.io/pkg/xcms/man/chromPeakSpectra.html" class="external-link">chromPeakSpectra()</a></code> function. This function identifies
+all MS2 spectra recorded by the instrument with a retention time within
+the retention time and with a precursor m/z within the <em>m/z</em>
+boundaries of the chromatographic peak. By setting
+<code>return.type = "Spectra"</code> we ensure that the data is being
+returned in the newer <code>Spectra</code> format hence enabling the
+simplified spectra matching with the functionality presented here.</p>
+<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pest_ms2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/chromPeakSpectra.html" class="external-link">chromPeakSpectra</a></span><span class="op">(</span><span class="va">pest_all</span>, return.type <span class="op">=</span> <span class="st">"Spectra"</span><span class="op">)</span></span>
 <span><span class="va">pest_ms2</span></span></code></pre></div>
 <pre><code><span><span class="co">## MSn data (Spectra) with 158 spectra in a MsBackendMzR backend:</span></span>
-<span><span class="co">##            msLevel     rtime scanIndex</span></span>
-<span><span class="co">##          &lt;integer&gt; &lt;numeric&gt; &lt;integer&gt;</span></span>
-<span><span class="co">## F1.S1000         2   128.237      1000</span></span>
-<span><span class="co">## F1.S1008         2   128.737      1008</span></span>
-<span><span class="co">## F1.S1023         2   129.857      1023</span></span>
-<span><span class="co">## F1.S1812         2   237.869      1812</span></span>
-<span><span class="co">## F1.S1846         2   241.299      1846</span></span>
-<span><span class="co">## ...            ...       ...       ...</span></span>
-<span><span class="co">## F1.S5115         2   575.255      5115</span></span>
-<span><span class="co">## F1.S5272         2   596.584      5272</span></span>
-<span><span class="co">## F1.S5236         2   592.424      5236</span></span>
-<span><span class="co">## F1.S5266         2   596.054      5266</span></span>
-<span><span class="co">## F1.S7344         2   873.714      7344</span></span>
-<span><span class="co">##  ... 38 more variables/columns.</span></span>
+<span><span class="co">##       msLevel     rtime scanIndex</span></span>
+<span><span class="co">##     &lt;integer&gt; &lt;numeric&gt; &lt;integer&gt;</span></span>
+<span><span class="co">## 1           2   128.237      1000</span></span>
+<span><span class="co">## 2           2   128.737      1008</span></span>
+<span><span class="co">## 3           2   129.857      1023</span></span>
+<span><span class="co">## 4           2   237.869      1812</span></span>
+<span><span class="co">## 5           2   241.299      1846</span></span>
+<span><span class="co">## ...       ...       ...       ...</span></span>
+<span><span class="co">## 154         2   575.255      5115</span></span>
+<span><span class="co">## 155         2   596.584      5272</span></span>
+<span><span class="co">## 156         2   592.424      5236</span></span>
+<span><span class="co">## 157         2   596.054      5266</span></span>
+<span><span class="co">## 158         2   873.714      7344</span></span>
+<span><span class="co">##  ... 34 more variables/columns.</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## file(s):</span></span>
-<span><span class="co">## PestMix1_DDA.mzML</span></span></code></pre>
+<span><span class="co">## PestMix1_DDA.mzML</span></span>
+<span><span class="co">## Processing:</span></span>
+<span><span class="co">##  Filter: select MS level(s) 2 [Fri May 17 14:12:02 2024]</span></span>
+<span><span class="co">##  Merge 1 Spectra into one [Fri May 17 14:12:02 2024]</span></span></code></pre>
 <p>Spectra variable <code>peak_id</code> contains the identified of the
 chromatographic peak (i.e. its row name in <code>chromPeaks</code>).</p>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pest_ms2</span><span class="op">$</span><span class="va">peak_id</span></span></code></pre></div>
 <pre><code><span><span class="co">##   [1] "CP01" "CP01" "CP01" "CP04" "CP04" "CP05" "CP05" "CP06" "CP06" "CP08"</span></span>
 <span><span class="co">##  [11] "CP08" "CP11" "CP11" "CP12" "CP12" "CP13" "CP13" "CP13" "CP13" "CP14"</span></span>
@@ -851,253 +1086,227 @@ <h3 id="ms2-spectra-matching-in-an-xcms-workflow">MS2 spectra matching in an <co
 <p>We next, like in the previous section, <em>clean up</em> the spectra
 removing peaks with an intensity below 5% of the largest peak intensity
 per spectrum and removing spectra with a single peak.</p>
-<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Remove peaks with an intensity below 5%</span></span>
 <span><span class="va">pest_ms2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">filterIntensity</a></span><span class="op">(</span><span class="va">pest_ms2</span>, intensity <span class="op">=</span> <span class="va">low_int</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co">#' Remove peaks with a single peak</span></span>
 <span><span class="va">pest_ms2</span> <span class="op">&lt;-</span> <span class="va">pest_ms2</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/lengths.html" class="external-link">lengths</a></span><span class="op">(</span><span class="va">pest_ms2</span><span class="op">)</span> <span class="op">&gt;</span> <span class="fl">1</span><span class="op">]</span></span></code></pre></div>
-<p>In addition we scale also the intensities within each MS2 spectrum by
-replacing them with intensities relative to the maximum peak intensity
-(see <a href="https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html#data-processing-and-manipulation-1">here</a>
-for more information). In addition to the query spectra, we also
-<em>normalize</em> the MassBank spectra in the same way.</p>
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co">#' Define a function to *normalize* the intensities</span></span>
-<span><span class="va">norm_int</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="va">maxint</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">x</span><span class="op">[</span>, <span class="st">"intensity"</span><span class="op">]</span>, na.rm <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span>    <span class="va">x</span><span class="op">[</span>, <span class="st">"intensity"</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fl">100</span> <span class="op">*</span> <span class="va">x</span><span class="op">[</span>, <span class="st">"intensity"</span><span class="op">]</span> <span class="op">/</span> <span class="va">maxint</span></span>
-<span>    <span class="va">x</span></span>
-<span><span class="op">}</span></span>
-<span><span class="co">#' *Apply* the function to the data</span></span>
-<span><span class="va">pest_ms2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">addProcessing</a></span><span class="op">(</span><span class="va">pest_ms2</span>, <span class="va">norm_int</span><span class="op">)</span></span>
-<span><span class="va">mbank</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">addProcessing</a></span><span class="op">(</span><span class="va">mbank</span>, <span class="va">norm_int</span><span class="op">)</span></span></code></pre></div>
-<p>Next we perform now the spectra matching with the same parameters as
-in the previous section.</p>
-<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
+<p>We in addition also scale the peak intensities per spectrum using the
+<code><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">scalePeaks()</a></code> function. While most spectra similarity
+scoring algorithms are independent of absolute peak intensities, peak
+scaling will improve the graphical visualization of results. We perform
+the peak scaling to both the experimental as well as the reference
+spectra in MassBank.</p>
+<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">pest_ms2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">scalePeaks</a></span><span class="op">(</span><span class="va">pest_ms2</span><span class="op">)</span></span>
+<span><span class="va">mbank</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">scalePeaks</a></span><span class="op">(</span><span class="va">mbank</span><span class="op">)</span></span></code></pre></div>
+<p>Next we perform the spectra matching with the same parameters as in
+the previous section.</p>
+<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pest_match</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/MetaboAnnotation/man/matchSpectra.html" class="external-link">matchSpectra</a></span><span class="op">(</span><span class="va">pest_ms2</span>, <span class="va">mbank</span>, param <span class="op">=</span> <span class="va">prm</span><span class="op">)</span></span>
 <span><span class="va">pest_match</span></span></code></pre></div>
 <pre><code><span><span class="co">## Object of class MatchedSpectra </span></span>
-<span><span class="co">## Total number of matches: 31 </span></span>
-<span><span class="co">## Number of query objects: 155 (7 matched)</span></span>
-<span><span class="co">## Number of target objects: 86576 (19 matched)</span></span></code></pre>
+<span><span class="co">## Total number of matches: 33 </span></span>
+<span><span class="co">## Number of query objects: 155 (12 matched)</span></span>
+<span><span class="co">## Number of target objects: 103525 (26 matched)</span></span></code></pre>
 <p>Again, we restrict the <code>MatchedSpectra</code> to query spectra
 which could be matched.</p>
-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pest_match</span> <span class="op">&lt;-</span> <span class="va">pest_match</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">whichQuery</a></span><span class="op">(</span><span class="va">pest_match</span><span class="op">)</span><span class="op">]</span></span></code></pre></div>
 <p>The table below lists the compound names of matching spectra for the
 chromatographic peaks.</p>
-<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/pander/man/pandoc.table.return.html" class="external-link">pandoc.table</a></span><span class="op">(</span></span>
+<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#' Get the name of the column containing the compound name</span></span>
+<span><span class="va">name_col</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/sets.html" class="external-link">intersect</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraVariables</a></span><span class="op">(</span><span class="va">pest_match</span><span class="op">)</span>,</span>
+<span>                      <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"target_name"</span>, <span class="st">"target_compound_name"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/pkg/pander/man/pandoc.table.return.html" class="external-link">pandoc.table</a></span><span class="op">(</span></span>
 <span>    style <span class="op">=</span> <span class="st">"rmarkdown"</span>,</span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraData</a></span><span class="op">(</span><span class="va">pest_match</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"peak_id"</span>, <span class="st">"rtime"</span>,</span>
-<span>                                            <span class="st">"target_compound_name"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span>    <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraData</a></span><span class="op">(</span><span class="va">pest_match</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"peak_id"</span>, <span class="st">"rtime"</span>, <span class="va">name_col</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
-<th align="center"> </th>
 <th align="center">peak_id</th>
 <th align="center">rtime</th>
-<th align="center">target_compound_name</th>
+<th align="center">target_name</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="center"><strong>F1.S2860</strong></td>
-<td align="center">CP18</td>
-<td align="center">362.6</td>
-<td align="center">Azaconazole</td>
+<td align="center">CP06</td>
+<td align="center">327.1</td>
+<td align="center">Lenacil</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3061</strong></td>
-<td align="center">CP25</td>
-<td align="center">382</td>
-<td align="center">Dimethachlor</td>
+<td align="center">CP06</td>
+<td align="center">327.1</td>
+<td align="center">Lenacil</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3061.1</strong></td>
-<td align="center">CP25</td>
-<td align="center">382</td>
-<td align="center">Dimethachlor</td>
+<td align="center">CP18</td>
+<td align="center">362.6</td>
+<td align="center">Azaconazole</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3061.2</strong></td>
 <td align="center">CP25</td>
 <td align="center">382</td>
 <td align="center">Dimethachlor</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3061.3</strong></td>
 <td align="center">CP25</td>
 <td align="center">382</td>
 <td align="center">Dimethachlor</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3061.4</strong></td>
 <td align="center">CP25</td>
 <td align="center">382</td>
 <td align="center">Dimethachlor</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3080</strong></td>
 <td align="center">CP25</td>
-<td align="center">384.5</td>
+<td align="center">382</td>
 <td align="center">Dimethachlor</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3080.1</strong></td>
 <td align="center">CP25</td>
 <td align="center">384.5</td>
 <td align="center">Dimethachlor</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3080.2</strong></td>
 <td align="center">CP25</td>
 <td align="center">384.5</td>
 <td align="center">Dimethachlor</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3080.3</strong></td>
 <td align="center">CP25</td>
 <td align="center">384.5</td>
 <td align="center">Dimethachlor</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3080.4</strong></td>
 <td align="center">CP25</td>
 <td align="center">384.5</td>
 <td align="center">Dimethachlor</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3314</strong></td>
 <td align="center">CP42</td>
 <td align="center">405.1</td>
 <td align="center">Cyproconazole</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3314.1</strong></td>
 <td align="center">CP42</td>
 <td align="center">405.1</td>
 <td align="center">Cyproconazole</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3314.2</strong></td>
 <td align="center">CP42</td>
 <td align="center">405.1</td>
-<td align="center">Cyproconazole (CP)</td>
+<td align="center">Cyproconazole</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3968</strong></td>
-<td align="center">CP67</td>
-<td align="center">452.9</td>
-<td align="center">Flufenacet</td>
+<td align="center">CP57</td>
+<td align="center">435.4</td>
+<td align="center">Fluopicolide</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3968.1</strong></td>
 <td align="center">CP67</td>
 <td align="center">452.9</td>
 <td align="center">Flufenacet</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3968.2</strong></td>
 <td align="center">CP67</td>
 <td align="center">452.9</td>
 <td align="center">Flufenacet</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3968.3</strong></td>
 <td align="center">CP67</td>
 <td align="center">452.9</td>
 <td align="center">Flufenacet</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3968.4</strong></td>
 <td align="center">CP67</td>
 <td align="center">452.9</td>
 <td align="center">Flufenacet</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3968.5</strong></td>
 <td align="center">CP67</td>
 <td align="center">452.9</td>
 <td align="center">Flufenacet</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3968.6</strong></td>
 <td align="center">CP67</td>
-<td align="center">452.9</td>
+<td align="center">453.3</td>
 <td align="center">Flufenacet</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3968.7</strong></td>
 <td align="center">CP67</td>
-<td align="center">452.9</td>
+<td align="center">453.3</td>
 <td align="center">Flufenacet</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3968.8</strong></td>
 <td align="center">CP67</td>
-<td align="center">452.9</td>
+<td align="center">453.3</td>
 <td align="center">Flufenacet</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3972</strong></td>
 <td align="center">CP67</td>
 <td align="center">453.3</td>
 <td align="center">Flufenacet</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3972.1</strong></td>
 <td align="center">CP67</td>
 <td align="center">453.3</td>
 <td align="center">Flufenacet</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3972.2</strong></td>
-<td align="center">CP67</td>
-<td align="center">453.3</td>
-<td align="center">Flufenacet</td>
+<td align="center">CP72</td>
+<td align="center">455.3</td>
+<td align="center">Dimoxystrobin</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3972.3</strong></td>
-<td align="center">CP67</td>
-<td align="center">453.3</td>
-<td align="center">Flufenacet</td>
+<td align="center">CP88</td>
+<td align="center">511</td>
+<td align="center">Diflufenican</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3972.4</strong></td>
-<td align="center">CP67</td>
-<td align="center">453.3</td>
-<td align="center">Flufenacet</td>
+<td align="center">CP88</td>
+<td align="center">512.1</td>
+<td align="center">Diflufenican</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S3972.5</strong></td>
-<td align="center">CP67</td>
-<td align="center">453.3</td>
-<td align="center">Flufenacet</td>
+<td align="center">CP88</td>
+<td align="center">512.1</td>
+<td align="center">Diflufenican</td>
 </tr>
 <tr class="even">
-<td align="center"><strong>F1.S3972.6</strong></td>
-<td align="center">CP67</td>
-<td align="center">453.3</td>
-<td align="center">Flufenacet</td>
+<td align="center">CP88</td>
+<td align="center">512.1</td>
+<td align="center">Diflufenican</td>
 </tr>
 <tr class="odd">
-<td align="center"><strong>F1.S5077</strong></td>
-<td align="center">CP94</td>
-<td align="center">570.5</td>
-<td align="center">Proquinazid</td>
+<td align="center">CP88</td>
+<td align="center">512.1</td>
+<td align="center">Diflufenican</td>
+</tr>
+<tr class="even">
+<td align="center">CP88</td>
+<td align="center">512.1</td>
+<td align="center">Diflufenican</td>
+</tr>
+<tr class="odd">
+<td align="center">CP95</td>
+<td align="center">596.6</td>
+<td align="center">Spirodiclofen-enol</td>
 </tr>
 </tbody>
 </table>
 <p>We can also directly plot matching (query and target) spectra against
-each other using the <code>plotSpectraMirror</code> function subsetting
-the <code>MatchedSpectra</code> object to the query spectrum of
-interest. Below we plot the third query spectrum against all of its
+each other using the <code><a href="https://rdrr.io/pkg/Spectra/man/spectra-plotting.html" class="external-link">plotSpectraMirror()</a></code> function
+subsetting the <code>MatchedSpectra</code> object to the query spectrum
+of interest. Below we plot the third query spectrum against all of its
 matching target spectra.</p>
-<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/spectra-plotting.html" class="external-link">plotSpectraMirror</a></span><span class="op">(</span><span class="va">pest_match</span><span class="op">[</span><span class="fl">3</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<p><img src="Spectra-matching-with-MetaboAnnotation_files/figure-html/unnamed-chunk-22-1.png" width="700"></p>
-<p>Summarizing, with the <code>chromPeakSpectra</code> and the
-<code>featureSpectra</code> functions, <code>xcms</code> allows to
+<p><img src="Spectra-matching-with-MetaboAnnotation_files/figure-html/unnamed-chunk-26-1.png" width="700"></p>
+<p>Summarizing, with the <code><a href="https://rdrr.io/pkg/xcms/man/chromPeakSpectra.html" class="external-link">chromPeakSpectra()</a></code> and the
+<code><a href="https://rdrr.io/pkg/xcms/man/featureSpectra.html" class="external-link">featureSpectra()</a></code> functions, <code>xcms</code> allows to
 return MS data as <code>Spectra</code> objects which enables, as shown
 in this simple example, to perform MS2 spectra matching using the
 <code>Spectra</code> as well as the <code>MetaboAnnotation</code>
@@ -1108,11 +1317,11 @@ <h3 id="ms2-spectra-matching-in-an-xcms-workflow">MS2 spectra matching in an <co
 <div class="section level2">
 <h2 id="session-information">Session information<a class="anchor" aria-label="anchor" href="#session-information"></a>
 </h2>
-<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html" class="external-link">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## R version 4.3.2 (2023-10-31)</span></span>
-<span><span class="co">## Platform: x86_64-pc-linux-gnu (64-bit)</span></span>
-<span><span class="co">## Running under: Ubuntu 22.04.3 LTS</span></span>
+<pre><code><span><span class="co">## R version 4.4.0 (2024-04-24)</span></span>
+<span><span class="co">## Platform: x86_64-pc-linux-gnu</span></span>
+<span><span class="co">## Running under: Ubuntu 22.04.4 LTS</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Matrix products: default</span></span>
 <span><span class="co">## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 </span></span>
@@ -1134,90 +1343,86 @@ <h2 id="session-information">Session information<a class="anchor" aria-label="an
 <span><span class="co">## [8] base     </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## other attached packages:</span></span>
-<span><span class="co">##  [1] xcms_4.0.0               MSnbase_2.28.1           mzR_2.36.0              </span></span>
-<span><span class="co">##  [4] Rcpp_1.0.11              Biobase_2.62.0           MetaboAnnotation_1.6.1  </span></span>
-<span><span class="co">##  [7] MsBackendMassbank_1.11.1 RMariaDB_1.3.1           pander_0.6.5            </span></span>
-<span><span class="co">## [10] Spectra_1.12.0           ProtGenerics_1.34.0      BiocParallel_1.36.0     </span></span>
-<span><span class="co">## [13] S4Vectors_0.40.2         BiocGenerics_0.48.1      BiocStyle_2.30.0        </span></span>
+<span><span class="co">##  [1] MsExperiment_1.6.0       xcms_4.2.1               MetaboAnnotation_1.8.1  </span></span>
+<span><span class="co">##  [4] CompoundDb_1.8.0         AnnotationFilter_1.28.0  AnnotationHub_3.12.0    </span></span>
+<span><span class="co">##  [7] BiocFileCache_2.12.0     dbplyr_2.5.0             MsBackendMassbank_1.12.0</span></span>
+<span><span class="co">## [10] RMariaDB_1.3.1           pander_0.6.5             Spectra_1.14.0          </span></span>
+<span><span class="co">## [13] ProtGenerics_1.36.0      S4Vectors_0.42.0         BiocGenerics_0.50.0     </span></span>
+<span><span class="co">## [16] BiocParallel_1.38.0      BiocStyle_2.32.0        </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## loaded via a namespace (and not attached):</span></span>
-<span><span class="co">##   [1] splines_4.3.2                 later_1.3.1                  </span></span>
-<span><span class="co">##   [3] bitops_1.0-7                  filelock_1.0.2               </span></span>
-<span><span class="co">##   [5] tibble_3.2.1                  preprocessCore_1.64.0        </span></span>
-<span><span class="co">##   [7] XML_3.99-0.16                 lifecycle_1.0.4              </span></span>
-<span><span class="co">##   [9] doParallel_1.0.17             rprojroot_2.0.4              </span></span>
-<span><span class="co">##  [11] lattice_0.22-5                MASS_7.3-60                  </span></span>
-<span><span class="co">##  [13] MultiAssayExperiment_1.28.0   magrittr_2.0.3               </span></span>
-<span><span class="co">##  [15] limma_3.58.1                  sass_0.4.7                   </span></span>
-<span><span class="co">##  [17] rmarkdown_2.25                jquerylib_0.1.4              </span></span>
-<span><span class="co">##  [19] yaml_2.3.7                    httpuv_1.6.12                </span></span>
-<span><span class="co">##  [21] MsCoreUtils_1.14.1            DBI_1.1.3                    </span></span>
-<span><span class="co">##  [23] RColorBrewer_1.1-3            lubridate_1.9.3              </span></span>
-<span><span class="co">##  [25] abind_1.4-5                   zlibbioc_1.48.0              </span></span>
-<span><span class="co">##  [27] GenomicRanges_1.54.1          purrr_1.0.2                  </span></span>
-<span><span class="co">##  [29] AnnotationFilter_1.26.0       RCurl_1.98-1.13              </span></span>
-<span><span class="co">##  [31] rappdirs_0.3.3                GenomeInfoDbData_1.2.11      </span></span>
-<span><span class="co">##  [33] IRanges_2.36.0                pkgdown_2.0.7                </span></span>
-<span><span class="co">##  [35] ncdf4_1.22                    codetools_0.2-19             </span></span>
-<span><span class="co">##  [37] CompoundDb_1.6.0              DelayedArray_0.28.0          </span></span>
-<span><span class="co">##  [39] DT_0.30                       xml2_1.3.6                   </span></span>
-<span><span class="co">##  [41] tidyselect_1.2.0              matrixStats_1.1.0            </span></span>
-<span><span class="co">##  [43] BiocFileCache_2.10.1          base64enc_0.1-3              </span></span>
-<span><span class="co">##  [45] jsonlite_1.8.8                multtest_2.58.0              </span></span>
-<span><span class="co">##  [47] ellipsis_0.3.2                survival_3.5-7               </span></span>
-<span><span class="co">##  [49] iterators_1.0.14              systemfonts_1.0.5            </span></span>
-<span><span class="co">##  [51] foreach_1.5.2                 tools_4.3.2                  </span></span>
-<span><span class="co">##  [53] progress_1.2.2                ragg_1.2.6                   </span></span>
-<span><span class="co">##  [55] glue_1.6.2                    gridExtra_2.3                </span></span>
-<span><span class="co">##  [57] SparseArray_1.2.2             xfun_0.41                    </span></span>
-<span><span class="co">##  [59] MatrixGenerics_1.14.0         GenomeInfoDb_1.38.1          </span></span>
-<span><span class="co">##  [61] dplyr_1.1.4                   BiocManager_1.30.22          </span></span>
-<span><span class="co">##  [63] fastmap_1.1.1                 fansi_1.0.5                  </span></span>
-<span><span class="co">##  [65] digest_0.6.33                 timechange_0.2.0             </span></span>
-<span><span class="co">##  [67] R6_2.5.1                      mime_0.12                    </span></span>
-<span><span class="co">##  [69] textshaping_0.3.7             colorspace_2.1-0             </span></span>
-<span><span class="co">##  [71] rsvg_2.6.0                    RSQLite_2.3.3                </span></span>
-<span><span class="co">##  [73] utf8_1.2.4                    generics_0.1.3               </span></span>
-<span><span class="co">##  [75] robustbase_0.99-1             prettyunits_1.2.0            </span></span>
-<span><span class="co">##  [77] httr_1.4.7                    htmlwidgets_1.6.3            </span></span>
-<span><span class="co">##  [79] S4Arrays_1.2.0                pkgconfig_2.0.3              </span></span>
-<span><span class="co">##  [81] gtable_0.3.4                  blob_1.2.4                   </span></span>
-<span><span class="co">##  [83] impute_1.76.0                 MassSpecWavelet_1.68.0       </span></span>
-<span><span class="co">##  [85] XVector_0.42.0                htmltools_0.5.7              </span></span>
-<span><span class="co">##  [87] MALDIquant_1.22.1             clue_0.3-65                  </span></span>
-<span><span class="co">##  [89] scales_1.3.0                  png_0.1-8                    </span></span>
-<span><span class="co">##  [91] knitr_1.45                    MetaboCoreUtils_1.10.0       </span></span>
-<span><span class="co">##  [93] rjson_0.2.21                  curl_5.1.0                   </span></span>
-<span><span class="co">##  [95] cachem_1.0.8                  stringr_1.5.1                </span></span>
-<span><span class="co">##  [97] BiocVersion_3.18.1            parallel_4.3.2               </span></span>
-<span><span class="co">##  [99] AnnotationDbi_1.64.1          mzID_1.40.0                  </span></span>
-<span><span class="co">## [101] vsn_3.70.0                    desc_1.4.2                   </span></span>
-<span><span class="co">## [103] pillar_1.9.0                  grid_4.3.2                   </span></span>
-<span><span class="co">## [105] vctrs_0.6.5                   MsFeatures_1.10.0            </span></span>
-<span><span class="co">## [107] RANN_2.6.1                    pcaMethods_1.94.0            </span></span>
-<span><span class="co">## [109] promises_1.2.1                dbplyr_2.4.0                 </span></span>
-<span><span class="co">## [111] xtable_1.8-4                  cluster_2.1.6                </span></span>
-<span><span class="co">## [113] evaluate_0.23                 cli_3.6.1                    </span></span>
-<span><span class="co">## [115] compiler_4.3.2                rlang_1.1.2                  </span></span>
-<span><span class="co">## [117] crayon_1.5.2                  QFeatures_1.12.0             </span></span>
-<span><span class="co">## [119] ChemmineR_3.54.0              affy_1.80.0                  </span></span>
-<span><span class="co">## [121] plyr_1.8.9                    fs_1.6.3                     </span></span>
-<span><span class="co">## [123] stringi_1.8.2                 munsell_0.5.0                </span></span>
-<span><span class="co">## [125] Biostrings_2.70.1             lazyeval_0.2.2               </span></span>
-<span><span class="co">## [127] Matrix_1.6-4                  hms_1.1.3                    </span></span>
-<span><span class="co">## [129] MsExperiment_1.4.0            bit64_4.0.5                  </span></span>
-<span><span class="co">## [131] ggplot2_3.4.4                 KEGGREST_1.42.0              </span></span>
-<span><span class="co">## [133] statmod_1.5.0                 shiny_1.8.0                  </span></span>
-<span><span class="co">## [135] highr_0.10                    SummarizedExperiment_1.32.0  </span></span>
-<span><span class="co">## [137] interactiveDisplayBase_1.40.0 AnnotationHub_3.10.0         </span></span>
-<span><span class="co">## [139] igraph_1.5.1                  memoise_2.0.1                </span></span>
-<span><span class="co">## [141] affyio_1.72.0                 bslib_0.6.1                  </span></span>
-<span><span class="co">## [143] DEoptimR_1.1-3                bit_4.0.5</span></span></code></pre>
+<span><span class="co">##   [1] RColorBrewer_1.1-3          jsonlite_1.8.8             </span></span>
+<span><span class="co">##   [3] MultiAssayExperiment_1.30.1 magrittr_2.0.3             </span></span>
+<span><span class="co">##   [5] MALDIquant_1.22.2           rmarkdown_2.27             </span></span>
+<span><span class="co">##   [7] fs_1.6.4                    zlibbioc_1.50.0            </span></span>
+<span><span class="co">##   [9] ragg_1.3.2                  vctrs_0.6.5                </span></span>
+<span><span class="co">##  [11] memoise_2.0.1               RCurl_1.98-1.14            </span></span>
+<span><span class="co">##  [13] base64enc_0.1-3             progress_1.2.3             </span></span>
+<span><span class="co">##  [15] htmltools_0.5.8.1           S4Arrays_1.4.0             </span></span>
+<span><span class="co">##  [17] curl_5.2.1                  SparseArray_1.4.3          </span></span>
+<span><span class="co">##  [19] mzID_1.42.0                 sass_0.4.9                 </span></span>
+<span><span class="co">##  [21] bslib_0.7.0                 htmlwidgets_1.6.4          </span></span>
+<span><span class="co">##  [23] desc_1.4.3                  plyr_1.8.9                 </span></span>
+<span><span class="co">##  [25] impute_1.78.0               lubridate_1.9.3            </span></span>
+<span><span class="co">##  [27] cachem_1.1.0                igraph_2.0.3               </span></span>
+<span><span class="co">##  [29] iterators_1.0.14            mime_0.12                  </span></span>
+<span><span class="co">##  [31] lifecycle_1.0.4             pkgconfig_2.0.3            </span></span>
+<span><span class="co">##  [33] Matrix_1.7-0                R6_2.5.1                   </span></span>
+<span><span class="co">##  [35] fastmap_1.2.0               GenomeInfoDbData_1.2.12    </span></span>
+<span><span class="co">##  [37] MatrixGenerics_1.16.0       clue_0.3-65                </span></span>
+<span><span class="co">##  [39] digest_0.6.35               pcaMethods_1.96.0          </span></span>
+<span><span class="co">##  [41] rsvg_2.6.0                  colorspace_2.1-0           </span></span>
+<span><span class="co">##  [43] AnnotationDbi_1.66.0        textshaping_0.3.7          </span></span>
+<span><span class="co">##  [45] GenomicRanges_1.56.0        RSQLite_2.3.6              </span></span>
+<span><span class="co">##  [47] filelock_1.0.3              fansi_1.0.6                </span></span>
+<span><span class="co">##  [49] timechange_0.3.0            httr_1.4.7                 </span></span>
+<span><span class="co">##  [51] abind_1.4-5                 compiler_4.4.0             </span></span>
+<span><span class="co">##  [53] doParallel_1.0.17           bit64_4.0.5                </span></span>
+<span><span class="co">##  [55] withr_3.0.0                 DBI_1.2.2                  </span></span>
+<span><span class="co">##  [57] highr_0.10                  MASS_7.3-60.2              </span></span>
+<span><span class="co">##  [59] ChemmineR_3.56.0            rappdirs_0.3.3             </span></span>
+<span><span class="co">##  [61] DelayedArray_0.30.1         rjson_0.2.21               </span></span>
+<span><span class="co">##  [63] mzR_2.38.0                  tools_4.4.0                </span></span>
+<span><span class="co">##  [65] PSMatch_1.8.0               glue_1.7.0                 </span></span>
+<span><span class="co">##  [67] QFeatures_1.14.1            grid_4.4.0                 </span></span>
+<span><span class="co">##  [69] cluster_2.1.6               reshape2_1.4.4             </span></span>
+<span><span class="co">##  [71] generics_0.1.3              gtable_0.3.5               </span></span>
+<span><span class="co">##  [73] preprocessCore_1.66.0       tidyr_1.3.1                </span></span>
+<span><span class="co">##  [75] hms_1.1.3                   MetaboCoreUtils_1.12.0     </span></span>
+<span><span class="co">##  [77] xml2_1.3.6                  utf8_1.2.4                 </span></span>
+<span><span class="co">##  [79] XVector_0.44.0              foreach_1.5.2              </span></span>
+<span><span class="co">##  [81] BiocVersion_3.19.1          pillar_1.9.0               </span></span>
+<span><span class="co">##  [83] stringr_1.5.1               limma_3.60.0               </span></span>
+<span><span class="co">##  [85] dplyr_1.1.4                 lattice_0.22-6             </span></span>
+<span><span class="co">##  [87] bit_4.0.5                   tidyselect_1.2.1           </span></span>
+<span><span class="co">##  [89] Biostrings_2.72.0           knitr_1.46                 </span></span>
+<span><span class="co">##  [91] gridExtra_2.3               IRanges_2.38.0             </span></span>
+<span><span class="co">##  [93] SummarizedExperiment_1.34.0 xfun_0.44                  </span></span>
+<span><span class="co">##  [95] Biobase_2.64.0              statmod_1.5.0              </span></span>
+<span><span class="co">##  [97] MSnbase_2.30.1              matrixStats_1.3.0          </span></span>
+<span><span class="co">##  [99] DT_0.33                     stringi_1.8.4              </span></span>
+<span><span class="co">## [101] UCSC.utils_1.0.0            lazyeval_0.2.2             </span></span>
+<span><span class="co">## [103] yaml_2.3.8                  evaluate_0.23              </span></span>
+<span><span class="co">## [105] codetools_0.2-20            MsCoreUtils_1.16.0         </span></span>
+<span><span class="co">## [107] tibble_3.2.1                BiocManager_1.30.23        </span></span>
+<span><span class="co">## [109] affyio_1.74.0               cli_3.6.2                  </span></span>
+<span><span class="co">## [111] systemfonts_1.1.0           munsell_0.5.1              </span></span>
+<span><span class="co">## [113] jquerylib_0.1.4             MassSpecWavelet_1.70.0     </span></span>
+<span><span class="co">## [115] Rcpp_1.0.12                 GenomeInfoDb_1.40.0        </span></span>
+<span><span class="co">## [117] png_0.1-8                   XML_3.99-0.16.1            </span></span>
+<span><span class="co">## [119] parallel_4.4.0              pkgdown_2.0.9              </span></span>
+<span><span class="co">## [121] ggplot2_3.5.1               blob_1.2.4                 </span></span>
+<span><span class="co">## [123] prettyunits_1.2.0           bitops_1.0-7               </span></span>
+<span><span class="co">## [125] MsFeatures_1.12.0           affy_1.82.0                </span></span>
+<span><span class="co">## [127] scales_1.3.0                ncdf4_1.22                 </span></span>
+<span><span class="co">## [129] purrr_1.0.2                 crayon_1.5.2               </span></span>
+<span><span class="co">## [131] rlang_1.1.3                 vsn_3.72.0                 </span></span>
+<span><span class="co">## [133] KEGGREST_1.44.0</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
 </h2>
-<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">
 <div id="ref-rainer_modular_2022" class="csl-entry">
 Rainer, Johannes, Andrea Vicini, Liesa Salzer, Jan Stanstrup, Josep M.
 Badia, Steffen Neumann, Michael A. Stravs, et al. 2022. <span>“A
@@ -1235,13 +1440,11 @@ <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="a
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
+  <p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
 </div>
 
     </footer>
diff --git a/articles/Spectra-matching-with-MetaboAnnotation_files/figure-html/unnamed-chunk-16-1.png b/articles/Spectra-matching-with-MetaboAnnotation_files/figure-html/unnamed-chunk-16-1.png
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diff --git a/articles/analyzing-MS-data-from-different-sources-with-Spectra.html b/articles/analyzing-MS-data-from-different-sources-with-Spectra.html
index 0310bb2..953e046 100644
--- a/articles/analyzing-MS-data-from-different-sources-with-Spectra.html
+++ b/articles/analyzing-MS-data-from-different-sources-with-Spectra.html
@@ -17,7 +17,7 @@
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 <script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
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-<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package">
+<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package">
 <meta property="og:description" content="SpectraTutorials">
 <meta property="og:image" content="https://jorainer.github.io/SpectraTutorials/logo.png">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
@@ -29,11 +29,11 @@
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
     
     <a class="navbar-brand me-2" href="../index.html">SpectraTutorials</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -48,9 +48,9 @@
 <li class="active nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
     <a class="dropdown-item" href="../articles/Spectra-backends.html">Spectra: an expandable infrastructure to handle mass spectrometry data</a>
     <a class="dropdown-item" href="../articles/Spectra-matching-with-MetaboAnnotation.html">MS/MS Spectra Matching with the `MetaboAnnotation` Package</a>
-    <a class="dropdown-item" href="../articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
   </div>
 </li>
 <li class="nav-item">
@@ -98,7 +98,7 @@ <h4 data-toc-skip class="author">Johannes
 
     
     
-<p><strong>Last modified:</strong> 2023-12-05 09:09:17.878995<br><strong>Compiled</strong>: Tue Dec 5 09:15:46 2023</p>
+<p><strong>Last modified:</strong> 2024-05-17 14:04:17.424737<br><strong>Compiled</strong>: Fri May 17 14:09:36 2024</p>
 <div class="section level2">
 <h2 id="overview">Overview<a class="anchor" aria-label="anchor" href="#overview"></a>
 </h2>
@@ -111,8 +111,8 @@ <h3 id="description">Description<a class="anchor" aria-label="anchor" href="#des
 additional resources of MS data exist, such as spectra libraries and
 databases, all with their own specific file formats and database systems
 that sometimes do not support manipulations of the original data.</p>
-<p>Learning from experiences with the <em><a href="https://bioconductor.org/packages/3.18/MSnbase" class="external-link">MSnbase</a></em>
-Bioconductor package, the <em><a href="https://bioconductor.org/packages/3.18/Spectra" class="external-link">Spectra</a></em>
+<p>Learning from experiences with the <em><a href="https://bioconductor.org/packages/3.19/MSnbase" class="external-link">MSnbase</a></em>
+Bioconductor package, the <em><a href="https://bioconductor.org/packages/3.19/Spectra" class="external-link">Spectra</a></em>
 package was developed to provide an even more flexible and expandable
 infrastructure for MS data in R. This package implements a clear
 separation between the user interface and the code to provide, store and
@@ -146,15 +146,18 @@ <h3 id="pre-requisites">Pre-requisites<a class="anchor" aria-label="anchor" href
 <h3 id="installation-and-participation">Installation and Participation<a class="anchor" aria-label="anchor" href="#installation-and-participation"></a>
 </h3>
 <ul>
+<li><p>The present version of the tutorial bases on package versions
+available through the <strong>Bioconductor release
+3.19</strong>.</p></li>
 <li><p>Get the <a href="https://hub.docker.com/r/jorainer/spectra_tutorials" class="external-link">docker
 image</a> of this tutorial with
-<code>docker pull jorainer/spectra_tutorials:latest</code>.</p></li>
+<code>docker pull jorainer/spectra_tutorials:RELEASE_3_19</code>.</p></li>
 <li>
 <p>Start docker using</p>
 <pre><code>docker run \
     -e PASSWORD=bioc \
     -p 8787:8787 \
-    jorainer/spectra_tutorials:latest</code></pre>
+    jorainer/spectra_tutorials:RELEASE_3_19</code></pre>
 </li>
 <li><p>Enter <code>http://localhost:8787</code> in a web browser and log
 in with username <code>rstudio</code> and password
@@ -350,7 +353,7 @@ <h3 id="ms-data-import-and-handling">MS data import and handling<a class="anchor
 which can be thought of as a <code>data.frame</code> with rows being the
 individual spectra and columns the spectra <em>variables</em> (such as
 <code>"rtime"</code>, i.e. the retention time). The
-<code>spectraVariables</code> function lists all available variables
+<code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraVariables()</a></code> function lists all available variables
 within such a <code>Spectra</code> object.</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' List all available spectra variables (attributes)</span></span>
@@ -376,7 +379,7 @@ <h3 id="ms-data-import-and-handling">MS data import and handling<a class="anchor
 <code>$</code> and its name or by using its dedicated access function
 (which is the preferred way). Below we extract the retention times of
 the first spectra using either <code>$rtime</code> or the function
-<code>rtime</code>.</p>
+<code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">rtime()</a></code>.</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Access the spectras' retention time</span></span>
 <span><span class="va">sps_all</span><span class="op">$</span><span class="va">rtime</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
@@ -392,14 +395,14 @@ <h3 id="ms-data-import-and-handling">MS data import and handling<a class="anchor
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/utils/object.size.html" class="external-link">object.size</a></span><span class="op">(</span><span class="va">sps_all</span><span class="op">)</span>, units <span class="op">=</span> <span class="st">"MB"</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## 0.4 Mb</span></span></code></pre>
 <p>The spectra’s m/z and intensity values can be accessed with the
-<code>mz</code> or <code>intensity</code> functions or also with the
-<code>peaksData</code> function that returns a list of numeric matrices
-with the m/z and intensity values. By using an <code>MsBackendMzR</code>
-backend, these are retrieved on demand from the original data files each
-time the functions are called.</p>
+<code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">mz()</a></code> or <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">intensity()</a></code> functions or also with the
+<code><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData()</a></code> function that returns a list of numeric
+matrices with the m/z and intensity values. By using an
+<code>MsBackendMzR</code> backend, these are retrieved on demand from
+the original data files each time the functions are called.</p>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">## Show the first 6 lines of the first spectrum's peak matrix</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_all</span><span class="op">)</span><span class="op">[[</span><span class="fl">1L</span><span class="op">]</span><span class="op">]</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_all</span><span class="op">)</span><span class="op">[[</span><span class="fl">1L</span><span class="op">]</span><span class="op">]</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##            mz intensity</span></span>
 <span><span class="co">## [1,] 50.22647  383.7802</span></span>
 <span><span class="co">## [2,] 52.97586  377.5944</span></span>
@@ -414,7 +417,7 @@ <h3 id="ms-data-import-and-handling">MS data import and handling<a class="anchor
 way the data is stored (i.e. which backend is used).</p>
 <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Change backend to a MsBackendDataFrame: load data into memory</span></span>
-<span><span class="va">sps_all</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">setBackend</a></span><span class="op">(</span><span class="va">sps_all</span>, <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/MsBackend.html" class="external-link">MsBackendDataFrame</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span><span class="va">sps_all</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/backendInitialize.html" class="external-link">setBackend</a></span><span class="op">(</span><span class="va">sps_all</span>, <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/MsBackend.html" class="external-link">MsBackendDataFrame</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p>The size of our <code>Spectra</code> object is now larger, since the
 full data has been loaded into memory.</p>
 <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
@@ -435,12 +438,12 @@ <h3 id="ms-data-import-and-handling">MS data import and handling<a class="anchor
 <span><span class="co">## 3         1     1.491         5</span></span>
 <span><span class="co">##  ... 33 more variables/columns.</span></span>
 <span><span class="co">## Processing:</span></span>
-<span><span class="co">##  Switch backend from MsBackendMzR to MsBackendDataFrame [Tue Dec  5 09:15:50 2023]</span></span></code></pre>
+<span><span class="co">##  Switch backend from MsBackendMzR to MsBackendDataFrame [Fri May 17 14:09:40 2024]</span></span></code></pre>
 <p>In our workshop, we next want to identify in our experimental data
 MS2 spectra that were generated from an ion that matches the m/z of the
 <code>[M+H]+</code> ion of the metabolite cystine (one of the standards
 added to the sample mix measured in the present experimental data). We
-thus use below the <code>filterPrecursorMzValues</code> function to
+thus use below the <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">filterPrecursorMzValues()</a></code> function to
 subset the data to MS2 spectra matching that m/z (accepting a difference
 in m/z of 10 parts-per-million (ppm)).</p>
 <div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
@@ -448,7 +451,7 @@ <h3 id="ms-data-import-and-handling">MS data import and handling<a class="anchor
 <span><span class="va">mz</span> <span class="op">&lt;-</span> <span class="fl">241.0311</span></span>
 <span></span>
 <span><span class="co">#' Subset the dataset to MS2 spectra matching the m/z</span></span>
-<span><span class="va">sps</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">filterPrecursorMzValues</a></span><span class="op">(</span><span class="va">sps_all</span>, mz <span class="op">=</span> <span class="va">mz</span>, ppm <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span>
+<span><span class="va">sps</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">filterPrecursorMzValues</a></span><span class="op">(</span><span class="va">sps_all</span>, mz <span class="op">=</span> <span class="va">mz</span>, ppm <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span>
 <span><span class="va">sps</span></span></code></pre></div>
 <pre><code><span><span class="co">## MSn data (Spectra) with 6 spectra in a MsBackendDataFrame backend:</span></span>
 <span><span class="co">##     msLevel     rtime scanIndex</span></span>
@@ -461,14 +464,14 @@ <h3 id="ms-data-import-and-handling">MS data import and handling<a class="anchor
 <span><span class="co">## 6         2   240.020       804</span></span>
 <span><span class="co">##  ... 33 more variables/columns.</span></span>
 <span><span class="co">## Processing:</span></span>
-<span><span class="co">##  Switch backend from MsBackendMzR to MsBackendDataFrame [Tue Dec  5 09:15:50 2023]</span></span>
-<span><span class="co">##  Filter: select spectra with precursor m/z matching 241.0311 [Tue Dec  5 09:15:50 2023]</span></span></code></pre>
+<span><span class="co">##  Switch backend from MsBackendMzR to MsBackendDataFrame [Fri May 17 14:09:40 2024]</span></span>
+<span><span class="co">##  Filter: select spectra with precursor m/z matching 241.0311 [Fri May 17 14:09:40 2024]</span></span></code></pre>
 <p>In total 6 spectra matched our target precursor m/z.</p>
 </div>
 <div class="section level3">
 <h3 id="data-processing-and-manipulation">Data processing and manipulation<a class="anchor" aria-label="anchor" href="#data-processing-and-manipulation"></a>
 </h3>
-<p>The <code>plotSpectra</code> function can be used to visualize
+<p>The <code><a href="https://rdrr.io/pkg/Spectra/man/spectra-plotting.html" class="external-link">plotSpectra()</a></code> function can be used to visualize
 spectra. Below we plot the first spectrum from our data subset.</p>
 <div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Plot the first spectrum</span></span>
@@ -481,7 +484,7 @@ <h3 id="data-processing-and-manipulation">Data processing and manipulation<a cla
 function that takes intensity values from each spectrum as input and
 returns a logical value whether the peak should be retained
 (<code>TRUE</code>) or not (<code>FALSE</code>). This function is then
-passed to the <code>filterIntensity</code> function to perform the
+passed to the <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">filterIntensity()</a></code> function to perform the
 actual filtering of the spectra.</p>
 <div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Define a filtering function</span></span>
@@ -501,7 +504,7 @@ <h3 id="data-processing-and-manipulation">Data processing and manipulation<a cla
 (i.e. a <code>numeric</code> matrix with columns containing the m/z and
 intensity values) and returns a two-column <code>numeric</code> matrix.
 This function is then passed with parameter <code>FUN</code> to the
-<code>addProcessing</code> function to perform the desired data
+<code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">addProcessing()</a></code> function to perform the desired data
 processing.</p>
 <div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Define a function to scale the intensities. `x` is expected to be a</span></span>
@@ -513,14 +516,16 @@ <h3 id="data-processing-and-manipulation">Data processing and manipulation<a cla
 <span><span class="op">}</span></span>
 <span><span class="co">#' *Apply* the function to the data</span></span>
 <span><span class="va">sps</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">addProcessing</a></span><span class="op">(</span><span class="va">sps</span>, <span class="va">scale_int</span><span class="op">)</span></span></code></pre></div>
-<p>The <code>addProcessing</code> function allows to add
+<p>The <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">addProcessing()</a></code> function allows to add
 <strong>any</strong> user-defined data manipulation operation on MS data
-(i.e. the peaks matrices) in a <code>Spectra</code> object.</p>
+(i.e. the peaks matrices) in a <code>Spectra</code> object. Note that,
+for scaling of peak intensities, there would also be a dedicated
+function, <code><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">scalePeaks()</a></code>, available.</p>
 <p>To show the effect of the data processing we extract the intensities
 of the first spectrum:</p>
 <div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Get the intensities after scaling</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps</span><span class="op">)</span><span class="op">[[</span><span class="fl">1L</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
+<span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps</span><span class="op">)</span><span class="op">[[</span><span class="fl">1L</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
 <pre><code><span><span class="co">##             mz  intensity</span></span>
 <span><span class="co">## [1,]  74.02289  13.439402</span></span>
 <span><span class="co">## [2,] 120.01111  93.631648</span></span>
@@ -542,7 +547,7 @@ <h3 id="spectrum-data-comparison">Spectrum data comparison<a class="anchor" aria
 spectrum correspond to which from the other spectrum based on their
 mass-to-charge ratios). We specify <code>ppm = 20</code> so that peaks
 with a difference in m/z smaller than 20ppm will be considered matching.
-By default, <code>compareSpectra</code> uses the (normalized)
+By default, <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">compareSpectra()</a></code> uses the (normalized)
 dot-product to calculate the similarity between the spectra, but a
 variety of different algorithms can be used instead (see the <a href="https://rformassspectrometry.github.io/Spectra/articles/Spectra.html#comparing-spectra" class="external-link"><em>Spectra
 vignette</em></a> for more information).</p>
@@ -559,7 +564,7 @@ <h3 id="spectrum-data-comparison">Spectrum data comparison<a class="anchor" aria
 <span><span class="co">## [6,] 0.8818719 0.9486682 0.9478201 0.9494561 0.9994800 1.0000000</span></span></code></pre>
 <p>The pairwise spectra similarities are represented with the heatmap
 below (note that RStudio in the docker might crash by the
-<code>pheatmap</code> call - to avoid this add
+<code><a href="https://rdrr.io/pkg/pheatmap/man/pheatmap.html" class="external-link">pheatmap()</a></code> call - to avoid this add
 <code>filename = "hm.pdf"</code> to the <code>heatmap</code> call).</p>
 <div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">pheatmap</span><span class="op">)</span></span>
@@ -591,13 +596,13 @@ <h3 id="comparing-spectra-against-massbank">Comparing spectra against MassBank<a
 <p>For some of these databases <code>MsBackend</code> interfaces are
 already implemented allowing inclusion of their data directly into
 R-based analysis workflows. Access to MassBank data is for example
-possible with the <em><a href="https://bioconductor.org/packages/3.18/MsBackendMassbank" class="external-link">MsBackendMassbank</a></em>
+possible with the <em><a href="https://bioconductor.org/packages/3.19/MsBackendMassbank" class="external-link">MsBackendMassbank</a></em>
 package. This package provides the <code>MsBackendMassbank</code> for
 import/export of <a href="https://github.com/MassBank/MassBank-data" class="external-link">MassBank files</a> as
 well as the <code>MsBackendMassbankSql</code> backend that directly
 interfaces the MassBank MySQL database.</p>
 <p>Below we connect to a local installation of the MassBank MySQL
-database (release <em>2021.03</em> which is provided within the docker
+database (release <em>2023.11</em> which is provided within the docker
 image of this tutorial).</p>
 <div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://rmariadb.r-dbi.org" class="external-link">RMariaDB</a></span><span class="op">)</span></span>
@@ -631,8 +636,8 @@ <h3 id="comparing-spectra-against-massbank">Comparing spectra against MassBank<a
 <span><span class="co">##  Use  'spectraVariables' to list all of them.</span></span></code></pre>
 <p>Alternatively, if no MySQL database system is available or if this
 tutorial can not be run within <em>docker</em>, the MassBank data can
-also be retrieved from Bioconductor’s <em><a href="https://bioconductor.org/packages/3.18/AnnotationHub" class="external-link">AnnotationHub</a></em>.
-In that case <code>MsBackendCompDb</code> backend (defined in the <em><a href="https://bioconductor.org/packages/3.18/CompoundDb" class="external-link">CompoundDb</a></em>)
+also be retrieved from Bioconductor’s <em><a href="https://bioconductor.org/packages/3.19/AnnotationHub" class="external-link">AnnotationHub</a></em>.
+In that case <code>MsBackendCompDb</code> backend (defined in the <em><a href="https://bioconductor.org/packages/3.19/CompoundDb" class="external-link">CompoundDb</a></em>)
 is used instead, but both backends retrieve their data from a SQL
 database and have the same properties. Below we list MassBank
 releases/versions available in Bioconductor’s
@@ -641,8 +646,8 @@ <h3 id="comparing-spectra-against-massbank">Comparing spectra against MassBank<a
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">AnnotationHub</span><span class="op">)</span></span>
 <span><span class="va">ah</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/AnnotationHub/man/AnnotationHub-class.html" class="external-link">AnnotationHub</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/AnnotationHub/man/AnnotationHub-class.html" class="external-link">query</a></span><span class="op">(</span><span class="va">ah</span>, <span class="st">"MassBank"</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## AnnotationHub with 3 records</span></span>
-<span><span class="co">## # snapshotDate(): 2023-10-20</span></span>
+<pre><code><span><span class="co">## AnnotationHub with 6 records</span></span>
+<span><span class="co">## # snapshotDate(): 2024-04-29</span></span>
 <span><span class="co">## # $dataprovider: MassBank</span></span>
 <span><span class="co">## # $species: NA</span></span>
 <span><span class="co">## # $rdataclass: CompDb</span></span>
@@ -654,11 +659,14 @@ <h3 id="comparing-spectra-against-massbank">Comparing spectra against MassBank<a
 <span><span class="co">##              title                                </span></span>
 <span><span class="co">##   AH107048 | MassBank CompDb for release 2021.03  </span></span>
 <span><span class="co">##   AH107049 | MassBank CompDb for release 2022.06  </span></span>
-<span><span class="co">##   AH111334 | MassBank CompDb for release 2022.12.1</span></span></code></pre>
-<p>We can retrieve the respective release and access its spectra data
-with the code below. Note that <em>AnnotationHub</em> will cache the
-downloaded database locally and any subsequent call will not download
-the database again.</p>
+<span><span class="co">##   AH111334 | MassBank CompDb for release 2022.12.1</span></span>
+<span><span class="co">##   AH116164 | MassBank CompDb for release 2023.06  </span></span>
+<span><span class="co">##   AH116165 | MassBank CompDb for release 2023.09  </span></span>
+<span><span class="co">##   AH116166 | MassBank CompDb for release 2023.11</span></span></code></pre>
+<p>We can retrieve the respective release (<em>2021.03</em> in this
+case) and access its spectra data with the code below. Note that
+<em>AnnotationHub</em> will cache the downloaded database locally and
+any subsequent call will not download the database again.</p>
 <div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">mb</span> <span class="op">&lt;-</span> <span class="va">ah</span><span class="op">[[</span><span class="st">"AH107048"</span><span class="op">]</span><span class="op">]</span></span>
 <span><span class="va">mbank</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">Spectra</a></span><span class="op">(</span><span class="va">mb</span><span class="op">)</span></span></code></pre></div>
@@ -673,12 +681,12 @@ <h3 id="comparing-spectra-against-massbank">Comparing spectra against MassBank<a
 <pre><code><span><span class="co">## 6.6 Mb</span></span></code></pre>
 <p>Any operation on a <code>Spectra</code> object with this backend will
 load the requested data from the database on demand. Calling
-<code>intensity</code> on such a <code>Spectra</code> object would for
+<code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">intensity()</a></code> on such a <code>Spectra</code> object would for
 example retrieve the intensity values for all spectra from the database.
 Below we extract the peaks matrix for the first spectrum.</p>
 <div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Get peaks matrix for the first spectrum</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">mbank</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span><span class="op">[[</span><span class="fl">1L</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
+<span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">mbank</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span><span class="op">[[</span><span class="fl">1L</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
 <pre><code><span><span class="co">##             mz    intensity</span></span>
 <span><span class="co">##  [1,] 146.7608     0.980184</span></span>
 <span><span class="co">##  [2,] 158.8635   938.145447</span></span>
@@ -711,7 +719,7 @@ <h3 id="comparing-spectra-against-massbank">Comparing spectra against MassBank<a
 <div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Scale intensities for all MassBank spectra</span></span>
 <span><span class="va">mbank</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">addProcessing</a></span><span class="op">(</span><span class="va">mbank</span>, <span class="va">scale_int</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">mbank</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span><span class="op">[[</span><span class="fl">1L</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
+<span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">mbank</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span><span class="op">[[</span><span class="fl">1L</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
 <pre><code><span><span class="co">##             mz    intensity</span></span>
 <span><span class="co">##  [1,] 146.7608 4.090250e-03</span></span>
 <span><span class="co">##  [2,] 158.8635 3.914826e+00</span></span>
@@ -743,20 +751,20 @@ <h3 id="comparing-spectra-against-massbank">Comparing spectra against MassBank<a
 is cached within the object’s <em>lazy evaluation queue</em> and any
 such operation is applied to the m/z and intensity values
 <em>on-the-fly</em> whenever the data is requested (i.e. whenever
-<code>mz</code> or <code>intensity</code> is called on it). As a side
-effect, this also allows to <em>undo</em> certain data operations by
-simply calling <code>reset</code> on the <code>Spectra</code>
+<code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">mz()</a></code> or <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">intensity()</a></code> is called on it). As a
+side effect, this also allows to <em>undo</em> certain data operations
+by simply calling <code><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">reset()</a></code> on the <code>Spectra</code>
 object.</p>
 <p>We next want to compare our experimental spectra against the spectra
 from MassBank. Instead of comparing against all 86576 available spectra,
 we first filter the MassBank database to spectra with a precursor m/z
 matching the one of the <em>[M+H]+</em> ion of cystine. Note that, as an
-alternative to the <code>filterPrecursorMzValues</code> used below, we
-could also use the <code>containsMz</code> function to screen for
+alternative to the <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">filterPrecursorMzValues()</a></code> used below, we
+could also use the <code><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">containsMz()</a></code> function to screen for
 spectra containing an actual peak matching the precursor m/z.</p>
 <div class="sourceCode" id="cb41"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Filter MassBank for spectra of precursor ion</span></span>
-<span><span class="va">mbank_sub</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">filterPrecursorMzValues</a></span><span class="op">(</span><span class="va">mbank</span>, mz <span class="op">=</span> <span class="va">mz</span>, ppm <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span>
+<span><span class="va">mbank_sub</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">filterPrecursorMzValues</a></span><span class="op">(</span><span class="va">mbank</span>, mz <span class="op">=</span> <span class="va">mz</span>, ppm <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span>
 <span><span class="va">mbank_sub</span></span></code></pre></div>
 <pre><code><span><span class="co">## MSn data (Spectra) with 12 spectra in a MsBackendMassbankSql backend:</span></span>
 <span><span class="co">##       msLevel precursorMz  polarity</span></span>
@@ -776,10 +784,10 @@ <h3 id="comparing-spectra-against-massbank">Comparing spectra against MassBank<a
 <span><span class="co">##  Use  'spectraVariables' to list all of them.</span></span>
 <span><span class="co">## Lazy evaluation queue: 1 processing step(s)</span></span>
 <span><span class="co">## Processing:</span></span>
-<span><span class="co">##  Filter: select spectra with precursor m/z matching 241.0311 [Tue Dec  5 09:15:59 2023]</span></span></code></pre>
+<span><span class="co">##  Filter: select spectra with precursor m/z matching 241.0311 [Fri May 17 14:09:48 2024]</span></span></code></pre>
 <p>This left us with 12 spectra for which we calculate the similarity
 against each of our experimental spectra using the
-<code>compareSpectra</code> function.</p>
+<code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">compareSpectra()</a></code> function.</p>
 <div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Compare MassBank subset to experimental spectra</span></span>
 <span><span class="va">res</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">compareSpectra</a></span><span class="op">(</span><span class="va">sps</span>, <span class="va">mbank_sub</span>, ppm <span class="op">=</span> <span class="fl">20</span><span class="op">)</span></span>
@@ -846,8 +854,9 @@ <h3 id="comparing-spectra-against-massbank">Comparing spectra against MassBank<a
 spectrum actually represents. <code>Spectra</code> objects are generally
 very flexible and can have arbitrarily many additional annotation fields
 (i.e. <em>spectra variables</em>) for each spectrum. Thus, we below use
-the <code>spectraVariables</code> function to list all of the variables
-that are available in our MassBank <code>Spectra</code> object.</p>
+the <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraVariables()</a></code> function to list all of the
+variables that are available in our MassBank <code>Spectra</code>
+object.</p>
 <div class="sourceCode" id="cb50"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' What variables are available in MassBank</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraVariables</a></span><span class="op">(</span><span class="va">mbank_sub</span><span class="op">)</span></span></code></pre></div>
@@ -913,11 +922,11 @@ <h3 id="comparing-spectra-against-massbank">Comparing spectra against MassBank<a
 <h3 id="data-export">Data export<a class="anchor" aria-label="anchor" href="#data-export"></a>
 </h3>
 <p>At last we want to export our spectra to a file in MGF format. For
-this we use the <em><a href="https://bioconductor.org/packages/3.18/MsBackendMgf" class="external-link">MsBackendMgf</a></em>
+this we use the <em><a href="https://bioconductor.org/packages/3.19/MsBackendMgf" class="external-link">MsBackendMgf</a></em>
 package which provides the <code>MsBackendMgf</code> backend that adds
 support for MGF file import/export to <code>Spectra</code> objects.</p>
 <p>Data from <code>Spectra</code> objects can generally be exported with
-the <code>export</code> function. The format in which the data is
+the <code><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">export()</a></code> function. The format in which the data is
 exported depends on the specified <code>MsBackend</code> class. By using
 an instance of <code>MsBackendMgf</code> we define to export the data to
 a file in MGF format.</p>
@@ -952,7 +961,7 @@ <h3 id="comparing-spectra-against-hmdb">Comparing spectra against HMDB<a class="
 package. This packages allows for example to create annotation databases
 for MoNA, MassBank or HMDB. <code>CompDb</code> with annotations for
 small compounds from different resources will become available also on
-Bioconductors <em><a href="https://bioconductor.org/packages/3.18/AnnotationHub" class="external-link">AnnotationHub</a></em>.</p>
+Bioconductors <em><a href="https://bioconductor.org/packages/3.19/AnnotationHub" class="external-link">AnnotationHub</a></em>.</p>
 <p>A <code>CompDb</code> database containing all compound annotations
 along with all (experimental) MS2 spectra is provided as a data release
 in the <a href="https://jorainer.github.io/MetaboAnnotationTutorials/" class="external-link">MetaboAnnotationTutorials</a>
@@ -1032,12 +1041,12 @@ <h3 id="comparing-spectra-against-hmdb">Comparing spectra against HMDB<a class="
 <p>Also here, we want to filter the data resource first for spectra with
 a matching precursor m/z. Unfortunately, HMDB does not provided the
 spectra’s precursor m/z and we hence need to use the
-<code>containsMz</code> function to find spectra containing a peak with
-an m/z matching the m/z of our ion of interest. In addition, We need to
-use a rather large <code>tolerance</code> value (which defines the
-maximal acceptable absolute difference in m/z values) since some of the
-experimental spectra in HMDB seem to be recorded by not well calibrated
-instrument.</p>
+<code><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">containsMz()</a></code> function to find spectra containing a peak
+with an m/z matching the m/z of our ion of interest. In addition, We
+need to use a rather large <code>tolerance</code> value (which defines
+the maximal acceptable absolute difference in m/z values) since some of
+the experimental spectra in HMDB seem to be recorded by not well
+calibrated instrument.</p>
 <div class="sourceCode" id="cb66"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Identify spectra containing a peak matching cystine m/z</span></span>
 <span><span class="va">has_mz</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">containsMz</a></span><span class="op">(</span><span class="va">hmdb</span>, mz <span class="op">=</span> <span class="va">mz</span>, tolerance <span class="op">=</span> <span class="fl">0.2</span><span class="op">)</span></span></code></pre></div>
@@ -1097,7 +1106,8 @@ <h3 id="an-alternative-way-to-store-ms-data">An alternative way to store MS data
 eventually in even better performance).</p>
 <div class="sourceCode" id="cb73"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://rsqlite.r-dbi.org" class="external-link">RSQLite</a></span><span class="op">)</span></span>
-<span><span class="va">scon</span> <span class="op">&lt;-</span> <span class="fu">dbConnect</span><span class="op">(</span><span class="fu"><a href="https://rsqlite.r-dbi.org/reference/SQLite.html" class="external-link">SQLite</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempfile</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span><span class="va">db_file</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempfile</a></span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="va">scon</span> <span class="op">&lt;-</span> <span class="fu">dbConnect</span><span class="op">(</span><span class="fu"><a href="https://rsqlite.r-dbi.org/reference/SQLite.html" class="external-link">SQLite</a></span><span class="op">(</span><span class="op">)</span>, <span class="va">db_file</span><span class="op">)</span></span></code></pre></div>
 <p>We can next import the data from our example experiment into the
 selected database.</p>
 <div class="sourceCode" id="cb74"><pre class="downlit sourceCode r">
@@ -1105,14 +1115,27 @@ <h3 id="an-alternative-way-to-store-ms-data">An alternative way to store MS data
 <span><span class="co">## Create a database containing the full raw data</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/MsBackendSql/man/MsBackendSql.html" class="external-link">createMsBackendSqlDatabase</a></span><span class="op">(</span><span class="va">scon</span>, <span class="va">fls</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## [1] TRUE</span></span></code></pre>
-<p>The <code>MsBackendSql</code> allows now to access and use the MS
-data stored in such a database. We simply provide the connection to the
-database as a parameter to the <code>Spectra</code> function and use the
-<code>MsBackendSql</code> as <em>backend</em>.</p>
 <div class="sourceCode" id="cb76"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sps_db</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">Spectra</a></span><span class="op">(</span><span class="va">scon</span>, source <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/MsBackendSql/man/MsBackendSql.html" class="external-link">MsBackendSql</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<code class="sourceCode R"><span><span class="fu">dbDisconnect</span><span class="op">(</span><span class="va">scon</span><span class="op">)</span></span></code></pre></div>
+<p>The <code>MsBackendSql</code> or <code>MsBackendOfflineSql</code>
+backends allow now to access and use the MS data stored in such a
+database. The <code>MsBackendOfflineSql</code> does not keep an active
+connection to the database but reconnects to the database when needed.
+While having a slightly lower performance, this allows the object to be
+<em>serialized</em>, e.g. saved to and reloaded from disk using the
+<code><a href="https://rdrr.io/r/base/save.html" class="external-link">save()</a></code> and <code><a href="https://rdrr.io/r/base/load.html" class="external-link">load()</a></code> functions. To access the
+data within the SQL database We simply the name to the database as a
+parameter to the <code><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">Spectra()</a></code> function and use the
+<code>MsBackendOfflineSql</code> as <em>backend</em>. For the
+<code>MsBackendOfflineSql</code> we need to specify the SQL
+<em>driver</em>, in our case, since we are using a SQLite database, it
+will be <code><a href="https://rsqlite.r-dbi.org/reference/SQLite.html" class="external-link">SQLite()</a></code> as well as the name of the database (which
+for SQLite is the file name of the database).</p>
+<div class="sourceCode" id="cb77"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sps_db</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">Spectra</a></span><span class="op">(</span><span class="va">db_file</span>, drv <span class="op">=</span> <span class="fu"><a href="https://rsqlite.r-dbi.org/reference/SQLite.html" class="external-link">SQLite</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>                  source <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/MsBackendSql/man/MsBackendOfflineSql.html" class="external-link">MsBackendOfflineSql</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">sps_db</span></span></code></pre></div>
-<pre><code><span><span class="co">## MSn data (Spectra) with 1578 spectra in a MsBackendSql backend:</span></span>
+<pre><code><span><span class="co">## MSn data (Spectra) with 1578 spectra in a MsBackendOfflineSql backend:</span></span>
 <span><span class="co">##        msLevel precursorMz  polarity</span></span>
 <span><span class="co">##      &lt;integer&gt;   &lt;numeric&gt; &lt;integer&gt;</span></span>
 <span><span class="co">## 1            1          NA         1</span></span>
@@ -1121,68 +1144,71 @@ <h3 id="an-alternative-way-to-store-ms-data">An alternative way to store MS data
 <span><span class="co">## 4            1          NA         1</span></span>
 <span><span class="co">## 5            1          NA         1</span></span>
 <span><span class="co">## ...        ...         ...       ...</span></span>
-<span><span class="co">## 1574         2    130.9659         1</span></span>
-<span><span class="co">## 1575         2    175.1178         1</span></span>
-<span><span class="co">## 1576         2    193.9281         1</span></span>
-<span><span class="co">## 1577         2    107.9501         1</span></span>
-<span><span class="co">## 1578         2     95.0608         1</span></span>
+<span><span class="co">## 1574         2    107.9501         1</span></span>
+<span><span class="co">## 1575         1          NA         1</span></span>
+<span><span class="co">## 1576         2     95.0608         1</span></span>
+<span><span class="co">## 1577         1          NA         1</span></span>
+<span><span class="co">## 1578         1          NA         1</span></span>
 <span><span class="co">##  ... 34 more variables/columns.</span></span>
 <span><span class="co">##  Use  'spectraVariables' to list all of them.</span></span>
-<span><span class="co">## Database: /tmp/Rtmp8PSjCo/file72476a12197</span></span></code></pre>
+<span><span class="co">## Database: /tmp/RtmpscQFqX/fileeae479f47ad</span></span></code></pre>
 <p>The memory footprint of this <code>Spectra</code> object is only very
 small as all the data is retrieved from the database
 <em>on-the-fly</em>.</p>
-<div class="sourceCode" id="cb78"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb79"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/utils/object.size.html" class="external-link">object.size</a></span><span class="op">(</span><span class="va">sps_db</span><span class="op">)</span>, units <span class="op">=</span> <span class="st">"MB"</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## 0.1 Mb</span></span></code></pre>
 <p>The performance of this backend is also higher than for example of a
 <code>MsBackendMzR</code>. Below we compare the performance of the
-<code>MsBackendSql</code> on the present small data set to a
+<code>MsBackendOfflineSql</code> on the present small data set to a
 <code>MsBackendDataFrame</code> (data in-memory) and a
-<code>MsBackendMzR</code> (data retrieved from original file).</p>
-<div class="sourceCode" id="cb80"><pre class="downlit sourceCode r">
+<code>MsBackendMzR</code> (data retrieved from original file). Note that
+in more recent versions of the <em>Spectra</em> package a
+higher-performance in-memory backend would be available
+(<code>MsBackendMemory</code>).</p>
+<div class="sourceCode" id="cb81"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">## Load the data to different backends</span></span>
 <span><span class="va">sps_mzr</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">Spectra</a></span><span class="op">(</span><span class="va">fls</span>, source <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/MsBackend.html" class="external-link">MsBackendMzR</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="va">sps_df</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">setBackend</a></span><span class="op">(</span><span class="va">sps_mzr</span>, <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/MsBackend.html" class="external-link">MsBackendDataFrame</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">sps_df</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/backendInitialize.html" class="external-link">setBackend</a></span><span class="op">(</span><span class="va">sps_mzr</span>, <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/MsBackend.html" class="external-link">MsBackendDataFrame</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
 <span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/joshuaulrich/microbenchmark/" class="external-link">microbenchmark</a></span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_mzr</span><span class="op">)</span>,</span>
-<span>               <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_df</span><span class="op">)</span>,</span>
-<span>               <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_db</span><span class="op">)</span>,</span>
+<span><span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_mzr</span><span class="op">)</span>,</span>
+<span>               <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_df</span><span class="op">)</span>,</span>
+<span>               <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_db</span><span class="op">)</span>,</span>
 <span>               times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Unit: milliseconds</span></span>
-<span><span class="co">##                expr       min        lq      mean   median        uq       max</span></span>
-<span><span class="co">##  peaksData(sps_mzr) 257.38043 279.94393 409.83186 309.5956 350.96996 956.12619</span></span>
-<span><span class="co">##   peaksData(sps_df)  81.88257  98.45020 104.34838 104.2593 118.27750 118.43437</span></span>
-<span><span class="co">##   peaksData(sps_db)  47.46898  53.22407  62.45025  60.9762  74.91102  79.79879</span></span>
-<span><span class="co">##  neval</span></span>
-<span><span class="co">##     10</span></span>
-<span><span class="co">##     10</span></span>
-<span><span class="co">##     10</span></span></code></pre>
-<p>Thus, the <code>MsBackendSql</code> has similar performance than
-having all the data in memory. The performance is even better if smaller
-subsets of the data are loaded.</p>
-<div class="sourceCode" id="cb82"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_mzr</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span><span class="op">)</span>,</span>
-<span>               <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_df</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span><span class="op">)</span>,</span>
-<span>               <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_db</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span><span class="op">)</span>,</span>
+<span><span class="co">##                expr       min        lq      mean    median        uq</span></span>
+<span><span class="co">##  peaksData(sps_mzr) 272.61497 279.93407 425.31103 309.83662 317.51824</span></span>
+<span><span class="co">##   peaksData(sps_df)  82.79383  96.04517 106.78148 100.13293 128.23367</span></span>
+<span><span class="co">##   peaksData(sps_db)  52.89115  59.55610  69.26605  65.85904  85.16414</span></span>
+<span><span class="co">##         max neval</span></span>
+<span><span class="co">##  1548.86966    10</span></span>
+<span><span class="co">##   133.89797    10</span></span>
+<span><span class="co">##    86.16455    10</span></span></code></pre>
+<p>Thus, the <code>MsBackendOfflineSql</code> has similar performance
+than having all the data in memory. The performance is even better if
+smaller subsets of the data are loaded.</p>
+<div class="sourceCode" id="cb83"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html" class="external-link">microbenchmark</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_mzr</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span><span class="op">)</span>,</span>
+<span>               <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_df</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span><span class="op">)</span>,</span>
+<span>               <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/peaksData.html" class="external-link">peaksData</a></span><span class="op">(</span><span class="va">sps_db</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span><span class="op">)</span>,</span>
 <span>               times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Unit: milliseconds</span></span>
-<span><span class="co">##                      expr      min       lq      mean   median        uq</span></span>
-<span><span class="co">##  peaksData(sps_mzr[1:10]) 9.665231 9.733448 11.578363 9.886057 10.471554</span></span>
-<span><span class="co">##   peaksData(sps_df[1:10]) 4.051564 4.079166  4.226335 4.168067  4.235768</span></span>
-<span><span class="co">##   peaksData(sps_db[1:10]) 2.837020 2.988482  3.127493 3.145069  3.250351</span></span>
+<span><span class="co">##                      expr       min        lq      mean    median        uq</span></span>
+<span><span class="co">##  peaksData(sps_mzr[1:10]) 10.723417 11.186059 12.923746 11.655220 12.190033</span></span>
+<span><span class="co">##   peaksData(sps_df[1:10])  3.539373  3.737403  3.965834  3.951337  4.275036</span></span>
+<span><span class="co">##   peaksData(sps_db[1:10])  3.621757  4.224282  4.603907  4.474016  4.874835</span></span>
 <span><span class="co">##        max neval</span></span>
-<span><span class="co">##  24.067594    10</span></span>
-<span><span class="co">##   4.777438    10</span></span>
-<span><span class="co">##   3.394379    10</span></span></code></pre>
+<span><span class="co">##  23.846750    10</span></span>
+<span><span class="co">##   4.371947    10</span></span>
+<span><span class="co">##   6.294283    10</span></span></code></pre>
 <p>A real world use case for such a backend would be to store a large MS
 experiment in a central place. Data analysts could connect to that
 central database, explore and subset the data and ultimately either load
 subsets of the data into the (local) memory by changing the backend of
 the data subset to e.g. a <code>MsBackendDataFrame</code> or store the
 data subset to local files e.g. in mzML format by using the
-<code>export</code> method and the <code>MsBackendMzR</code>
+<code><a href="https://rdrr.io/pkg/Spectra/man/hidden_aliases.html" class="external-link">export()</a></code> method and the <code>MsBackendMzR</code>
 backend.</p>
 </div>
 </div>
@@ -1235,8 +1261,9 @@ <h2 id="what-else">What else?<a class="anchor" aria-label="anchor" href="#what-e
 to represent whole MS experiments (incl. <code>Spectra</code>, sample
 annotation, data quantitation, annotation files etc).</li>
 <li>
-<a href="https://github.com/tnaake/msQC" class="external-link"><code>msQC</code></a>
-(Thomas Naake): QC measures for MS data.</li>
+<a href="https://github.com/tnaake/MsQuality" class="external-link"><code>MsQuality</code></a>
+(Thomas Naake): QC measures for MS data <span class="citation">(Naake,
+Rainer, and Huber 2023)</span>.</li>
 <li>
 <a href="https://github.com/rformassspectrometry/MsBackendMsp" class="external-link"><code>MsBackendMsp</code></a>
 (Steffen Neumann): backend for files in MSP format.</li>
@@ -1246,7 +1273,7 @@ <h2 id="what-else">What else?<a class="anchor" aria-label="anchor" href="#what-e
 <div class="section level2">
 <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
 </h2>
-<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">
 <div id="ref-gattoMSnbaseEfficientElegant2020a" class="csl-entry">
 Gatto, Laurent, Sebastian Gibb, and Johannes Rainer. 2020.
 <span>“<span>MSnbase</span>, <span>Efficient</span> and <span>Elegant
@@ -1254,6 +1281,13 @@ <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="a
 <span>Raw Mass Spectrometry Data</span>.”</span> <em>Journal of Proteome
 Research</em>, September. <a href="https://doi.org/10.1021/acs.jproteome.0c00313" class="external-link">https://doi.org/10.1021/acs.jproteome.0c00313</a>.
 </div>
+<div id="ref-naake_msquality_2023" class="csl-entry">
+Naake, Thomas, Johannes Rainer, and Wolfgang Huber. 2023.
+<span>“<span>MsQuality</span>: An Interoperable Open-Source Package for
+the Calculation of Standardized Quality Metrics of Mass Spectrometry
+Data.”</span> <em>Bioinformatics (Oxford, England)</em> 39 (10):
+btad618. <a href="https://doi.org/10.1093/bioinformatics/btad618" class="external-link">https://doi.org/10.1093/bioinformatics/btad618</a>.
+</div>
 <div id="ref-rainer_modular_2022" class="csl-entry">
 Rainer, Johannes, Andrea Vicini, Liesa Salzer, Jan Stanstrup, Josep M.
 Badia, Steffen Neumann, Michael A. Stravs, et al. 2022. <span>“A
@@ -1278,13 +1312,11 @@ <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="a
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
+  <p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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@@ -53,24 +53,24 @@
 
     <div class="section ">
       <h3>All vignettes</h3>
-      <p class="section-desc"></p>
+      <div class="section-desc"></div>
 
-      <dl><dt><a href="Spectra-backends.html">Spectra: an expandable infrastructure to handle mass spectrometry data</a></dt>
+      <dl><dt><a href="analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a></dt>
         <dd>
-        </dd><dt><a href="Spectra-matching-with-MetaboAnnotation.html">MS/MS Spectra Matching with the `MetaboAnnotation` Package</a></dt>
+        </dd><dt><a href="Spectra-backends.html">Spectra: an expandable infrastructure to handle mass spectrometry data</a></dt>
         <dd>
-        </dd><dt><a href="analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a></dt>
+        </dd><dt><a href="Spectra-matching-with-MetaboAnnotation.html">MS/MS Spectra Matching with the `MetaboAnnotation` Package</a></dt>
         <dd>
       </dd></dl></div>
   </main></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
+  <p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
 </div>
 
     </footer></div>
diff --git a/authors.html b/authors.html
index acfdcfb..e9e7c90 100644
--- a/authors.html
+++ b/authors.html
@@ -1,16 +1,16 @@
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • SpectraTutorials</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="https://jorainer.github.io/SpectraTutorials/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
     
     <a class="navbar-brand me-2" href="index.html">SpectraTutorials</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -24,9 +24,9 @@
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
     <a class="dropdown-item" href="articles/Spectra-backends.html">Spectra: an expandable infrastructure to handle mass spectrometry data</a>
     <a class="dropdown-item" href="articles/Spectra-matching-with-MetaboAnnotation.html">MS/MS Spectra Matching with the `MetaboAnnotation` Package</a>
-    <a class="dropdown-item" href="articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
   </div>
 </li>
 <li class="nav-item">
@@ -80,15 +80,15 @@ <h2>Authors</h2>
       <h2 id="citation">Citation</h2>
       <p><small class="dont-index">Source: <a href="https://github.com/jorainer/SpectraTutorials/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
-      <p>Rainer J, Witting M, Gatto L, Gibb S (2023).
+      <p>Rainer J, Witting M, Gatto L, Gibb S (2024).
 <em>SpectraTutorials: Mass Spectrometry Data Analysis with Spectra</em>.
-R package version 0.9.0, <a href="https://jorainer.github.io/SpectraTutorials/">https://jorainer.github.io/SpectraTutorials/</a>. 
+R package version 1.0.0, <a href="https://jorainer.github.io/SpectraTutorials/">https://jorainer.github.io/SpectraTutorials/</a>. 
 </p>
       <pre>@Manual{,
   title = {SpectraTutorials: Mass Spectrometry Data Analysis with Spectra},
   author = {Johannes Rainer and Michael Witting and Laurent Gatto and Sebastian Gibb},
-  year = {2023},
-  note = {R package version 0.9.0},
+  year = {2024},
+  note = {R package version 1.0.0},
   url = {https://jorainer.github.io/SpectraTutorials/},
 }</pre>
     </div>
@@ -97,11 +97,11 @@ <h2 id="citation">Citation</h2>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
+  <p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
 </div>
 
     </footer></div>
diff --git a/deps/bootstrap-5.3.1/bootstrap.min.css b/deps/bootstrap-5.3.1/bootstrap.min.css
index 740f5fd..1be3b57 100644
--- a/deps/bootstrap-5.3.1/bootstrap.min.css
+++ b/deps/bootstrap-5.3.1/bootstrap.min.css
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var(--bs-navbar-hover-color);--bs-nav-link-disabled-color: var(--bs-navbar-disabled-color);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;list-style:none}.navbar-nav .nav-link.active,.navbar-nav .nav-link.show{color:var(--bs-navbar-active-color)}.navbar-nav .dropdown-menu{position:static}.navbar-text{padding-top:.5rem;padding-bottom:.5rem;color:var(--bs-navbar-color)}.navbar-text a,.navbar-text a:hover,.navbar-text a:focus{color:var(--bs-navbar-active-color)}.navbar-collapse{flex-basis:100%;-webkit-flex-basis:100%;flex-grow:1;-webkit-flex-grow:1;align-items:center;-webkit-align-items:center}.navbar-toggler{padding:var(--bs-navbar-toggler-padding-y) var(--bs-navbar-toggler-padding-x);font-size:var(--bs-navbar-toggler-font-size);line-height:1;color:var(--bs-navbar-color);background-color:transparent;border:var(--bs-border-width) solid 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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!important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme="dark"]{--bs-navbar-color: #fff;--bs-navbar-hover-color: #2c3e50;--bs-navbar-disabled-color: rgba(255,255,255,0.25);--bs-navbar-active-color: #2c3e50;--bs-navbar-brand-color: #fff;--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(255,255,255,0.1);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme="dark"] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: .5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: 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url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23121920'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #969fa8;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media (prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: .75rem;--bs-breadcrumb-padding-y: .375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: .25rem;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: rgba(0,0,0,0);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #0f7864;--bs-pagination-hover-border-color: rgba(0,0,0,0);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0f7864;--bs-pagination-active-border-color: rgba(0,0,0,0);--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: #3be6c4;--bs-pagination-disabled-border-color: rgba(0,0,0,0);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0 * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: #2c3e50;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: 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var(--bs-navbar-hover-color);--bs-nav-link-disabled-color: var(--bs-navbar-disabled-color);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;list-style:none}.navbar-nav .nav-link.active,.navbar-nav .nav-link.show{color:var(--bs-navbar-active-color)}.navbar-nav .dropdown-menu{position:static}.navbar-text{padding-top:.5rem;padding-bottom:.5rem;color:var(--bs-navbar-color)}.navbar-text a,.navbar-text a:hover,.navbar-text a:focus{color:var(--bs-navbar-active-color)}.navbar-collapse{flex-basis:100%;-webkit-flex-basis:100%;flex-grow:1;-webkit-flex-grow:1;align-items:center;-webkit-align-items:center}.navbar-toggler{padding:var(--bs-navbar-toggler-padding-y) var(--bs-navbar-toggler-padding-x);font-size:var(--bs-navbar-toggler-font-size);line-height:1;color:var(--bs-navbar-color);background-color:transparent;border:var(--bs-border-width) solid 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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!important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme="dark"]{--bs-navbar-color: #fff;--bs-navbar-hover-color: #2c3e50;--bs-navbar-disabled-color: rgba(255,255,255,0.25);--bs-navbar-active-color: #2c3e50;--bs-navbar-brand-color: #fff;--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(255,255,255,0.1);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme="dark"] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: .5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: 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url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23121920'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #969fa8;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media (prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: .75rem;--bs-breadcrumb-padding-y: .375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: .25rem;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: rgba(0,0,0,0);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #0f7864;--bs-pagination-hover-border-color: rgba(0,0,0,0);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0f7864;--bs-pagination-active-border-color: rgba(0,0,0,0);--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: #3be6c4;--bs-pagination-disabled-border-color: rgba(0,0,0,0);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0 * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: #2c3e50;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: 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#4093a8;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #4093a8;color:#fff}.bg-gradient-teal-pink{--bslib-color-fg: #fff;--bslib-color-bg: #709193;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #709193;color:#fff}.bg-gradient-teal-red{--bslib-color-fg: #fff;--bslib-color-bg: #709773;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #e74c3c var(--bg-gradient-end, 180%)) #709773;color:#fff}.bg-gradient-teal-orange{--bslib-color-fg: #fff;--bslib-color-bg: #78ab63;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #78ab63;color:#fff}.bg-gradient-teal-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #74b762;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) #74b762;color:#fff}.bg-gradient-teal-green{--bslib-color-fg: #fff;--bslib-color-bg: #1dc499;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #18bc9c var(--bg-gradient-end, 180%)) #1dc499;color:#fff}.bg-gradient-teal-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #28b5b2;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #3498db var(--bg-gradient-end, 180%)) #28b5b2;color:#fff}.bg-gradient-cyan-blue{--bslib-color-fg: #fff;--bslib-color-bg: #3174a3;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #2c3e50 var(--bg-gradient-end, 180%)) #3174a3;color:#fff}.bg-gradient-cyan-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #4862e4;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #4862e4;color:#fff}.bg-gradient-cyan-purple{--bslib-color-fg: #fff;--bslib-color-bg: #4c76d1;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #4c76d1;color:#fff}.bg-gradient-cyan-pink{--bslib-color-fg: #fff;--bslib-color-bg: #7c74bb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #7c74bb;color:#fff}.bg-gradient-cyan-red{--bslib-color-fg: #fff;--bslib-color-bg: #7c7a9b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #e74c3c var(--bg-gradient-end, 180%)) #7c7a9b;color:#fff}.bg-gradient-cyan-orange{--bslib-color-fg: #fff;--bslib-color-bg: #848e8b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #848e8b;color:#fff}.bg-gradient-cyan-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #809a8b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) #809a8b;color:#fff}.bg-gradient-cyan-green{--bslib-color-fg: #fff;--bslib-color-bg: #29a6c2;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #18bc9c var(--bg-gradient-end, 180%)) #29a6c2;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #2cacc0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #2cacc0;color:#fff}.bg-primary .navbar-nav .show>.nav-link,.bg-primary .navbar-nav .nav-link.active,.bg-primary .navbar-nav .nav-link:hover,.bg-primary .navbar-nav .nav-link:focus{color:#18bc9c !important}.nav-tabs .nav-link.active,.nav-tabs .nav-link.active:focus,.nav-tabs .nav-link.active:hover,.nav-tabs .nav-item.open .nav-link,.nav-tabs .nav-item.open .nav-link:focus,.nav-tabs .nav-item.open 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ease}.headroom--pinned{transform:translateY(0%)}.headroom--unpinned{transform:translateY(-100%)}.row>main,.row>aside{margin-top:56px}html,body{scroll-padding:56px}@media (min-width: 576px){#toc{position:sticky;top:56px;max-height:calc(100vh - 56px - 1rem);overflow-y:auto}}aside h2,aside .h2{margin-top:1.5rem;font-size:1.25rem}aside .roles{color:#4d5154}aside .list-unstyled li{margin-bottom:0.5rem}aside .dev-status .list-unstyled li{margin-bottom:0.1rem}@media (max-width: 575.98px){aside{margin:0.5rem;width:calc(100vw - 1rem);background-color:#f8f9fa;border-color:#dee2e6;border-radius:.375rem}aside h2:first-child,aside .h2:first-child{margin-top:1rem}}body{position:relative}#toc>.nav{margin-bottom:1rem}#toc>.nav a.nav-link{color:inherit;padding:0.25rem 0.5rem;margin-bottom:2px;border-radius:.375rem;border:0 solid #dee2e6}#toc>.nav a.nav-link:hover,#toc>.nav a.nav-link:focus{background-color:rgba(44,62,80,0.1);color:#000}#toc>.nav a.nav-link.active{background-color:#e9e9ea;color:#000}#toc>.nav .nav a.nav-link{margin-left:0.5rem}#toc>.nav .nav{display:none !important}#toc>.nav a.active+.nav{display:flex !important}footer{margin:1rem 0 1rem 0;padding-top:1rem;font-size:.875em;border-top:1px solid #dee2e6;background:rgba(0,0,0,0);color:#4d5154;display:flex;column-gap:1rem}@media (max-width: 575.98px){footer{flex-direction:column}}@media (min-width: 576px){footer .pkgdown-footer-right{text-align:right}}footer div{flex:1 1 auto}html,body{height:100%}body>.container{min-height:100%;display:flex;flex-direction:column}body>.container .row{flex:1 0 auto}::selection{background-color:#d5d8dc}main img{max-width:100%;height:auto}main table{display:block;overflow:auto}body{font-display:fallback}.page-header{border-bottom:1px solid #dee2e6;padding-bottom:0.5rem;margin-bottom:0.5rem;margin-top:1.5rem}dd{margin-left:1.5rem}summary{margin-bottom:0.5rem}details{margin-bottom:1rem}.html-widget{margin-bottom:1rem}a.anchor{display:none;margin-left:5px;width:Min(0.9em, 20px);height:Min(0.9em, 20px);background-image:url(../../link.svg);background-repeat:no-repeat;background-size:Min(0.9em, 20px) Min(0.9em, 20px);background-position:center center}h2:hover .anchor,.h2:hover .anchor,h3:hover .anchor,.h3:hover .anchor,h4:hover .anchor,.h4:hover .anchor,h5:hover .anchor,.h5:hover .anchor,h6:hover .anchor,.h6:hover .anchor{display:inline-block}.orcid{color:#A6CE39;margin-right:4px}.fab{font-family:"Font Awesome 5 Brands" !important}img.logo{float:right;width:100px;margin-left:30px}.template-home img.logo{width:120px}@media (max-width: 575.98px){img.logo{width:80px}}@media (min-width: 576px){.page-header{min-height:88px}.template-home .page-header{min-height:104px}}.line-block{margin-bottom:1rem}.template-reference-index dt{font-weight:normal}.template-reference-index code{word-wrap:normal}.icon{float:right}.icon img{width:40px}a[href='#main']{position:absolute;margin:4px;padding:0.75rem;background-color:#fff;text-decoration:none;z-index:2000}a.footnote-ref{cursor:pointer}.popover{width:Min(100vw, 32rem);font-size:0.9rem;box-shadow:4px 4px 8px rgba(0,0,0,0.3)}.popover-body{padding:0.75rem}.popover-body p:last-child{margin-bottom:0}.tab-content{padding:1rem}.tabset-pills .tab-content{border:solid 1px #e5e5e5}.tab-content{display:flex}.tab-content>.tab-pane{display:block;visibility:hidden;margin-right:-100%;width:100%}.tab-content>.active{visibility:visible}div.csl-entry{clear:both}.hanging-indent div.csl-entry{margin-left:2em;text-indent:-2em}div.csl-left-margin{min-width:2em;float:left}div.csl-right-inline{margin-left:2em;padding-left:1em}div.csl-indent{margin-left:2em}pre,pre 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diff --git a/deps/bootstrap-5.3.1/font.css b/deps/bootstrap-5.3.1/font.css
index 409df22..02b33a1 100644
--- a/deps/bootstrap-5.3.1/font.css
+++ b/deps/bootstrap-5.3.1/font.css
@@ -1,21 +1,54 @@
+/* latin-ext */
 @font-face {
   font-family: 'Lato';
   font-style: italic;
   font-weight: 400;
   font-display: swap;
-  src: url(fonts/S6u8w4BMUTPHjxswWA.woff) format('woff');
+  src: url(fonts/S6u8w4BMUTPHjxsAUi-qJCY.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
 }
+/* latin */
+@font-face {
+  font-family: 'Lato';
+  font-style: italic;
+  font-weight: 400;
+  font-display: swap;
+  src: url(fonts/S6u8w4BMUTPHjxsAXC-q.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(fonts/S6uyw4BMUTPHjxAwXjeu.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
 @font-face {
   font-family: 'Lato';
   font-style: normal;
   font-weight: 400;
   font-display: swap;
-  src: url(fonts/S6uyw4BMUTPHvxo.woff) format('woff');
+  src: url(fonts/S6uyw4BMUTPHjx4wXg.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-weight: 700;
+  font-display: swap;
+  src: url(fonts/S6u9w4BMUTPHh6UVSwaPGR_p.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
 }
+/* latin */
 @font-face {
   font-family: 'Lato';
   font-style: normal;
   font-weight: 700;
   font-display: swap;
-  src: url(fonts/S6u9w4BMUTPHh6UVeww.woff) format('woff');
+  src: url(fonts/S6u9w4BMUTPHh6UVSwiPGQ.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
 }
diff --git a/deps/bootstrap-5.3.1/fonts/07d40e985ad7c747025dabb9f22142c4.woff2 b/deps/bootstrap-5.3.1/fonts/07d40e985ad7c747025dabb9f22142c4.woff2
new file mode 100644
index 0000000..a45f477
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diff --git a/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyC0ITw.woff2 b/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyC0ITw.woff2
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diff --git a/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCAIT5lu.woff2 b/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCAIT5lu.woff2
new file mode 100644
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diff --git a/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCIIT5lu.woff2 b/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCIIT5lu.woff2
new file mode 100644
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diff --git a/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCMIT5lu.woff2 b/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCMIT5lu.woff2
new file mode 100644
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diff --git a/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCkIT5lu.woff2 b/deps/bootstrap-5.3.1/fonts/1Ptug8zYS_SKggPNyCkIT5lu.woff2
new file mode 100644
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diff --git a/deps/bootstrap-5.3.1/fonts/1Ptxg8zYS_SKggPN4iEgvnHyvveLxVs9pbCIPrc.woff b/deps/bootstrap-5.3.1/fonts/1Ptxg8zYS_SKggPN4iEgvnHyvveLxVs9pbCIPrc.woff
deleted file mode 100644
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diff --git a/deps/bootstrap-5.3.1/fonts/1Ptxg8zYS_SKggPN4iEgvnHyvveLxVvaorCIPrc.woff b/deps/bootstrap-5.3.1/fonts/1Ptxg8zYS_SKggPN4iEgvnHyvveLxVvaorCIPrc.woff
deleted file mode 100644
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diff --git a/index.html b/index.html
index adf40ac..a1e092f 100644
--- a/index.html
+++ b/index.html
@@ -5,9 +5,7 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-<meta name="description" content="These workshops and tutorials provide use cases and examples for
-    mass spectrometry data handling and analysis using the Spectra Bioconductor
-    package and how these can be included into other R-based analyis workflows.">
+<meta name="description" content="These workshops and tutorials provide use cases and examples for mass spectrometry data handling and analysis using the Spectra Bioconductor package and how these can be included into other R-based analyis workflows.">
 <title>Mass Spectrometry Data Analysis with Spectra • SpectraTutorials</title>
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 <link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png">
@@ -19,10 +17,8 @@
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-<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Mass Spectrometry Data Analysis with Spectra">
-<meta property="og:description" content="These workshops and tutorials provide use cases and examples for
-    mass spectrometry data handling and analysis using the Spectra Bioconductor
-    package and how these can be included into other R-based analyis workflows.">
+<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Mass Spectrometry Data Analysis with Spectra">
+<meta property="og:description" content="These workshops and tutorials provide use cases and examples for mass spectrometry data handling and analysis using the Spectra Bioconductor package and how these can be included into other R-based analyis workflows.">
 <meta property="og:image" content="https://jorainer.github.io/SpectraTutorials/logo.png">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
@@ -33,11 +29,11 @@
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
     
     <a class="navbar-brand me-2" href="index.html">SpectraTutorials</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -52,9 +48,9 @@
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
     <a class="dropdown-item" href="articles/Spectra-backends.html">Spectra: an expandable infrastructure to handle mass spectrometry data</a>
     <a class="dropdown-item" href="articles/Spectra-matching-with-MetaboAnnotation.html">MS/MS Spectra Matching with the `MetaboAnnotation` Package</a>
-    <a class="dropdown-item" href="articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
   </div>
 </li>
 <li class="nav-item">
@@ -93,16 +89,16 @@
 <div class="section level2">
 <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
 </h2>
-<p>For on-line code evaluation the workshops can also be run using a self-contained docker image with all R packages and a server version of RStudio (Posit) pre-installed. As an alternative, rendered versions of all workshops are available at <a href="https://jorainer.github.io/SpectraTutorials">SpectraTutorials</a> (navigate to the <em>Articles</em> drop-down menu and select the workshop of interest).</p>
+<p>For on-line code evaluation the workshops can also be run using a self-contained docker image with all R packages and a server version of RStudio (Posit) pre-installed. The current docker container bases (and includes) the official docker container for <strong>Bioconductor release 3.19</strong>. As an alternative, rendered versions of all workshops are available at <a href="https://jorainer.github.io/SpectraTutorials">SpectraTutorials</a> (navigate to the <em>Articles</em> drop-down menu and select the workshop of interest).</p>
 <p>For installation:</p>
 <ul>
-<li><p>Get the <a href="https://hub.docker.com/r/jorainer/spectra_tutorials" class="external-link">docker image</a> of this tutorial with <code>docker pull jorainer/spectra_tutorials:latest</code>.</p></li>
+<li><p>Get the <a href="https://hub.docker.com/r/jorainer/spectra_tutorials" class="external-link">docker image</a> of this tutorial with <code>docker pull jorainer/spectra_tutorials:RELEASE_3_19</code>.</p></li>
 <li>
 <p>Start docker using</p>
 <pre><code>docker run \
     -e PASSWORD=bioc \
     -p 8787:8787 \
-    jorainer/spectra_tutorials:latest</code></pre>
+    jorainer/spectra_tutorials:RELEASE_3_19</code></pre>
 </li>
 <li><p>Enter <code>http://localhost:8787</code> in a web browser and log in with username <code>rstudio</code> and password <code>bioc</code>.</p></li>
 <li><p>In the RStudio server version: open any of the R-markdown (<em>.Rmd</em>) files in the <em>vignettes</em> folder and evaluate the R code blocks.</p></li>
@@ -300,13 +296,11 @@ <h2 data-toc-skip>Developers</h2>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
+  <p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
 </div>
 
     </footer>
diff --git a/news/index.html b/news/index.html
index 86bcb5c..8fa8d46 100644
--- a/news/index.html
+++ b/news/index.html
@@ -1,16 +1,16 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • SpectraTutorials</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><meta property="og:image" content="https://jorainer.github.io/SpectraTutorials/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • SpectraTutorials</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><meta property="og:image" content="https://jorainer.github.io/SpectraTutorials/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
     
     <a class="navbar-brand me-2" href="../index.html">SpectraTutorials</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.9.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -24,9 +24,9 @@
 <li class="nav-item dropdown">
   <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
   <div class="dropdown-menu" aria-labelledby="dropdown-articles">
+    <a class="dropdown-item" href="../articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
     <a class="dropdown-item" href="../articles/Spectra-backends.html">Spectra: an expandable infrastructure to handle mass spectrometry data</a>
     <a class="dropdown-item" href="../articles/Spectra-matching-with-MetaboAnnotation.html">MS/MS Spectra Matching with the `MetaboAnnotation` Package</a>
-    <a class="dropdown-item" href="../articles/analyzing-MS-data-from-different-sources-with-Spectra.html">Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package</a>
   </div>
 </li>
 <li class="active nav-item">
@@ -52,6 +52,10 @@
       <small>Source: <a href="https://github.com/jorainer/SpectraTutorials/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="1.0" id="version-10">Version 1.0<a class="anchor" aria-label="anchor" href="#version-10"></a></h2>
+<ul><li>Use R 4.4 and Bioconductor 3.19.</li>
+</ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.9" id="version-09">Version 0.9<a class="anchor" aria-label="anchor" href="#version-09"></a></h2>
 <div class="section level3">
@@ -144,11 +148,11 @@ <h3 id="version-0-3">Version 0.3.2<a class="anchor" aria-label="anchor" href="#v
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
+  <p>Developed by Johannes Rainer, Michael Witting, Laurent Gatto, Sebastian Gibb.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
 </div>
 
     </footer></div>
diff --git a/pkgdown.js b/pkgdown.js
index 5fccd9c..9bd6621 100644
--- a/pkgdown.js
+++ b/pkgdown.js
@@ -30,10 +30,10 @@
   /* Clipboard --------------------------*/
 
   function changeTooltipMessage(element, msg) {
-    var tooltipOriginalTitle=element.getAttribute('data-original-title');
-    element.setAttribute('data-original-title', msg);
+    var tooltipOriginalTitle=element.getAttribute('data-bs-original-title');
+    element.setAttribute('data-bs-original-title', msg);
     $(element).tooltip('show');
-    element.setAttribute('data-original-title', tooltipOriginalTitle);
+    element.setAttribute('data-bs-original-title', tooltipOriginalTitle);
   }
 
   if(ClipboardJS.isSupported()) {
@@ -60,7 +60,7 @@
         e.clearSelection();
       });
 
-      clipboard.on('error', function() {
+      clipboard.on('error', function(e) {
         changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy');
       });
 
diff --git a/pkgdown.yml b/pkgdown.yml
index 6433a5a..a441e58 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -1,11 +1,11 @@
-pandoc: 3.1.1
-pkgdown: 2.0.7
+pandoc: 3.1.13
+pkgdown: 2.0.9
 pkgdown_sha: ~
 articles:
+  analyzing-MS-data-from-different-sources-with-Spectra: analyzing-MS-data-from-different-sources-with-Spectra.html
   Spectra-backends: Spectra-backends.html
   Spectra-matching-with-MetaboAnnotation: Spectra-matching-with-MetaboAnnotation.html
-  analyzing-MS-data-from-different-sources-with-Spectra: analyzing-MS-data-from-different-sources-with-Spectra.html
-last_built: 2023-12-05T09:13Z
+last_built: 2024-05-17T14:09Z
 urls:
   reference: https://jorainer.github.io/SpectraTutorials/reference
   article: https://jorainer.github.io/SpectraTutorials/articles
diff --git a/reference/index.html b/reference/index.html
index 2fc6419..2264a13 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -1,16 +1,16 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title> • SpectraTutorials</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content=""><meta property="og:image" content="https://jorainer.github.io/SpectraTutorials/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title> • SpectraTutorials</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content=""><meta property="og:image" content="https://jorainer.github.io/SpectraTutorials/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <![endif]--></head><body>
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-[{"path":"https://jorainer.github.io/SpectraTutorials/Abstract-EuroBioc2020.html","id":null,"dir":"","previous_headings":"","what":"Abstract","title":"Abstract","text":"Mass spectrometry (MS) data key technology modern proteomics metabolomics experiments. Due continuous improvements MS instrumentation, generated data can easily become large. Also, different additional resources MS data exist, spectra libraries databases, specific file formats sometimes support manipulations original data. Learning experiences r Biocpkg(\"MSnbase\") Bioconductor package developed novel infrastructure handle MS spectral data R, r Biocpkg(\"Spectra\") package. package implements clear separation user functionality code provide, store import mass spectrometry data. Different backends can hence used enable access data various data resources designed specifically large MS data sets. Data manipulations default directly applied data cached lazy processing queue allows analyses also read-data representations. workshop shows expandability new infrastructure enable seamless integration analysis MS data variety input formats illustrated simple matching experimental MS2 spectra public spectral database export data format commonly used exchange MS2 data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/Abstract-EuroBioc2022.html","id":null,"dir":"","previous_headings":"","what":"Abstract","title":"Abstract","text":"Mass spectrometry (MS) data key technology modern proteomics metabolomics experiments. Due continuous improvements MS instrumentation, generated data can easily become large. Also, different additional resources MS data exist, spectra libraries databases, specific file formats sometimes support manipulations original data. Learning experiences r Biocpkg(\"MSnbase\") Bioconductor package developed novel infrastructure handle MS spectral data R, r Biocpkg(\"Spectra\") package. package implements clear separation user functionality code provide, store import mass spectrometry data. Different backends can hence used enable access data various data resources. Depending backend, data representations can memory, disk, local remote. Data manipulations default directly applied data cached lazy processing queue allows analyses also read-data representations. workshop shows new infrastructure can used integrate data variety different input sources simple example fragment (MS2) spectra experiment matched reference MS2 spectra public spectral library. Results finally exported format commonly used exchange MS2 data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/Abstract-EuroBioc2023.html","id":null,"dir":"","previous_headings":"","what":"Spectra: an expandable infrastructure to handle mass spectrometry data","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Mass spectrometry (MS) data key technology modern metabolomics proteomics experiments. Continuous improvements MS instrumentation, larger experiments new technological developments lead ever growing data sizes increased number available variables making standard -memory data handling processing difficult. Spectra package provides modern infrastructure MS data handling specifically designed enable extension additional data resources alternative data representations. can realized extending virtual MsBackend class related methods. Implementations MsBackend classes can tailored specific needs, low memory footprint, fast processing, remote data access, also support specific additional data types variables. Importantly, data processing Spectra objects independent backend use due lazy evaluation mechanism caches data manipulations internally. workshop discusses different available data representations MS data along properties, advantages performances. addition, Spectra’s concept lazy evaluation data manipulations presented, well simple caching mechanism data modifications. Finally, explains new MsBackend instances can implemented tested ensure compliance. Describe: - Concept backends. - Loading MS data. - Backends, -disk, -memory. - Using MsBackendSql store data. - Use MsBackendMassbankSql access data: parallel processing possible. - Different properties backends. - Lazy processing caching.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"abstract","dir":"Articles","previous_headings":"","what":"Abstract","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Mass spectrometry (MS) data key technology modern metabolomics proteomics experiments. Continuous improvements MS instrumentation, larger experiments new technological developments lead ever growing data sizes increased number available variables making standard -memory data handling processing difficult. Spectra package provides modern infrastructure MS data handling specifically designed enable extension additional data resources alternative data representations. can realized extending virtual MsBackend class related methods. Implementations MsBackend classes can tailored specific needs, low memory footprint, fast processing, remote data access, also support specific additional data types variables. Importantly, data processing Spectra objects independent backend use due lazy evaluation mechanism caches data manipulations internally. workshop discusses different available data representations MS data along properties, advantages performances. addition, Spectra’s concept lazy evaluation data manipulations presented, well simple caching mechanism data modifications. Finally, explains new MsBackend instances can implemented tested ensure compliance.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"workshop/tutorial assumes readers familiar mass spectrometry data. See LC-MS/MS nutshell section Seamless Integration Mass Spectrometry Data Different Sources vignette package general introduction MS.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"pre-requisites","dir":"Articles","previous_headings":"Introduction","what":"Pre-requisites","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Basic familiarity R Bioconductor. Basic understanding Mass Spectrometry (MS) data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"installation","dir":"Articles","previous_headings":"Introduction","what":"Installation","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Get docker image tutorial docker pull jorainer/spectra_tutorials:latest. Start docker using Enter http://localhost:8787 web browser log username rstudio password bioc. Open R-markdown file (vignettes/Spectra-backends.Rmd) RStudio server version web browser evaluate R code blocks.","code":"docker run \\     -e PASSWORD=bioc \\     -p 8787:8787 \\     jorainer/spectra_tutorials:latest"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"rbioconductor-packages-used","dir":"Articles","previous_headings":"Introduction","what":"R/Bioconductor packages used","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Spectra MsBackendSql","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"workshop-goals-and-objectives","dir":"Articles","previous_headings":"Introduction","what":"Workshop goals and objectives","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"technical demonstration internals Spectra package design MS infrastructure. ’re demonstrating use cases analysis workflows .","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"learning-goals","dir":"Articles","previous_headings":"Introduction > Workshop goals and objectives","what":"Learning goals","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Understand MS data handled Spectra. Understand differences properties different MsBackend implementations.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"learning-objectives","dir":"Articles","previous_headings":"Introduction > Workshop goals and objectives","what":"Learning objectives","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Learn MS data handled Spectra. Understand data representations/backends fit purpose/use case. Insights internals Spectra MS infrastructure facilitate implementation backend(s).","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"the-spectra-package","dir":"Articles","previous_headings":"Workshop","what":"The Spectra package","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Purpose: provide expandable, well tested user-friendly infrastructure mass spectrometry (MS) data. Design: separation code representing main user interface code provide, store read MS data. Spectra: main interface end user. MsBackend: defines data stored managed. user need care data stored. functionality can applied MS data, regardless data stored. enables specialized data storage/representation options: remote data, low memory footprint, high performance.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"creating-and-using-spectra-objects","dir":"Articles","previous_headings":"Workshop","what":"Creating and using Spectra objects","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"MS data consists duplets mass--charge (m/z) intensity values along potential additional information, MS level, retention time, polarity etc. simplest form, MS data consists thus two (aligned) numeric vectors m/z intensity values e.g. data.frame containing potential additional annotations MS spectrum. Spectra terminology, \"mz\" \"intensity\" called peak variables (provide information individual mass peaks), annotations spectra variables (usually single value per spectrum). define m/z intensity values mass spectrum well data.frame additional spectra variables. basic representation MS data R. obvious reasons however better store data single container keeping separate, unrelated, variables. thus create Spectra object MS data. Spectra objects can created Spectra constructor function accepts (among possibilities) data.frame full MS data input. row data.frame expected contain data one spectrum. therefore need add m/z intensity values list numerical vectors data frame. now created Spectra object representing toy MS data. Individual spectra variables can accessed either $ name spectra variable, one dedicated accessor functions. Also, provided spectra variables \"msLevel\" \"polarity\" input data frame, variables available default Spectra object. called core spectra variables can always extracted Spectra object, even defined (case missing values returned). Spectra variables available Spectra object can listed spectraVariables function: ’ve now got Spectra object representing single MS spectrum - , name class implies, actually designed represent data multiple mass spectra (possibly whole experiment). thus define data.frame, time 3 rows, create Spectra object . Also, define additional spectra variables providing name ID compounds MS2 (fragment) spectrum represents. Now Spectra object representing 3 mass spectra set spectra peak variables. Spectra organized linearly, .e., one . Thus, Spectra length 3 element one spectrum. Subsetting works R object: Spectra behaves similar data.frame elements (rows) individual spectra columns spectra (peaks) variables, can accessed $ operator. Similar data.frame can also add new spectra variables (columns) using $<-. full peak data can extracted peaksData function, returns list two-dimensional arrays values peak variables. list element represents peak data one spectrum, stored e.g. matrix columns peak variables rows respective values peak. number rows peak variable arrays depends number mass peaks spectrum. number can extracted using lengths: next extract peaks’ data Spectra subset data second spectrum. Finally, can also visualize peak data spectrum Spectra.  plots also created manually m/z intensity values, built-functions cases efficient.  Experimental MS data generally (ideally) stored files mzML, mzXML CDF format. open file formats can read software. Bioconductor, mzR package allows read/write data /formats. Also Spectra allows represent data raw data files. illustrate create Spectra object two mzML files provided Bioconductor’s msdata package. thus access raw experimental MS data Spectra object. contrast first example , used Spectra constructor providing character vector file names specified source = MsBackendMzR(). anticipates concept backends Spectra package: source parameter Spectra call defines backend use import (represent) data, example MsBackendMzR enables import data mzML files. Like first example, can subset object access spectra variables. Note however Spectra object many spectra variables available (depending provided raw data files). can visualize data.","code":"#' Define simple MS data mz <- c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,         111.0551, 123.0429, 138.0662, 195.0876) int <- c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994)  sv <- data.frame(msLevel = 1L, polarity = 1L) library(Spectra)  #' wrap m/z and intensities into a list and add them to the data.frame sv$mz <- list(mz) sv$intensity <- list(int)  #' Create a `Spectra` object from the data. s <- Spectra(sv) s ## MSn data (Spectra) with 1 spectra in a MsBackendMemory backend: ##     msLevel     rtime scanIndex ##   <integer> <numeric> <integer> ## 1         1        NA        NA ##  ... 16 more variables/columns. #' Access the MS level spectra variable s$msLevel ## [1] 1 msLevel(s) ## [1] 1 #' List all available spectra variables spectraVariables(s) ##  [1] \"msLevel\"                 \"rtime\"                   ##  [3] \"acquisitionNum\"          \"scanIndex\"               ##  [5] \"dataStorage\"             \"dataOrigin\"              ##  [7] \"centroided\"              \"smoothed\"                ##  [9] \"polarity\"                \"precScanNum\"             ## [11] \"precursorMz\"             \"precursorIntensity\"      ## [13] \"precursorCharge\"         \"collisionEnergy\"         ## [15] \"isolationWindowLowerMz\"  \"isolationWindowTargetMz\" ## [17] \"isolationWindowUpperMz\" #' Extract the retention time spectra variable s$rtime ## [1] NA #' Define spectra variables for 3 MS spectra. sv <- data.frame(     msLevel = c(2L, 2L, 2L),     polarity = c(1L, 1L, 1L),     id = c(\"HMDB0000001\", \"HMDB0000001\", \"HMDB0001847\"),     name = c(\"1-Methylhistidine\", \"1-Methylhistidine\", \"Caffeine\"))  #' Assign m/z and intensity values. sv$mz <- list(     c(109.2, 124.2, 124.5, 170.16, 170.52),     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,       111.0551, 123.0429, 138.0662, 195.0876)) sv$intensity <- list(     c(3.407, 47.494, 3.094, 100.0, 13.240),     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))  #' Create a Spectra from this data. s <- Spectra(sv) s ## MSn data (Spectra) with 3 spectra in a MsBackendMemory backend: ##     msLevel     rtime scanIndex ##   <integer> <numeric> <integer> ## 1         2        NA        NA ## 2         2        NA        NA ## 3         2        NA        NA ##  ... 18 more variables/columns. #' List available spectra and peaks variables. spectraVariables(s) ##  [1] \"msLevel\"                 \"rtime\"                   ##  [3] \"acquisitionNum\"          \"scanIndex\"               ##  [5] \"dataStorage\"             \"dataOrigin\"              ##  [7] \"centroided\"              \"smoothed\"                ##  [9] \"polarity\"                \"precScanNum\"             ## [11] \"precursorMz\"             \"precursorIntensity\"      ## [13] \"precursorCharge\"         \"collisionEnergy\"         ## [15] \"isolationWindowLowerMz\"  \"isolationWindowTargetMz\" ## [17] \"isolationWindowUpperMz\"  \"id\"                      ## [19] \"name\" peaksVariables(s) ## [1] \"mz\"        \"intensity\" length(s) ## [1] 3 #' Extract the 2nd spectrum s[2] ## MSn data (Spectra) with 1 spectra in a MsBackendMemory backend: ##     msLevel     rtime scanIndex ##   <integer> <numeric> <integer> ## 1         2        NA        NA ##  ... 18 more variables/columns. s$msLevel ## [1] 2 2 2 #' Add a new spectra variable. s$new_variable <- c(\"a\", \"b\", \"c\") spectraVariables(s) ##  [1] \"msLevel\"                 \"rtime\"                   ##  [3] \"acquisitionNum\"          \"scanIndex\"               ##  [5] \"dataStorage\"             \"dataOrigin\"              ##  [7] \"centroided\"              \"smoothed\"                ##  [9] \"polarity\"                \"precScanNum\"             ## [11] \"precursorMz\"             \"precursorIntensity\"      ## [13] \"precursorCharge\"         \"collisionEnergy\"         ## [15] \"isolationWindowLowerMz\"  \"isolationWindowTargetMz\" ## [17] \"isolationWindowUpperMz\"  \"id\"                      ## [19] \"name\"                    \"new_variable\" #' Get the number of peaks per spectrum. lengths(s) ## [1] 5 7 9 #' Extract the peaks matrix of the second spectrum peaksData(s)[[2]] ##          mz intensity ## [1,]  83.10     6.685 ## [2,]  96.12     4.381 ## [3,]  97.14     3.022 ## [4,] 109.14    16.708 ## [5,] 124.08   100.000 ## [6,] 125.10     4.565 ## [7,] 170.16    40.643 #' Plot the second spectrum. plotSpectra(s[2]) plot(mz(s)[[2]], intensity(s)[[2]], type = \"h\",      xlab = \"m/z\", ylab = \"intensity\") #' Import MS data from 2 mzML files. fls <- dir(system.file(\"sciex\", package = \"msdata\"), full.names = TRUE) s2 <- Spectra(fls, source = MsBackendMzR()) s2 ## MSn data (Spectra) with 1862 spectra in a MsBackendMzR backend: ##        msLevel     rtime scanIndex ##      <integer> <numeric> <integer> ## 1            1     0.280         1 ## 2            1     0.559         2 ## 3            1     0.838         3 ## 4            1     1.117         4 ## 5            1     1.396         5 ## ...        ...       ...       ... ## 1858         1   258.636       927 ## 1859         1   258.915       928 ## 1860         1   259.194       929 ## 1861         1   259.473       930 ## 1862         1   259.752       931 ##  ... 33 more variables/columns. ##  ## file(s): ## 20171016_POOL_POS_1_105-134.mzML ## 20171016_POOL_POS_3_105-134.mzML #' Available spectra variables spectraVariables(s2) ##  [1] \"msLevel\"                  \"rtime\"                    ##  [3] \"acquisitionNum\"           \"scanIndex\"                ##  [5] \"dataStorage\"              \"dataOrigin\"               ##  [7] \"centroided\"               \"smoothed\"                 ##  [9] \"polarity\"                 \"precScanNum\"              ## [11] \"precursorMz\"              \"precursorIntensity\"       ## [13] \"precursorCharge\"          \"collisionEnergy\"          ## [15] \"isolationWindowLowerMz\"   \"isolationWindowTargetMz\"  ## [17] \"isolationWindowUpperMz\"   \"peaksCount\"               ## [19] \"totIonCurrent\"            \"basePeakMZ\"               ## [21] \"basePeakIntensity\"        \"ionisationEnergy\"         ## [23] \"lowMZ\"                    \"highMZ\"                   ## [25] \"mergedScan\"               \"mergedResultScanNum\"      ## [27] \"mergedResultStartScanNum\" \"mergedResultEndScanNum\"   ## [29] \"injectionTime\"            \"filterString\"             ## [31] \"spectrumId\"               \"ionMobilityDriftTime\"     ## [33] \"scanWindowLowerLimit\"     \"scanWindowUpperLimit\" #' Access the MS levels for the first 6 spectra s2$msLevel |> head() ## [1] 1 1 1 1 1 1 #' Plot the 4th spectrum. plotSpectra(s2[4])"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"use-of-different-backends-with-spectra-why-and-how","dir":"Articles","previous_headings":"Workshop","what":"Use of different backends with Spectra, why and how?","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Spectra objects behave way, .e. data can accessed fashion, actually use different backends thus data representations: first example Spectra uses MsBackendMemory backend , name tells, keeps MS data memory (within backend object). contrast, second Spectra represents experimental MS data two mzML files uses MsBackendMzR backend. backend loads general spectra data (variables) memory imports peak data -demand original data files needed/requested (similar -disk mode discussed (Gatto, Gibb, Rainer 2020)). advantage offline storage mode lower memory footprint enables also analysis large data experiments standard computers. different backends? Reason 1: support additional file formats. backend class defines functionality import/export data /specific file formats. Spectra class stays data storage agnostic e.g. import/export routines need implemented class. Support additional file formats can implemented independently Spectra package can contributed different developers. Examples: MsBackendMgf (defined MsBackendMgf) adds support MS files MGF file format. MsBackendMsp (defined MsBackendMsp adds support MSP files. MsBackendRawFileReader (defined MsBackendRawFileReader) adds support MS data files Thermo Fisher Scientific’s raw data file format. import data files certain file format, respective backend able handle data needs specified parameter source Spectra constructor call: Reason 2: support different implementations store represent data. includes data stored stored. Specialized backends can defined , example, optimized high performance low memory demand. Examples: MsBackendMemory (defined Spectra): keeps MS data memory optimized fast efficient access peaks data matrices (.e., m/z intensity values spectra). MsBackendMzR (defined Spectra): keeps spectra variables memory retrieves peaks data --fly original data files upon request. guarantees lower memory footprint enables also analysis large MS experiments. MsBackendSql (defined MsBackendSql package): MS data stored SQL database. minimal memory requirements data (whether spectra peaks variables) need retrieved database. Depending SQL database system used, backend also allow remote data access. evaluate illustrate properties different backends create Spectra objects data, using different backends. use setBackend function change backend Spectra object. call loaded MS data original data files memory. third option next store full MS data SQL database using/changing MsBackendOfflineSql backend defined MsBackendSql package. setBackend call need provide connection information database contain data. includes database driver (parameter drv, depending database system), database name (parameter dbname) well eventual additional connection information like host, username, port password. parameters required depends SQL database used hence driver (see also ?dbConnect information parameters). example store data SQLite database. thus set drv = SQLite() provide parameter dbname name SQLite database file (yet exist). addition, importantly, disable parallel processing BPPARAM = SerialParam() since SQL databases don’t support parallel data import. Note: efficient way import MS data data files SQL database createMsBackendSqlDatabase MsBackendSql package. Also, larger data sets suggested use advanced powerful SQL database systems (e.g. MySQL/MariaDB SQL databases). now 3 Spectra objects representing MS data, using different backends. first comparison evaluate size memory: expected, size Spectra object MsBackendMemory backend largest three. Since MsBackendOfflineSql backend keeps primary keys spectra memory ’s memory requirements particularly small thus ideal represent even large MS experiments. next evaluate performance extract spectra variables. compare time needed extract retention times 3 Spectra objects using microbenchmark function. register(SerialParam()) globally disable parallel processing Spectra ensure results independent . Highest performance can seen Spectra object MsBackendMemory backend, followed Spectra object MsBackendMzR backend, extraction spectra variables slowest Spectra MsBackendOfflineSql. , expected MsBackendOfflineSql needs retrieve data database two backends keep retention times spectra (along general spectra metadata) memory. next also evaluate performance extract peaks data, .e. individual m/z intensity values spectra two files. MsBackendMemory outperforms two backends also comparison. MsBackendMzR MsBackendOfflineSql need import data, MsBackendMzR original mzML files MsBackendOfflineSql SQL database (present data efficient). last evaluate performance subset 3 Spectra objects 10 random spectra. , MsBackendOfflineSql clear advantage two backends, needs subset integer vector primary keys (data keeps memory), MsBackendMemory needs subset full data, MsBackendMzR data frame spectra variables.","code":"class(s@backend) ## [1] \"MsBackendMemory\" ## attr(,\"package\") ## [1] \"Spectra\" class(s2@backend) ## [1] \"MsBackendMzR\" ## attr(,\"package\") ## [1] \"Spectra\" #' Import MS data from mzML files s_mzr <- Spectra(fls, source = MsBackendMzR()) #' Change the backend to MsBackendMemory s_mem <- setBackend(s_mzr, MsBackendMemory()) #' Change the backend to a SQL representation of the data. library(MsBackendSql) library(RSQLite) s_db <- setBackend(s_mzr, MsBackendOfflineSql(), drv = SQLite(),                    dbname = \"ms_backend_sql_test.sqlite\",                    BPPARAM = SerialParam()) #' Compare memory footprint. print(object.size(s_mem), units = \"MB\") ## 53.2 Mb print(object.size(s_mzr), units = \"MB\") ## 0.4 Mb print(object.size(s_db), units = \"MB\") ## 0.1 Mb #' Compare performance to extract the retention times. register(SerialParam()) library(microbenchmark) microbenchmark(     rtime(s_mem),     rtime(s_mzr),     rtime(s_db),     times = 13 ) ## Unit: microseconds ##          expr      min       lq       mean   median       uq       max neval ##  rtime(s_mem)   12.292   13.395   19.64815   19.877   26.169    31.269    13 ##  rtime(s_mzr)  385.288  407.640  451.69762  419.863  445.912   836.380    13 ##   rtime(s_db) 6983.250 7037.401 8782.54131 7179.236 7268.913 27920.037    13 #' Compare performance to extract the peaks data. microbenchmark(     peaksData(s_mem),     peaksData(s_mzr),     peaksData(s_db),     times = 7 ) ## Unit: microseconds ##              expr        min          lq        mean     median          uq ##  peaksData(s_mem)    380.319    485.0095    704.9917    780.716    806.9495 ##  peaksData(s_mzr) 487238.872 492587.2645 524201.2064 496422.977 500894.0630 ##   peaksData(s_db)  89571.778  97788.3475 185583.6216 102420.344 210867.2850 ##         max neval ##    1189.989     7 ##  698783.941     7 ##  489781.964     7 #' Compare performance to subset the Spectra. idx <- sample(seq_along(s_mem), 10) microbenchmark(     s_mem[idx],     s_mzr[idx],     s_db[idx] ) ## Unit: microseconds ##        expr     min       lq     mean  median       uq      max neval ##  s_mem[idx] 230.711 238.1690 246.4851 245.468 251.5495  314.927   100 ##  s_mzr[idx] 642.047 664.8100 683.6366 676.962 690.1570 1084.352   100 ##   s_db[idx] 133.279 141.9445 163.7531 147.515 153.4465 1633.125   100"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"performance-considerations-and-tweaks","dir":"Articles","previous_headings":"Workshop > Use of different backends with Spectra, why and how?","what":"Performance considerations and tweaks","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Keeping data memory (e.g. using MsBackendMemory) obvious performance advantages, might possible MS experiments. -disk backends MsBackendMzR MsBackendOfflineSql allow, due lower memory footprint, analyze also large data sets. optimized build-option Spectra methods load process MS data small chunks contrast loading processing full data whole. performance offline backends MsBackendMzR MsBackendSql/MsBackendOfflineSql depend also /O capabilities hard disks containing data files , latter, addition configuration SQL database system used.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"data-manipulation-and-the-lazy-evaluation-queue","dir":"Articles","previous_headings":"Workshop","what":"Data manipulation and the lazy evaluation queue","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"data representations might, design way data handled stored, allow changing existing adding new values. MsBackendMzR backend example retrieves peaks data directly raw data files don’t want manipulation m/z intensity values directly written back original data files. Also backends, specifically provide access spectral reference databases (MsBackendMassbankSql MsBackendMassbank package MsBackendCompDb CompoundDb package), supposed allow changes information stored databases. Data manipulations however integral part data analysis thus need backends mechanism allows changes data without propagating original data resource. backend classes caching mechanism implemented enables adding, changing, deleting spectra variables. illustrate add new spectra variable \"new_variable\" 3 Spectra objects. new spectra variable now added 3 Spectra objects: MsBackendMemory stores spectra variables data.frame within object. new spectra variable thus simply added new column data.frame. Warning: NEVER access manipulate slots backend class directly like can easily result data corruption. Similarly, also MsBackendMzR stores data spectra variables (peaks variables!) within DataFrame inside object. Adding new spectra variable thus also added new column DataFrame. Note: use DataFrame instead data.frame MsBackendMzR explains performance differences subset access spectra variables ’ve seen backend MsBackendMemory . MsBackendOfflineSql implements support changes spectra variables differently: object contains vector available database column names addition (initially empty) data.frame cache changes spectra variables. spectra variable deleted, respective column name removed character vector available column names. new spectra variable added (like example ) values existing spectra variable changed, spectra variable, respectively values, stored (new column) caching data frame: Actually, want change data directly database? fairly simple add new column database table change values . actually two reasons : firstly don’t want change raw data might stored database (e.g. database contains reference MS2 spectra, case Spectra MassBank data) secondly, want avoid following situation: imagine create copy s_db variable change retention times variable: Writing changes back database also change retention time values original s_db variable obvious reasons desired. Note can also used MsBackendOfflineSql backends cache spectra variables memory faster access. Note: MsBackendCached class Spectra package provides internal data.frame-based caching mechanism described . MsBackendOfflineSql class directly extends backend hence inherits mechanism database-specific additional functionality needed implemented. Thus, MsBackend implementation extending MsBackendCached base class automatically inherit caching mechanism need implement . Examples backends extending MsBackendCached , among others, MsBackendSql, MsBackendOfflineSql, MsBackendMassbankSql. Analysis MS data requires, addition changing adding spectra variables also possibility manipulate peaks’ data (.e. m/z intensity values individual mass peaks spectra). simple example remove mass peaks intensity 100 data set compare number peaks per spectra operation.  operation reduce number peaks considerably. repeat operation now also two backends. number peaks also reduced two backends although keep peak data memory , discussed , allow changes data file (database) containing original data. caching mechanisms described work well spectra variables (generally) limited manageable sizes. MS peaks data (m/z intensity values individual peaks MS spectra) however represent much larger data volumes preventing cache store changes directly -memory, especially data large experiments high resolution instruments. reason, instead caching changes values within object, cache actual data manipulation operation(s). function modify peaks data, along parameters, automatically added internal lazy processing queue within Spectra object call one Spectra’s (peaks) data manipulation functions. mechanism implemented directly Spectra class backends thus implement . calling filterIntensity , data actually modified, function filter peaks data added processig queue. function along possibly defined parameters added ProcessingStep object: , advisable directly access internal slots Spectra object! function accessed : parameters : time peaks data accessed (like intensity call example ), Spectra object first request raw peaks data backend, check processing queue , empty, apply contained cached processing steps peaks data returning user. nice side effect, subset data accessed, data manipulations need applied requested data subset, full data. can positive impact overall performance: Next number common peaks data manipulation methods already implemented Spectra, make use processing queue, also addProcessing function allows apply user-provided function peaks data (using lazy evaluation mechanism). simple example define function scales intensities spectrum total intensity sum per spectrum 1. Functions addProcessing expected take peaks array input return peaks array result. Note also ... function definition required, internally additional parameters, spectrum’s MS level, default passed along function (see also addProcessing documentation entry ?Spectra information advanced examples). validation calculate sum intensities first 6 spectra. intensities thus scaled total intensity sum per spectra 1. Note , common operation MS2 data (fragment spectra), scaling function generally used (quantitative) MS1 data (example ). Finally, since lazy evaluation mechanism changing actual peaks data, can also undo data manipulations. simple reset call Spectra object restore data object initial state (fact simply clears processing queue). Along potential chaching mechanisms spectra variables, implementation lazy-evaluation queue allows apply also data manipulations MS data data resources inherently read-(reference libraries fragment spectra) thus enable analysis MS data independently data stored.","code":"#' Assign new spectra variables. s_mem$new_variable <- seq_along(s_mem) s_mzr$new_variable <- seq_along(s_mem) s_db$new_variable <- seq_along(s_mem) #' Show spectra variables for one of the Spectra spectraVariables(s_mzr) ##  [1] \"msLevel\"                  \"rtime\"                    ##  [3] \"acquisitionNum\"           \"scanIndex\"                ##  [5] \"dataStorage\"              \"dataOrigin\"               ##  [7] \"centroided\"               \"smoothed\"                 ##  [9] \"polarity\"                 \"precScanNum\"              ## [11] \"precursorMz\"              \"precursorIntensity\"       ## [13] \"precursorCharge\"          \"collisionEnergy\"          ## [15] \"isolationWindowLowerMz\"   \"isolationWindowTargetMz\"  ## [17] \"isolationWindowUpperMz\"   \"peaksCount\"               ## [19] \"totIonCurrent\"            \"basePeakMZ\"               ## [21] \"basePeakIntensity\"        \"ionisationEnergy\"         ## [23] \"lowMZ\"                    \"highMZ\"                   ## [25] \"mergedScan\"               \"mergedResultScanNum\"      ## [27] \"mergedResultStartScanNum\" \"mergedResultEndScanNum\"   ## [29] \"injectionTime\"            \"filterString\"             ## [31] \"spectrumId\"               \"ionMobilityDriftTime\"     ## [33] \"scanWindowLowerLimit\"     \"scanWindowUpperLimit\"     ## [35] \"new_variable\" #' Direct access to the backend's data. s_mem@backend@spectraData |> head() ##   msLevel rtime acquisitionNum scanIndex dataStorage ## 1       1 0.280              1         1    <memory> ## 2       1 0.559              2         2    <memory> ## 3       1 0.838              3         3    <memory> ## 4       1 1.117              4         4    <memory> ## 5       1 1.396              5         5    <memory> ## 6       1 1.675              6         6    <memory> ##                                                                    dataOrigin ## 1 /usr/local/lib/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML ## 2 /usr/local/lib/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML ## 3 /usr/local/lib/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML ## 4 /usr/local/lib/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML ## 5 /usr/local/lib/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML ## 6 /usr/local/lib/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML ##   centroided smoothed polarity precScanNum precursorMz precursorIntensity ## 1      FALSE       NA        1          NA          NA                 NA ## 2      FALSE       NA        1          NA          NA                 NA ## 3      FALSE       NA        1          NA          NA                 NA ## 4      FALSE       NA        1          NA          NA                 NA ## 5      FALSE       NA        1          NA          NA                 NA ## 6      FALSE       NA        1          NA          NA                 NA ##   precursorCharge collisionEnergy isolationWindowLowerMz ## 1              NA              NA                     NA ## 2              NA              NA                     NA ## 3              NA              NA                     NA ## 4              NA              NA                     NA ## 5              NA              NA                     NA ## 6              NA              NA                     NA ##   isolationWindowTargetMz isolationWindowUpperMz peaksCount totIonCurrent ## 1                      NA                     NA        578        898185 ## 2                      NA                     NA       1529       1037012 ## 3                      NA                     NA       1600       1094971 ## 4                      NA                     NA       1664       1135015 ## 5                      NA                     NA       1417       1106233 ## 6                      NA                     NA       1602       1181489 ##   basePeakMZ basePeakIntensity ionisationEnergy    lowMZ   highMZ mergedScan ## 1   124.0860            154089                0 105.0435 133.9837         NA ## 2   124.0859            182690                0 105.0275 133.9836         NA ## 3   124.0859            196650                0 105.0376 133.9902         NA ## 4   124.0859            203502                0 105.0376 133.9853         NA ## 5   124.0859            191715                0 105.0347 133.9885         NA ## 6   124.0859            213400                0 105.0420 133.9836         NA ##   mergedResultScanNum mergedResultStartScanNum mergedResultEndScanNum ## 1                  NA                       NA                     NA ## 2                  NA                       NA                     NA ## 3                  NA                       NA                     NA ## 4                  NA                       NA                     NA ## 5                  NA                       NA                     NA ## 6                  NA                       NA                     NA ##   injectionTime filterString                             spectrumId ## 1             0         <NA> sample=1 period=1 cycle=1 experiment=1 ## 2             0         <NA> sample=1 period=1 cycle=2 experiment=1 ## 3             0         <NA> sample=1 period=1 cycle=3 experiment=1 ## 4             0         <NA> sample=1 period=1 cycle=4 experiment=1 ## 5             0         <NA> sample=1 period=1 cycle=5 experiment=1 ## 6             0         <NA> sample=1 period=1 cycle=6 experiment=1 ##   ionMobilityDriftTime scanWindowLowerLimit scanWindowUpperLimit new_variable ## 1                   NA                   NA                   NA            1 ## 2                   NA                   NA                   NA            2 ## 3                   NA                   NA                   NA            3 ## 4                   NA                   NA                   NA            4 ## 5                   NA                   NA                   NA            5 ## 6                   NA                   NA                   NA            6 s_mzr@backend@spectraData ## DataFrame with 1862 rows and 26 columns ##      scanIndex acquisitionNum   msLevel  polarity peaksCount totIonCurrent ##      <integer>      <integer> <integer> <integer>  <integer>     <numeric> ## 1            1              1         1         1        578        898185 ## 2            2              2         1         1       1529       1037012 ## 3            3              3         1         1       1600       1094971 ## 4            4              4         1         1       1664       1135015 ## 5            5              5         1         1       1417       1106233 ## ...        ...            ...       ...       ...        ...           ... ## 1858       927            927         1         1       3528        418159 ## 1859       928            928         1         1       3662        429930 ## 1860       929            929         1         1       3442        413427 ## 1861       930            930         1         1       3564        447033 ## 1862       931            931         1         1       3517        441025 ##          rtime basePeakMZ basePeakIntensity ionisationEnergy     lowMZ ##      <numeric>  <numeric>         <numeric>        <numeric> <numeric> ## 1        0.280    124.086            154089                0   105.043 ## 2        0.559    124.086            182690                0   105.028 ## 3        0.838    124.086            196650                0   105.038 ## 4        1.117    124.086            203502                0   105.038 ## 5        1.396    124.086            191715                0   105.035 ## ...        ...        ...               ...              ...       ... ## 1858   258.636    123.090             28544                0   105.008 ## 1859   258.915    123.090             29164                0   105.009 ## 1860   259.194    123.090             28724                0   105.010 ## 1861   259.473    123.091             28743                0   105.012 ## 1862   259.752    123.091             27503                0   105.012 ##         highMZ mergedScan mergedResultScanNum mergedResultStartScanNum ##      <numeric>  <integer>           <integer>                <integer> ## 1      133.984         NA                  NA                       NA ## 2      133.984         NA                  NA                       NA ## 3      133.990         NA                  NA                       NA ## 4      133.985         NA                  NA                       NA ## 5      133.989         NA                  NA                       NA ## ...        ...        ...                 ...                      ... ## 1858   134.000         NA                  NA                       NA ## 1859   134.000         NA                  NA                       NA ## 1860   134.000         NA                  NA                       NA ## 1861   133.998         NA                  NA                       NA ## 1862   133.997         NA                  NA                       NA ##      mergedResultEndScanNum injectionTime filterString             spectrumId ##                   <integer>     <numeric>  <character>            <character> ## 1                        NA             0           NA sample=1 period=1 cy.. ## 2                        NA             0           NA sample=1 period=1 cy.. ## 3                        NA             0           NA sample=1 period=1 cy.. ## 4                        NA             0           NA sample=1 period=1 cy.. ## 5                        NA             0           NA sample=1 period=1 cy.. ## ...                     ...           ...          ...                    ... ## 1858                     NA             0           NA sample=1 period=1 cy.. ## 1859                     NA             0           NA sample=1 period=1 cy.. ## 1860                     NA             0           NA sample=1 period=1 cy.. ## 1861                     NA             0           NA sample=1 period=1 cy.. ## 1862                     NA             0           NA sample=1 period=1 cy.. ##      centroided ionMobilityDriftTime scanWindowLowerLimit scanWindowUpperLimit ##       <logical>            <numeric>            <numeric>            <numeric> ## 1         FALSE                   NA                   NA                   NA ## 2         FALSE                   NA                   NA                   NA ## 3         FALSE                   NA                   NA                   NA ## 4         FALSE                   NA                   NA                   NA ## 5         FALSE                   NA                   NA                   NA ## ...         ...                  ...                  ...                  ... ## 1858      FALSE                   NA                   NA                   NA ## 1859      FALSE                   NA                   NA                   NA ## 1860      FALSE                   NA                   NA                   NA ## 1861      FALSE                   NA                   NA                   NA ## 1862      FALSE                   NA                   NA                   NA ##                 dataStorage             dataOrigin new_variable ##                 <character>            <character>    <integer> ## 1    /usr/local/lib/R/sit.. /usr/local/lib/R/sit..            1 ## 2    /usr/local/lib/R/sit.. /usr/local/lib/R/sit..            2 ## 3    /usr/local/lib/R/sit.. /usr/local/lib/R/sit..            3 ## 4    /usr/local/lib/R/sit.. /usr/local/lib/R/sit..            4 ## 5    /usr/local/lib/R/sit.. /usr/local/lib/R/sit..            5 ## ...                     ...                    ...          ... ## 1858 /usr/local/lib/R/sit.. /usr/local/lib/R/sit..         1858 ## 1859 /usr/local/lib/R/sit.. /usr/local/lib/R/sit..         1859 ## 1860 /usr/local/lib/R/sit.. /usr/local/lib/R/sit..         1860 ## 1861 /usr/local/lib/R/sit.. /usr/local/lib/R/sit..         1861 ## 1862 /usr/local/lib/R/sit.. /usr/local/lib/R/sit..         1862 s_db@backend@localData |> head() ##   new_variable ## 1            1 ## 2            2 ## 3            3 ## 4            4 ## 5            5 ## 6            6 #' Change values in a copy of the variable s_db2 <- s_db rtime(s_db2) <- rtime(s_db2) + 10 #' Retention times of original copy should NOT change rtime(s_db) |> head() ## [1] 0.280 0.559 0.838 1.117 1.396 1.675 rtime(s_db2) |> head() ## [1] 10.280 10.559 10.838 11.117 11.396 11.675 s_db2 <- s_db  #' Cache an existing spectra variable in memory s_db2$rtime <- rtime(s_db2)  microbenchmark(     rtime(s_db),     rtime(s_db2),     times = 17 ) ## Unit: milliseconds ##          expr      min       lq     mean   median       uq      max neval ##   rtime(s_db) 6.867144 6.935762 7.046637 6.957643 7.034997 8.032556    17 ##  rtime(s_db2) 2.967924 3.004833 3.158521 3.054445 3.088198 4.590840    17 #' Remove all peaks with an intensity below 100 s_mem <- filterIntensity(s_mem, intensity = 100)  #' Compare the number of peaks before/after boxplot(list(before = lengths(s_mzr), after = lengths(s_mem)),         ylab = \"number of peaks\") #' Repeast filtering for the two other Spectra objects. s_mzr <- filterIntensity(s_mzr, intensity = 100) s_db <- filterIntensity(s_db, intensity = 100) #' Evaluate that subsetting worked for all. median(lengths(s_mem)) ## [1] 433 median(lengths(s_mzr)) ## [1] 433 median(lengths(s_db)) ## [1] 433 #' Show the processing queue. s_db@processingQueue ## [[1]] ## Object of class \"ProcessingStep\" ##  Function: user-provided function ##  Arguments: ##   o intensity = 100Inf ##   o msLevel = 1 #' Access a processing step's function s_db@processingQueue[[1L]]@FUN ## function (x, spectrumMsLevel, intensity = c(0, Inf), msLevel = spectrumMsLevel,  ##     ...)  ## { ##     if (!spectrumMsLevel %in% msLevel || !length(x))  ##         return(x) ##     x[which(between(x[, \"intensity\"], intensity)), , drop = FALSE] ## } ## <bytecode: 0x55a2b582b590> ## <environment: namespace:Spectra> #' Access the parameters for that function s_db@processingQueue[[1L]]@ARGS ## $intensity ## [1] 100 Inf ##  ## $msLevel ## [1] 1 #' Access intensity values and extract those of the 1st spectrum. intensity(s_db)[[1L]] ##   [1]    412    412    412    412    412    412    412    412    412   1237 ##  [11]    412    412    412    412    412    412    412    412    412    412 ##  [21]    412    412    824    412    412    412    412    412    412    412 ##  [31]    412    412    412    412    412    412   1237    412    412    412 ##  [41]    412    412    412    412    412    412    412    412    412    412 ##  [51]    824    412    412    412    412    412    412    412    824   1649 ##  [61]   2061   1237    412    412    412    412    412    412   2061   7008 ##  [71]  17727  22674  23910  12780   6184   3710   2061   1237    412    412 ##  [81]    412   1662    412   1258    861    854    845    873    842    412 ##  [91]    860   1307    882    442  12347  55349 132662 154089 115667  76817 ## [101]  41233  26904  11577   6155   2650   3966    872    461    461    433 ## [111]    412    442    430    461    412    412    412    412    412    426 ## [121]    449    412    412    824    412    412    433    412    412    412 ## [131]    412    412   1684    412    412    412   1649   1649   2061   1649 ## [141]   4947   6184   9482  10718   5359   2886   1649   1649   1237    412 ## [151]    412    412    412    412    412    412    412    412    412    412 ## [161]    412    412    412    412    412    412    412    412    412    412 ## [171]    412    412    412    412    412    412    412    412    412    412 ## [181]    412    412    412    412    412    412    412    412    412    412 ## [191]    412    412    412    412    824    412    412    412    412    412 ## [201]    412    412    412    412    412    412    412    824    412    412 ## [211]    412    412    412    412    412    412    412    412    412    412 ## [221]    412    824    824   1237   1649    824    412    412    412    412 ## [231]    824    412    824   1237    412    412    412    412    412    412 ## [241]    412    412    412    412    412    412    412 #' Compare applying the processing queue to either 10 spectra or the #' full set of spectra. microbenchmark(     intensity(s_mem[1:10]),     intensity(s_mem)[1:10],     times = 7) ## Unit: milliseconds ##                    expr       min        lq       mean     median         uq ##  intensity(s_mem[1:10])  3.542345  3.699177   3.906552   3.882108   3.977156 ##  intensity(s_mem)[1:10] 99.563948 99.721442 103.931024 100.194454 108.821023 ##         max neval ##    4.568749     7 ##  110.673833     7 #' Define a function that scales intensities scale_sum <- function(x, ...) {     x[, \"intensity\"] <- x[, \"intensity\"] / sum(x[, \"intensity\"], na.rm = TRUE)     x }  #' Add this function to the processing queue s_mem <- addProcessing(s_mem, scale_sum) s_mzr <- addProcessing(s_mzr, scale_sum) s_db <- addProcessing(s_db, scale_sum) sum(intensity(s_mem)) |> head() ## [1] 1 1 1 1 1 1 sum(intensity(s_mzr)) |> head() ## [1] 1 1 1 1 1 1 sum(intensity(s_db)) |> head() ## [1] 1 1 1 1 1 1 #' Restore the Spectra object to its initial state. s_db <- reset(s_db) median(lengths(s_db)) ## [1] 1547 sum(intensity(s_db)) |> head() ## [1]  898185 1037012 1094971 1135015 1106233 1181489"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"implementing-your-own-msbackend","dir":"Articles","previous_headings":"Workshop","what":"Implementing your own MsBackend","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"New backends Spectra extend MsBackend class implement methods defined infrastructure. Extending classes, MsBackendMemory MsBackendCached can help reducing number methods concepts needed implement. detailed example description provided Creating new MsBackend classes vignette - otherwise: open issue Spectra github repo. Add following code testthat.R file ensure compliance MsBackend definition:","code":"test_suite <- system.file(\"test_backends\", \"test_MsBackend\",                         package = \"Spectra\")  #' Assign an instance of the developed `MsBackend` to variable `be`. be <- my_be test_dir(test_suite, stop_on_failure = TRUE)"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"final-words","dir":"Articles","previous_headings":"Workshop","what":"Final words","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"designed support large data sets. easily extendable. support variety MS data file formats data representations. caching mechanism processing queue allow data analysis also read-data resources.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"acknowledgments","dir":"Articles","previous_headings":"","what":"Acknowledgments","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Thank Philippine Louail fixing typos suggesting improvements.","code":""},{"path":[]},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-matching-with-MetaboAnnotation.html","id":"introduction","dir":"Articles","previous_headings":"Overview","what":"Introduction","title":"MS/MS Spectra Matching with the `MetaboAnnotation` Package","text":"Spectra package provides functionality required annotation identification workflows untargeted LC-MS/MS data, , flexible customizable, might cumbersome beginners analysts accustomed R. fill gap developed MetaboAnnotation package builds upon Spectra provides functions annotation LC-MS LC-MS/MS data sets tailored towards less experienced R user (Rainer et al. 2022).","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-matching-with-MetaboAnnotation.html","id":"convenient-spectra-matching-using-metaboannotation","dir":"Articles","previous_headings":"Overview","what":"Convenient spectra matching using MetaboAnnotation","title":"MS/MS Spectra Matching with the `MetaboAnnotation` Package","text":"example use case match experimental MS2 spectra DDA experiment pesticide mix reference spectra MassBank. load experimental data file distributed via msdata R package. next restrict data set MS2 spectra addition clean spectra removing peaks spectrum intensity lower 5% largest peak intensity spectrum. Finally, single-peak spectra removed. leads data set consisting 2451 spectra. next connect MassBank database (running within docker image) create Spectra object representing data. Alternatively, MySQL database system available tutorial can run within docker, SQLite database version MassBank data (format required MsBackendMassbankSql available . downloading database MassbankSql-2021-03.db current R workspace, resource can loaded : now directly calculate similarities 2451 experimental (query) MS2 spectra 86576 MassBank reference (target) spectra using compareSpectra method, computationally intense similarity score calculated query target spectrum. alternative use matchSpectra function MetaboAnnotation package allows restrict similarity calculations query target spectra similar m/z precursor ion similar retention time. create CompareSpectraParam object setting parameter requirePrecursor = TRUE (restrict similarity calculations query target spectra similar precursor m/z) ppm = 10 (m/z difference query target precursor within 10 ppm). Parameter THRESHFUN enables define threshold function defines spectra considered matching. function used MS2 spectra similarity (calculated default dotproduct function) larger equal 0.8 considered matching. next call matchSpectra parameter object pass pest mbank query target Spectra, respectively. takes approximately 1 minute complete, tremendously fast, still much faster pairwise comparison query target spectra . result get MatchedSpectra object contains query target spectra well matching result (.e. information query spectrum matches target spectrum based similarity score). can use query target functions access query target Spectra objects matches extract matching information. display first 6 rows matrix. Functions whichQuery whichTarget return (unique) indices query target spectra matched. can see query spectra (29 2451 spectra) matched. part large proportion spectra MassBank precursor m/z available requirePrecursor = TRUE considered similarity calculation. Setting requirePrecursor = FALSE calculate similarity spectra (even missing precursor information) calculations can take several hours. MatchedSpectra object inherits much functionality Spectra object. spectraVariables returns example available spectra variables, query well target Spectra. variable names latter prefixed target_ discriminate variable names query. can access spectra individual variables using $ variable name, multiple variables spectraData function. extract retention time, precursor m/z query spectrum, precursor m/z target spectrum well similarity score object using spectraData function. returned DataFrame contains matching information full data set, .e. query spectrum hence, returns NA values query spectra matched target spectrum. Note also query spectra matching multiple target spectra represented multiple rows (one matching target spectrum). ’re interested query spectra match found thus subset MatchedSpectra query spectra matching target spectrum. Subsetting MatchedSpectra always relative query, .e. subsetting object index 4 restrict object matching results 4th query spectrum. now extract matching information subsetting: can also return compound names matching spectra. matchSpectra enables thus perform convenient spectra matching MS data represented Spectra objects. result, MatchedSpectra object returned , addition matching results, contains also query target spectra. Pre-filtering spectra prior actual spectra similarity calculation can reduce running time matchSpectra call might also miss potential matches. Note addition precursor m/z-based pre-filter also retention time pre-filtering available (see ?matchSpectra information). Also, advanced matching approach available MatchForwardReverseParam calculates addition forward score also reverse similarity match.","code":"library(Spectra) library(pander)  #' Load the pesticide mix data fl <- system.file(\"TripleTOF-SWATH\", \"PestMix1_DDA.mzML\", package = \"msdata\") pest <- Spectra(fl) #' restrict to MS2 data and remove intensities with intensity lower 5% pest <- filterMsLevel(pest, msLevel = 2L)  #' Remove peaks with an intensity below 5% or the spectra's BPC low_int <- function(x, ...) {     x > max(x, na.rm = TRUE) * 0.05 } pest <- filterIntensity(pest, intensity = low_int)  #' Remove peaks with a single peak pest <- pest[lengths(pest) > 1] library(RMariaDB) library(MsBackendMassbank)  #' Connect to the MassBank MySQL database con <- dbConnect(MariaDB(), user = \"massbank\", dbname = \"MassBank\",                  host = \"localhost\", pass = \"massbank\") mbank <- Spectra(con, source = MsBackendMassbankSql()) ## Alternative to the MySQL server - assuming the SQLite database was ## stored to the R working directory. library(RSQLite) library(MsBackendMassbank) con <- dbConnect(SQLite(), \"MassbankSql-2021-03.db\") mbank <- Spectra(con, source = MsBackendMassbankSql()) library(MetaboAnnotation) prm <- CompareSpectraParam(ppm = 10, requirePrecursor = TRUE,                            THRESHFUN = function(x) which(x >= 0.8)) mtch <- matchSpectra(pest, mbank, param = prm) mtch ## Object of class MatchedSpectra  ## Total number of matches: 78  ## Number of query objects: 2451 (29 matched) ## Number of target objects: 86576 (47 matched) head(matches(mtch)) ##   query_idx target_idx     score ## 1       163      31941 0.9264249 ## 2       163      32022 0.9264249 ## 3       163      32214 0.9264249 ## 4       163      32508 0.9264249 ## 5       163      32667 0.9264249 ## 6       163      32900 0.9264249 whichQuery(mtch) ##  [1]  163  320  420  433  493  496  497  571  682  685  686  805  806  809  810 ## [16]  819  829  983 1095 1454 1457 1706 1830 1834 1839 1906 2047 2048 2050 sum(is.na(precursorMz(mbank))) ## [1] 24498 spectraVariables(mtch) ##  [1] \"msLevel\"                        \"rtime\"                          ##  [3] \"acquisitionNum\"                 \"scanIndex\"                      ##  [5] \"dataStorage\"                    \"dataOrigin\"                     ##  [7] \"centroided\"                     \"smoothed\"                       ##  [9] \"polarity\"                       \"precScanNum\"                    ## [11] \"precursorMz\"                    \"precursorIntensity\"             ## [13] \"precursorCharge\"                \"collisionEnergy\"                ## [15] \"isolationWindowLowerMz\"         \"isolationWindowTargetMz\"        ## [17] \"isolationWindowUpperMz\"         \"peaksCount\"                     ## [19] \"totIonCurrent\"                  \"basePeakMZ\"                     ## [21] \"basePeakIntensity\"              \"ionisationEnergy\"               ## [23] \"lowMZ\"                          \"highMZ\"                         ## [25] \"mergedScan\"                     \"mergedResultScanNum\"            ## [27] \"mergedResultStartScanNum\"       \"mergedResultEndScanNum\"         ## [29] \"injectionTime\"                  \"filterString\"                   ## [31] \"spectrumId\"                     \"ionMobilityDriftTime\"           ## [33] \"scanWindowLowerLimit\"           \"scanWindowUpperLimit\"           ## [35] \"target_msLevel\"                 \"target_rtime\"                   ## [37] \"target_acquisitionNum\"          \"target_scanIndex\"               ## [39] \"target_dataStorage\"             \"target_dataOrigin\"              ## [41] \"target_centroided\"              \"target_smoothed\"                ## [43] \"target_polarity\"                \"target_precScanNum\"             ## [45] \"target_precursorMz\"             \"target_precursorIntensity\"      ## [47] \"target_precursorCharge\"         \"target_collisionEnergy\"         ## [49] \"target_isolationWindowLowerMz\"  \"target_isolationWindowTargetMz\" ## [51] \"target_isolationWindowUpperMz\"  \"target_spectrum_id\"             ## [53] \"target_spectrum_name\"           \"target_date\"                    ## [55] \"target_authors\"                 \"target_license\"                 ## [57] \"target_copyright\"               \"target_publication\"             ## [59] \"target_splash\"                  \"target_compound_id\"             ## [61] \"target_adduct\"                  \"target_ionization\"              ## [63] \"target_ionization_voltage\"      \"target_fragmentation_mode\"      ## [65] \"target_collision_energy_text\"   \"target_instrument\"              ## [67] \"target_instrument_type\"         \"target_formula\"                 ## [69] \"target_exactmass\"               \"target_smiles\"                  ## [71] \"target_inchi\"                   \"target_inchikey\"                ## [73] \"target_cas\"                     \"target_pubchem\"                 ## [75] \"target_synonym\"                 \"target_precursor_mz_text\"       ## [77] \"target_compound_name\"           \"score\" spectraData(mtch, c(\"rtime\", \"precursorMz\", \"target_precursorMz\", \"score\")) ## DataFrame with 2500 rows and 4 columns ##          rtime precursorMz target_precursorMz     score ##      <numeric>   <numeric>          <numeric> <numeric> ## 1        7.216    137.9639                 NA        NA ## 2       13.146     56.9419                 NA        NA ## 3       13.556     89.9449                 NA        NA ## 4       23.085    207.0294                 NA        NA ## 5       27.385    121.0990                 NA        NA ## ...        ...         ...                ...       ... ## 2496   895.182    137.9850                 NA        NA ## 2497   895.472     56.0495                 NA        NA ## 2498   896.252    142.9611                 NA        NA ## 2499   896.662     53.0129                 NA        NA ## 2500   898.602     91.5022                 NA        NA mtch <- mtch[whichQuery(mtch)] spectraData(mtch, c(\"rtime\", \"precursorMz\", \"target_precursorMz\", \"score\")) ## DataFrame with 78 rows and 4 columns ##         rtime precursorMz target_precursorMz     score ##     <numeric>   <numeric>          <numeric> <numeric> ## 1     173.184     305.156            305.157  0.926425 ## 2     173.184     305.156            305.157  0.926425 ## 3     173.184     305.156            305.157  0.926425 ## 4     173.184     305.156            305.157  0.926425 ## 5     173.184     305.156            305.157  0.926425 ## ...       ...         ...                ...       ... ## 74    527.419     279.156            279.159  0.814331 ## 75    527.419     279.156            279.159  0.822396 ## 76    570.096     373.039            373.041  0.934031 ## 77    570.506     373.042            373.041  0.888829 ## 78    570.825     373.040            373.041  0.827892 pandoc.table(style = \"rmarkdown\",     as.data.frame(spectraData(mtch, c(\"rtime\", \"target_compound_name\",                                       \"score\"))))"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-matching-with-MetaboAnnotation.html","id":"ms2-spectra-matching-in-an-xcms-workflow","dir":"Articles","previous_headings":"Overview","what":"MS2 spectra matching in an xcms workflow","title":"MS/MS Spectra Matching with the `MetaboAnnotation` Package","text":"LC-MS/MS-based untargeted metabolomics (small compound mass spectrometry experiments general) quantification compounds performed MS1 MS2 data used identification features. Quantification MS1 data requires chromatographic peak detection step can performed using functionality xcms package. load thus xcms package import full MS data using readMSData function. next perform chromatographic peak detection using centWave algorithm (see LC-MS/MS data analysis xcms vignette xcms package details chromatographic peak detection settings). total 99 chromatographic peaks identified. display first 6 . can now extract MS2 spectra chromatographic peak chromPeakSpectra function. function identifies MS2 spectra recorded instrument retention time within retention time precursor m/z within m/z boundaries chromatographic peak. setting return.type = \"Spectra\" ensure data returned newer Spectra format hence enabling simplified spectra matching functionality presented . Spectra variable peak_id contains identified chromatographic peak (.e. row name chromPeaks). next, like previous section, clean spectra removing peaks intensity 5% largest peak intensity per spectrum removing spectra single peak. addition scale also intensities within MS2 spectrum replacing intensities relative maximum peak intensity (see information). addition query spectra, also normalize MassBank spectra way. Next perform now spectra matching parameters previous section. , restrict MatchedSpectra query spectra matched. table lists compound names matching spectra chromatographic peaks. can also directly plot matching (query target) spectra using plotSpectraMirror function subsetting MatchedSpectra object query spectrum interest. plot third query spectrum matching target spectra.  Summarizing, chromPeakSpectra featureSpectra functions, xcms allows return MS data Spectra objects enables, shown simple example, perform MS2 spectra matching using Spectra well MetaboAnnotation packages hence simplifying MS/MS-based annotation LC-MS features xcms.","code":"library(xcms) pest_all <- readMSData(fl, mode = \"onDisk\") cwp <- CentWaveParam(snthresh = 5, noise = 100, ppm = 10,                      peakwidth = c(3, 30)) pest_all <- findChromPeaks(pest_all, param = cwp) head(chromPeaks(pest_all)) ##            mz    mzmin    mzmax      rt   rtmin   rtmax       into      intb ## CP01 142.9926 142.9921 142.9931 130.615 125.856 134.241  1113.8028  1106.229 ## CP02 221.0918 221.0906 221.0925 240.897 236.657 246.984   756.6935   744.779 ## CP03 220.0985 220.0978 220.0988 240.897 237.187 246.327  2060.5921  2052.549 ## CP04 219.0957 219.0950 219.0962 241.018 236.253 246.327 15172.6662 15133.811 ## CP05 153.0659 153.0655 153.0663 330.591 325.373 334.400  2148.7134  2141.943 ## CP06 235.1447 235.1441 235.1454 330.591 326.431 334.400  2836.2675  2829.627 ##           maxo  sn sample ## CP01  346.7006 102      1 ## CP02  212.5239  21      1 ## CP03  585.3036 151      1 ## CP04 4877.1162 367      1 ## CP05  784.9196 114      1 ## CP06 1006.9720 110      1 pest_ms2 <- chromPeakSpectra(pest_all, return.type = \"Spectra\") pest_ms2 ## MSn data (Spectra) with 158 spectra in a MsBackendMzR backend: ##            msLevel     rtime scanIndex ##          <integer> <numeric> <integer> ## F1.S1000         2   128.237      1000 ## F1.S1008         2   128.737      1008 ## F1.S1023         2   129.857      1023 ## F1.S1812         2   237.869      1812 ## F1.S1846         2   241.299      1846 ## ...            ...       ...       ... ## F1.S5115         2   575.255      5115 ## F1.S5272         2   596.584      5272 ## F1.S5236         2   592.424      5236 ## F1.S5266         2   596.054      5266 ## F1.S7344         2   873.714      7344 ##  ... 38 more variables/columns. ##  ## file(s): ## PestMix1_DDA.mzML pest_ms2$peak_id ##   [1] \"CP01\" \"CP01\" \"CP01\" \"CP04\" \"CP04\" \"CP05\" \"CP05\" \"CP06\" \"CP06\" \"CP08\" ##  [11] \"CP08\" \"CP11\" \"CP11\" \"CP12\" \"CP12\" \"CP13\" \"CP13\" \"CP13\" \"CP13\" \"CP14\" ##  [21] \"CP14\" \"CP14\" \"CP14\" \"CP18\" \"CP22\" \"CP22\" \"CP22\" \"CP22\" \"CP22\" \"CP25\" ##  [31] \"CP25\" \"CP25\" \"CP25\" \"CP25\" \"CP26\" \"CP26\" \"CP26\" \"CP26\" \"CP26\" \"CP26\" ##  [41] \"CP33\" \"CP33\" \"CP34\" \"CP34\" \"CP34\" \"CP34\" \"CP34\" \"CP35\" \"CP35\" \"CP35\" ##  [51] \"CP35\" \"CP35\" \"CP36\" \"CP41\" \"CP41\" \"CP41\" \"CP42\" \"CP42\" \"CP42\" \"CP42\" ##  [61] \"CP42\" \"CP44\" \"CP44\" \"CP46\" \"CP46\" \"CP46\" \"CP46\" \"CP47\" \"CP47\" \"CP47\" ##  [71] \"CP48\" \"CP48\" \"CP48\" \"CP50\" \"CP51\" \"CP51\" \"CP51\" \"CP52\" \"CP52\" \"CP52\" ##  [81] \"CP53\" \"CP53\" \"CP53\" \"CP53\" \"CP53\" \"CP57\" \"CP57\" \"CP57\" \"CP57\" \"CP57\" ##  [91] \"CP59\" \"CP59\" \"CP60\" \"CP60\" \"CP61\" \"CP61\" \"CP63\" \"CP63\" \"CP63\" \"CP63\" ## [101] \"CP64\" \"CP64\" \"CP64\" \"CP64\" \"CP64\" \"CP65\" \"CP66\" \"CP66\" \"CP66\" \"CP66\" ## [111] \"CP67\" \"CP67\" \"CP67\" \"CP69\" \"CP69\" \"CP69\" \"CP71\" \"CP71\" \"CP71\" \"CP71\" ## [121] \"CP72\" \"CP72\" \"CP72\" \"CP73\" \"CP81\" \"CP81\" \"CP81\" \"CP81\" \"CP82\" \"CP82\" ## [131] \"CP82\" \"CP85\" \"CP85\" \"CP88\" \"CP88\" \"CP89\" \"CP89\" \"CP89\" \"CP90\" \"CP90\" ## [141] \"CP90\" \"CP91\" \"CP91\" \"CP91\" \"CP93\" \"CP93\" \"CP93\" \"CP93\" \"CP93\" \"CP93\" ## [151] \"CP94\" \"CP94\" \"CP94\" \"CP94\" \"CP95\" \"CP98\" \"CP98\" \"CP99\" #' Remove peaks with an intensity below 5% pest_ms2 <- filterIntensity(pest_ms2, intensity = low_int)  #' Remove peaks with a single peak pest_ms2 <- pest_ms2[lengths(pest_ms2) > 1] #' Define a function to *normalize* the intensities norm_int <- function(x, ...) {     maxint <- max(x[, \"intensity\"], na.rm = TRUE)     x[, \"intensity\"] <- 100 * x[, \"intensity\"] / maxint     x } #' *Apply* the function to the data pest_ms2 <- addProcessing(pest_ms2, norm_int) mbank <- addProcessing(mbank, norm_int) pest_match <- matchSpectra(pest_ms2, mbank, param = prm) pest_match ## Object of class MatchedSpectra  ## Total number of matches: 31  ## Number of query objects: 155 (7 matched) ## Number of target objects: 86576 (19 matched) pest_match <- pest_match[whichQuery(pest_match)] pandoc.table(     style = \"rmarkdown\",     as.data.frame(spectraData(pest_match, c(\"peak_id\", \"rtime\",                                             \"target_compound_name\")))) plotSpectraMirror(pest_match[3])"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-matching-with-MetaboAnnotation.html","id":"session-information","dir":"Articles","previous_headings":"","what":"Session information","title":"MS/MS Spectra Matching with the `MetaboAnnotation` Package","text":"","code":"sessionInfo() ## R version 4.3.2 (2023-10-31) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Ubuntu 22.04.3 LTS ##  ## Matrix products: default ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3  ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0 ##  ## locale: ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C             ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        ##  ## time zone: Etc/UTC ## tzcode source: system (glibc) ##  ## attached base packages: ## [1] stats4    stats     graphics  grDevices utils     datasets  methods   ## [8] base      ##  ## other attached packages: ##  [1] xcms_4.0.0               MSnbase_2.28.1           mzR_2.36.0               ##  [4] Rcpp_1.0.11              Biobase_2.62.0           MetaboAnnotation_1.6.1   ##  [7] MsBackendMassbank_1.11.1 RMariaDB_1.3.1           pander_0.6.5             ## [10] Spectra_1.12.0           ProtGenerics_1.34.0      BiocParallel_1.36.0      ## [13] S4Vectors_0.40.2         BiocGenerics_0.48.1      BiocStyle_2.30.0         ##  ## loaded via a namespace (and not attached): ##   [1] splines_4.3.2                 later_1.3.1                   ##   [3] bitops_1.0-7                  filelock_1.0.2                ##   [5] tibble_3.2.1                  preprocessCore_1.64.0         ##   [7] XML_3.99-0.16                 lifecycle_1.0.4               ##   [9] doParallel_1.0.17             rprojroot_2.0.4               ##  [11] lattice_0.22-5                MASS_7.3-60                   ##  [13] MultiAssayExperiment_1.28.0   magrittr_2.0.3                ##  [15] limma_3.58.1                  sass_0.4.7                    ##  [17] rmarkdown_2.25                jquerylib_0.1.4               ##  [19] yaml_2.3.7                    httpuv_1.6.12                 ##  [21] MsCoreUtils_1.14.1            DBI_1.1.3                     ##  [23] RColorBrewer_1.1-3            lubridate_1.9.3               ##  [25] abind_1.4-5                   zlibbioc_1.48.0               ##  [27] GenomicRanges_1.54.1          purrr_1.0.2                   ##  [29] AnnotationFilter_1.26.0       RCurl_1.98-1.13               ##  [31] rappdirs_0.3.3                GenomeInfoDbData_1.2.11       ##  [33] IRanges_2.36.0                pkgdown_2.0.7                 ##  [35] ncdf4_1.22                    codetools_0.2-19              ##  [37] CompoundDb_1.6.0              DelayedArray_0.28.0           ##  [39] DT_0.30                       xml2_1.3.6                    ##  [41] tidyselect_1.2.0              matrixStats_1.1.0             ##  [43] BiocFileCache_2.10.1          base64enc_0.1-3               ##  [45] jsonlite_1.8.8                multtest_2.58.0               ##  [47] ellipsis_0.3.2                survival_3.5-7                ##  [49] iterators_1.0.14              systemfonts_1.0.5             ##  [51] foreach_1.5.2                 tools_4.3.2                   ##  [53] progress_1.2.2                ragg_1.2.6                    ##  [55] glue_1.6.2                    gridExtra_2.3                 ##  [57] SparseArray_1.2.2             xfun_0.41                     ##  [59] MatrixGenerics_1.14.0         GenomeInfoDb_1.38.1           ##  [61] dplyr_1.1.4                   BiocManager_1.30.22           ##  [63] fastmap_1.1.1                 fansi_1.0.5                   ##  [65] digest_0.6.33                 timechange_0.2.0              ##  [67] R6_2.5.1                      mime_0.12                     ##  [69] textshaping_0.3.7             colorspace_2.1-0              ##  [71] rsvg_2.6.0                    RSQLite_2.3.3                 ##  [73] utf8_1.2.4                    generics_0.1.3                ##  [75] robustbase_0.99-1             prettyunits_1.2.0             ##  [77] httr_1.4.7                    htmlwidgets_1.6.3             ##  [79] S4Arrays_1.2.0                pkgconfig_2.0.3               ##  [81] gtable_0.3.4                  blob_1.2.4                    ##  [83] impute_1.76.0                 MassSpecWavelet_1.68.0        ##  [85] XVector_0.42.0                htmltools_0.5.7               ##  [87] MALDIquant_1.22.1             clue_0.3-65                   ##  [89] scales_1.3.0                  png_0.1-8                     ##  [91] knitr_1.45                    MetaboCoreUtils_1.10.0        ##  [93] rjson_0.2.21                  curl_5.1.0                    ##  [95] cachem_1.0.8                  stringr_1.5.1                 ##  [97] BiocVersion_3.18.1            parallel_4.3.2                ##  [99] AnnotationDbi_1.64.1          mzID_1.40.0                   ## [101] vsn_3.70.0                    desc_1.4.2                    ## [103] pillar_1.9.0                  grid_4.3.2                    ## [105] vctrs_0.6.5                   MsFeatures_1.10.0             ## [107] RANN_2.6.1                    pcaMethods_1.94.0             ## [109] promises_1.2.1                dbplyr_2.4.0                  ## [111] xtable_1.8-4                  cluster_2.1.6                 ## [113] evaluate_0.23                 cli_3.6.1                     ## [115] compiler_4.3.2                rlang_1.1.2                   ## [117] crayon_1.5.2                  QFeatures_1.12.0              ## [119] ChemmineR_3.54.0              affy_1.80.0                   ## [121] plyr_1.8.9                    fs_1.6.3                      ## [123] stringi_1.8.2                 munsell_0.5.0                 ## [125] Biostrings_2.70.1             lazyeval_0.2.2                ## [127] Matrix_1.6-4                  hms_1.1.3                     ## [129] MsExperiment_1.4.0            bit64_4.0.5                   ## [131] ggplot2_3.4.4                 KEGGREST_1.42.0               ## [133] statmod_1.5.0                 shiny_1.8.0                   ## [135] highr_0.10                    SummarizedExperiment_1.32.0   ## [137] interactiveDisplayBase_1.40.0 AnnotationHub_3.10.0          ## [139] igraph_1.5.1                  memoise_2.0.1                 ## [141] affyio_1.72.0                 bslib_0.6.1                   ## [143] DEoptimR_1.1-3                bit_4.0.5"},{"path":[]},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"description","dir":"Articles","previous_headings":"Overview","what":"Description","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Mass spectrometry (MS) data key technology modern proteomics metabolomics experiments. Due continuous improvements MS instrumentation, generated data can easily become large. Also, additional resources MS data exist, spectra libraries databases, specific file formats database systems sometimes support manipulations original data. Learning experiences MSnbase Bioconductor package, Spectra package developed provide even flexible expandable infrastructure MS data R. package implements clear separation user interface code provide, store import MS data. latter defined MsBackend interface allows implementation data type, format storage-dependent backends. Backends can thus implemented specific file types data resources different ways represent MS data (e.g. memory -disk data representations described (Gatto, Gibb, Rainer 2020)). also supposed interchangeable hence allowing user switch backends without affecting analysis. enable processing also large MS data sets, data manipulations default directly applied data cached lazy evaluation queue allows analyses also read-data representations. workshop shows expandability new infrastructure enable seamless integration analysis MS data variety input formats. illustrated simple comparison matching experimental MS2 spectra public spectral databases export data format commonly used exchange MS2 data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"pre-requisites","dir":"Articles","previous_headings":"Overview","what":"Pre-requisites","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Basic familiarity R Bioconductor. Basic understanding Mass Spectrometry (MS) data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"installation-and-participation","dir":"Articles","previous_headings":"Overview","what":"Installation and Participation","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Get docker image tutorial docker pull jorainer/spectra_tutorials:latest. Start docker using Enter http://localhost:8787 web browser log username rstudio password bioc. Open R-markdown file (vignettes/analyzing-MS-data--different-sources--Spectra.Rmd) RStudio server version web browser evaluate R code blocks.","code":"docker run \\     -e PASSWORD=bioc \\     -p 8787:8787 \\     jorainer/spectra_tutorials:latest"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"rbioconductor-packages-used","dir":"Articles","previous_headings":"Overview","what":"R/Bioconductor packages used","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Spectra MsCoreUtils MsBackendMgf MsBackendMassbank CompoundDb","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"learning-goals","dir":"Articles","previous_headings":"Overview > Workshop goals and objectives","what":"Learning goals","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Understand import MS data R. Understand basic concept backends Spectra can used work MS data various sources.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"learning-objectives","dir":"Articles","previous_headings":"Overview > Workshop goals and objectives","what":"Learning objectives","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Import export MS data Spectra. Integrate MS data different resources MS data analysis workflow. Apply different data manipulations MS data represented Spectra object. Use Spectra perform spectra comparisons R.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"lc-msms-in-a-nutshell","dir":"Articles","previous_headings":"Workshop","what":"LC-MS/MS in a nutshell","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Mass spectrometry (MS) instruments measure mass--charge ratios (m/z) abundances ions. resulting m/z intensity values stored/represented spectrum. compounds charged, need ionized first (e.g. electro spray ionization (ESI)). MS usually combined another separation technique, liquid chromatography (LC). adds another dimension data: retention time (rt). LC-MS data represented multiple spectra, spectrum retention time. LC-MS measure features characterized m/z retention time - still don’t know molecule actually measured. Create addition fragment (MS/MS) spectra ions get information structure. Commonly used method: collision induced dissociation (CID). collision chamber filled e.g. N2, ions get fragmented spectrum fragments recorded (Steckel Schlosser 2019). Comparing matching fragment spectra reference helps identifying compound.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"the-spectra-package","dir":"Articles","previous_headings":"Workshop","what":"The Spectra package","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Purpose: provide expandable, well tested user-friendly infrastructure mass spectrometry (MS) data. Design: separation user interface (functions) code provide, store read MS data. Advantage: support different ways store handle data without affecting user functionality: functionality Spectra object independent data , either -memory, -disk, locally remote. Expandable: implementation additional backend classes (extending MsBackend virtual class) possible add support additional input file formats data representations without needing change Spectra package object. workshop : import MS data mzML files, select MS2 spectra certain compound, compare match MS2 spectra reference MS2 spectra public database, annotate spectra export file MGF format.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"ms-data-import-and-handling","dir":"Articles","previous_headings":"Workshop","what":"MS data import and handling","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"import MS data mzML files provided within package. files contain MSn data mix 8 standard compounds (added either water pool human serum samples) measured HILIC-based LC-MS/MS setup. MS2 data generated data dependent acquisition using collision energy 20eV. data import representation experimental data use MsBackendMzR backend supports import (export) data common raw mass spectrometry file formats (.e. mzML, mzXML CDF). MS data now represented Spectra object, can thought data.frame rows individual spectra columns spectra variables (\"rtime\", .e. retention time). spectraVariables function lists available variables within Spectra object. spectra variable can accessed either via $ name using dedicated access function (preferred way). extract retention times first spectra using either $rtime function rtime. Spectra object contains information total 1578 spectra 2 mzML files. using MsBackendMzR backend, general information spectrum kept memory resulting low memory footprint. spectra’s m/z intensity values can accessed mz intensity functions also peaksData function returns list numeric matrices m/z intensity values. using MsBackendMzR backend, retrieved demand original data files time functions called. can also load full data memory changing backend MsBackendMzR MsBackendDataFrame. affect way use Spectra object : operations functions available, independently way data stored (.e. backend used). size Spectra object now larger, since full data loaded memory. subset Spectra objects, can use either [ one many available filter* functions (usually efficient [). subset sps_all arbitrary spectra simply using: workshop, next want identify experimental data MS2 spectra generated ion matches m/z [M+H]+ ion metabolite cystine (one standards added sample mix measured present experimental data). thus use filterPrecursorMzValues function subset data MS2 spectra matching m/z (accepting difference m/z 10 parts-per-million (ppm)). total 6 spectra matched target precursor m/z.","code":"library(Spectra)  #' Define the input files fls <- dir(system.file(\"mzML\", package = \"SpectraTutorials\"),            full.names = TRUE)  #' Import the data sps_all <- Spectra(fls, source = MsBackendMzR()) #' List all available spectra variables (attributes) spectraVariables(sps_all) ##  [1] \"msLevel\"                  \"rtime\"                    ##  [3] \"acquisitionNum\"           \"scanIndex\"                ##  [5] \"dataStorage\"              \"dataOrigin\"               ##  [7] \"centroided\"               \"smoothed\"                 ##  [9] \"polarity\"                 \"precScanNum\"              ## [11] \"precursorMz\"              \"precursorIntensity\"       ## [13] \"precursorCharge\"          \"collisionEnergy\"          ## [15] \"isolationWindowLowerMz\"   \"isolationWindowTargetMz\"  ## [17] \"isolationWindowUpperMz\"   \"peaksCount\"               ## [19] \"totIonCurrent\"            \"basePeakMZ\"               ## [21] \"basePeakIntensity\"        \"ionisationEnergy\"         ## [23] \"lowMZ\"                    \"highMZ\"                   ## [25] \"mergedScan\"               \"mergedResultScanNum\"      ## [27] \"mergedResultStartScanNum\" \"mergedResultEndScanNum\"   ## [29] \"injectionTime\"            \"filterString\"             ## [31] \"spectrumId\"               \"ionMobilityDriftTime\"     ## [33] \"scanWindowLowerLimit\"     \"scanWindowUpperLimit\" #' Access the spectras' retention time sps_all$rtime |> head() ## [1] 0.273 0.570 0.873 1.183 1.491 1.798 rtime(sps_all) |> head() ## [1] 0.273 0.570 0.873 1.183 1.491 1.798 print(object.size(sps_all), units = \"MB\") ## 0.4 Mb ## Show the first 6 lines of the first spectrum's peak matrix peaksData(sps_all)[[1L]] |> head() ##            mz intensity ## [1,] 50.22647  383.7802 ## [2,] 52.97586  377.5944 ## [3,] 59.97846  518.6014 ## [4,] 60.04677 4716.1259 ## [5,] 60.06897  181.5105 ## [6,] 61.02565  250.6573 #' Change backend to a MsBackendDataFrame: load data into memory sps_all <- setBackend(sps_all, MsBackendDataFrame()) print(object.size(sps_all), units = \"MB\") ## 16.5 Mb sps_all[c(4, 2, 5)] ## MSn data (Spectra) with 3 spectra in a MsBackendDataFrame backend: ##     msLevel     rtime scanIndex ##   <integer> <numeric> <integer> ## 1         1     1.183         4 ## 2         1     0.570         2 ## 3         1     1.491         5 ##  ... 33 more variables/columns. ## Processing: ##  Switch backend from MsBackendMzR to MsBackendDataFrame [Tue Dec  5 09:15:50 2023] #' Define the m/z ratio for an ion of cystine mz <- 241.0311  #' Subset the dataset to MS2 spectra matching the m/z sps <- filterPrecursorMzValues(sps_all, mz = mz, ppm = 10) sps ## MSn data (Spectra) with 6 spectra in a MsBackendDataFrame backend: ##     msLevel     rtime scanIndex ##   <integer> <numeric> <integer> ## 1         2   209.936       673 ## 2         2   220.072       714 ## 3         2   231.604       734 ## 4         2   215.089       761 ## 5         2   225.739       781 ## 6         2   240.020       804 ##  ... 33 more variables/columns. ## Processing: ##  Switch backend from MsBackendMzR to MsBackendDataFrame [Tue Dec  5 09:15:50 2023] ##  Filter: select spectra with precursor m/z matching 241.0311 [Tue Dec  5 09:15:50 2023]"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"data-processing-and-manipulation","dir":"Articles","previous_headings":"Workshop","what":"Data processing and manipulation","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"plotSpectra function can used visualize spectra. plot first spectrum data subset.  raw MS2 spectrum contains many low intensity peaks, likely representing noise. Thus next filter spectra removing peaks intensity smaller 5% maximum intensity spectrum (.e. base peak intensity). end define function takes intensity values spectrum input returns logical value whether peak retained (TRUE) (FALSE). function passed filterIntensity function perform actual filtering spectra. filtering, spectra cleaner:  addition want replace absolute peak intensities values relative spectrum’s highest peak intensity. end define function takes peak matrix input (.e. numeric matrix columns containing m/z intensity values) returns two-column numeric matrix. function passed parameter FUN addProcessing function perform desired data processing. addProcessing function allows add user-defined data manipulation operation MS data (.e. peaks matrices) Spectra object. show effect data processing extract intensities first spectrum: intensity values now 0 100.","code":"#' Plot the first spectrum plotSpectra(sps[1]) #' Define a filtering function low_int <- function(x, ...) {     x > max(x, na.rm = TRUE) * 0.05 } #' Apply the function to filter the spectra sps <- filterIntensity(sps, intensity = low_int) #' Plot the first spectrum after filtering plotSpectra(sps[1]) #' Define a function to scale the intensities. `x` is expected to be a #' *peak matrix*, i.e. numeric matrix with columns m/z and intensity scale_int <- function(x, ...) {     maxint <- max(x[, \"intensity\"], na.rm = TRUE)     x[, \"intensity\"] <- 100 * x[, \"intensity\"] / maxint     x } #' *Apply* the function to the data sps <- addProcessing(sps, scale_int) #' Get the intensities after scaling peaksData(sps)[[1L]] ##             mz  intensity ## [1,]  74.02289  13.439402 ## [2,] 120.01111  93.631648 ## [3,] 122.02667  49.287979 ## [4,] 151.98277 100.000000 ## [5,] 153.99924   9.422415 ## [6,] 177.99880   6.581111 ## [7,] 195.02556  12.969807 ## [8,] 241.03074  54.633224"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"spectrum-data-comparison","dir":"Articles","previous_headings":"Workshop","what":"Spectrum data comparison","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"next perform pairwise comparison subsetted spectra using dot product similarity measure. Prior actual similarity calculation, peaks individual spectra matched (.e. determined peak one spectrum correspond spectrum based mass--charge ratios). specify ppm = 20 peaks difference m/z smaller 20ppm considered matching. default, compareSpectra uses (normalized) dot-product calculate similarity spectra, variety different algorithms can used instead (see Spectra vignette information). pairwise spectra similarities represented heatmap (note RStudio docker might crash pheatmap call - avoid add filename = \"hm.pdf\" heatmap call).  similarity selected experimental MS2 spectra high (fact 0.88) suggesting fragment spectra original compound.","code":"#' Pairwise comparison of all spectra sim_mat <- compareSpectra(sps, ppm = 20) sim_mat ##           [,1]      [,2]      [,3]      [,4]      [,5]      [,6] ## [1,] 1.0000000 0.9717669 0.8907129 0.9721095 0.8795912 0.8818719 ## [2,] 0.9717669 1.0000000 0.9992127 0.9964783 0.9525726 0.9486682 ## [3,] 0.8907129 0.9992127 1.0000000 0.9947650 0.9518138 0.9478201 ## [4,] 0.9721095 0.9964783 0.9947650 1.0000000 0.9543584 0.9494561 ## [5,] 0.8795912 0.9525726 0.9518138 0.9543584 1.0000000 0.9994800 ## [6,] 0.8818719 0.9486682 0.9478201 0.9494561 0.9994800 1.0000000 library(pheatmap) hm <- pheatmap(sim_mat, breaks = seq(0, 1, length.out = 101))"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"comparing-spectra-against-massbank","dir":"Articles","previous_headings":"Workshop","what":"Comparing spectra against MassBank","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Although precursor m/z spectra matches m/z cystine, can still exclude represent fragments ions different compounds (m/z cystine). Matching experimental spectra public spectral library can used first step identification process untargeted metabolomics data. Several (public) spectral libraries small molecules available, : MassBank MassBank North America MoNa Human Metabolom Database HMDB GNPS … databases MsBackend interfaces already implemented allowing inclusion data directly R-based analysis workflows. Access MassBank data example possible MsBackendMassbank package. package provides MsBackendMassbank import/export MassBank files well MsBackendMassbankSql backend directly interfaces MassBank MySQL database. connect local installation MassBank MySQL database (release 2021.03 provided within docker image tutorial). next load MsBackendMassbank package initialize Spectra object MsBackendMassbankSql backend providing database connection. Alternatively, MySQL database system available tutorial can run within docker, MassBank data can also retrieved Bioconductor’s AnnotationHub. case MsBackendCompDb backend (defined CompoundDb) used instead, backends retrieve data SQL database properties. list MassBank releases/versions available Bioconductor’s AnnotationHub. can retrieve respective release access spectra data code . Note AnnotationHub cache downloaded database locally subsequent call download database . Spectra object mbank represents now MS data MassBank database total 86576 spectra. fact, mbank object contain MS data, primary keys spectra MassBank database. Hence, also relatively low memory footprint. operation Spectra object backend load requested data database demand. Calling intensity Spectra object example retrieve intensity values spectra database. extract peaks matrix first spectrum. can see, also MassBank provides absolute intensities spectrum. data scale, however like scale values 0 100, just like experimental data. thus apply data processing operation also MassBank Spectra object. worked, although (obvious reasons) allowed change m/z intensity values within MassBank database. possible? works, data manipulation operation Spectra object cached within object’s lazy evaluation queue operation applied m/z intensity values --fly whenever data requested (.e. whenever mz intensity called ). side effect, also allows undo certain data operations simply calling reset Spectra object. next want compare experimental spectra spectra MassBank. Instead comparing 86576 available spectra, first filter MassBank database spectra precursor m/z matching one [M+H]+ ion cystine. Note , alternative filterPrecursorMzValues used , also use containsMz function screen spectra containing actual peak matching precursor m/z. left us 12 spectra calculate similarity experimental spectra using compareSpectra function. result got (normalized dot product) similarity score tested MassBank spectrum (columns) experimental spectrum (rows).  get high similarity scores, also lower ones. next determine best matching pair comparisons. best_match variable contains now index best matching MassBank experimental spectrum. visualize two using mirror plot showing best matches experimental spectra (upper panel) fragment spectra MassBank (lower panel). Matching peaks highlighted blue color. Plotting functions Spectra highly customizable example add m/z individual peak annotation intensity peak higher 5.  comparison plot also two spectra low similarity score.  Since identify MassBank spectrum high similarity experimental spectra also like know compound spectrum actually represents. Spectra objects generally flexible can arbitrarily many additional annotation fields (.e. spectra variables) spectrum. Thus, use spectraVariables function list variables available MassBank Spectra object. fact, addition spectra specific information like instrument measured ionization voltage used, get also information originating compound name (\"compound_name\"), chemical formula (\"formula\") INChI key (\"inchikey\"). thus next subset mbank_sub best matching spectrum display associated compound name. Note database provided AnnotationHub contain compound names column/spectra variable \"name\" instead \"compound_name\", thus, used option download MassBank data AnnotationHub , use mbank_best_match$name instead mbank_best_match$compound_name . Indeed, experimental cystine spectrum matches (one) cystine spectrum MassBank. next add name chemical formula spectrum experimental spectra. also set collision energy 20eV assign ion/adduct cystine reference spectrum created. Spectra allows adding new (replacing existing) spectra variables using $<-. Note: convenient spectra matching functionality designed less experienced R users available MetaboAnnotation package. second tutorial available within github repository/R package additional tutorials can found MetaboAnnotationTutorials repository/R package.","code":"library(RMariaDB) #' Connect to the MassBank MySQL database con <- dbConnect(MariaDB(), user = \"massbank\", dbname = \"MassBank\",                  host = \"localhost\", pass = \"massbank\") library(MsBackendMassbank) #' Access the spectra data in MassBank mbank <- Spectra(con, source = MsBackendMassbankSql()) mbank ## MSn data (Spectra) with 86576 spectra in a MsBackendMassbankSql backend: ##         msLevel precursorMz  polarity ##       <integer>   <numeric> <integer> ## 1             2         506         0 ## 2            NA          NA         1 ## 3            NA          NA         0 ## 4            NA          NA         1 ## 5            NA          NA         0 ## ...         ...         ...       ... ## 86572         2     449.380         1 ## 86573         2     426.022         0 ## 86574         2     131.060         0 ## 86575         2     183.170         1 ## 86576         2     358.270         0 ##  ... 42 more variables/columns. ##  Use  'spectraVariables' to list all of them. library(AnnotationHub) ah <- AnnotationHub() query(ah, \"MassBank\") ## AnnotationHub with 3 records ## # snapshotDate(): 2023-10-20 ## # $dataprovider: MassBank ## # $species: NA ## # $rdataclass: CompDb ## # additional mcols(): taxonomyid, genome, description, ## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags, ## #   rdatapath, sourceurl, sourcetype  ## # retrieve records with, e.g., 'object[[\"AH107048\"]]'  ##  ##              title                                 ##   AH107048 | MassBank CompDb for release 2021.03   ##   AH107049 | MassBank CompDb for release 2022.06   ##   AH111334 | MassBank CompDb for release 2022.12.1 mb <- ah[[\"AH107048\"]] mbank <- Spectra(mb) print(object.size(mbank), units = \"MB\") ## 6.6 Mb #' Get peaks matrix for the first spectrum peaksData(mbank[1])[[1L]] ##             mz    intensity ##  [1,] 146.7608     0.980184 ##  [2,] 158.8635   938.145447 ##  [3,] 174.9888    56.718353 ##  [4,] 176.8688   847.169678 ##  [5,] 213.9867   133.479233 ##  [6,] 221.0402     3.189743 ##  [7,] 238.9681    76.114944 ##  [8,] 246.3159     2.592528 ##  [9,] 255.0206     2.059480 ## [10,] 256.8184     1.597428 ## [11,] 272.9973   801.059082 ## [12,] 290.9484     4.848598 ## [13,] 293.9753     1.233919 ## [14,] 310.1210    33.710808 ## [15,] 352.9528    49.103123 ## [16,] 370.9348   461.860779 ## [17,] 390.0576     4.611422 ## [18,] 408.0003 23963.912109 ## [19,] 409.0284     0.884388 ## [20,] 426.1263    69.098648 ## [21,] 470.0574     2.051622 ## [22,] 487.9893   812.386597 ## [23,] 585.8845     1.093564 #' Scale intensities for all MassBank spectra mbank <- addProcessing(mbank, scale_int) peaksData(mbank[1])[[1L]] ##             mz    intensity ##  [1,] 146.7608 4.090250e-03 ##  [2,] 158.8635 3.914826e+00 ##  [3,] 174.9888 2.366824e-01 ##  [4,] 176.8688 3.535189e+00 ##  [5,] 213.9867 5.570010e-01 ##  [6,] 221.0402 1.331061e-02 ##  [7,] 238.9681 3.176232e-01 ##  [8,] 246.3159 1.081847e-02 ##  [9,] 255.0206 8.594089e-03 ## [10,] 256.8184 6.665973e-03 ## [11,] 272.9973 3.342773e+00 ## [12,] 290.9484 2.023292e-02 ## [13,] 293.9753 5.149072e-03 ## [14,] 310.1210 1.406732e-01 ## [15,] 352.9528 2.049045e-01 ## [16,] 370.9348 1.927318e+00 ## [17,] 390.0576 1.924319e-02 ## [18,] 408.0003 1.000000e+02 ## [19,] 409.0284 3.690499e-03 ## [20,] 426.1263 2.883446e-01 ## [21,] 470.0574 8.561298e-03 ## [22,] 487.9893 3.390042e+00 ## [23,] 585.8845 4.563378e-03 #' Filter MassBank for spectra of precursor ion mbank_sub <- filterPrecursorMzValues(mbank, mz = mz, ppm = 10) mbank_sub ## MSn data (Spectra) with 12 spectra in a MsBackendMassbankSql backend: ##       msLevel precursorMz  polarity ##     <integer>   <numeric> <integer> ## 1           2     241.031         1 ## 2           2     241.031         1 ## 3           2     241.031         1 ## 4           2     241.031         1 ## 5           2     241.031         1 ## ...       ...         ...       ... ## 8           2     241.031         1 ## 9           2     241.031         1 ## 10          2     241.031         1 ## 11          2     241.031         1 ## 12          2     241.031         1 ##  ... 42 more variables/columns. ##  Use  'spectraVariables' to list all of them. ## Lazy evaluation queue: 1 processing step(s) ## Processing: ##  Filter: select spectra with precursor m/z matching 241.0311 [Tue Dec  5 09:15:59 2023] #' Compare MassBank subset to experimental spectra res <- compareSpectra(sps, mbank_sub, ppm = 20) res ##       RP014401  RP014402    RP014403  CE000600  CE000598  CE000602  CE000595 ## [1,] 0.2026317 0.3343503 0.024658599 0.9021127 0.9069832 0.7714246 0.4711052 ## [2,] 0.1807144 0.2933160 0.021440961 0.8863867 0.8655798 0.7507946 0.4307388 ## [3,] 0.1801990 0.2892779 0.005165072 0.8862688 0.8541779 0.7335706 0.4406949 ## [4,] 0.1596113 0.3067826 0.005668103 0.8666230 0.8760503 0.7770975 0.4382448 ## [5,] 0.1784321 0.3608116 0.005392068 0.8792278 0.9101864 0.7761744 0.5016645 ## [6,] 0.1871595 0.3629835 0.005564441 0.8849054 0.8705634 0.7200598 0.5052482 ##       CE000599  CE000596  CE000597  CE000603  CE000601 ## [1,] 0.5101048 0.7358534 0.2843629 0.6267950 0.6085564 ## [2,] 0.5304578 0.7415840 0.3069911 0.6079285 0.5669921 ## [3,] 0.5379880 0.7480320 0.3157130 0.6220260 0.5458613 ## [4,] 0.4738241 0.6957804 0.2580864 0.5819616 0.6049588 ## [5,] 0.4913249 0.7185230 0.2783447 0.6469762 0.6055037 ## [6,] 0.5013606 0.7278495 0.2864854 0.6554185 0.5523657 pheatmap(res) best_match <- which(res == max(res), arr.ind = TRUE) best_match ##      row col ## [1,]   5   5 #' Specifying a function to draw peak labels label_fun <- function(x) {     ints <- unlist(intensity(x))     mzs <- format(unlist(mz(x)), digits = 4)     mzs[ints < 5] <- \"\"     mzs } plotSpectraMirror(sps[best_match[1]], mbank_sub[best_match[2]],                   ppm = 20, labels = label_fun, labelPos = 2,                   labelOffset = 0.2, labelSrt = -30) grid() plotSpectraMirror(sps[3], mbank_sub[1],                   ppm = 20, labels = label_fun, labelPos = 2,                   labelOffset = 0.2, labelSrt = -30) grid() #' What variables are available in MassBank spectraVariables(mbank_sub) ##  [1] \"msLevel\"                 \"rtime\"                   ##  [3] \"acquisitionNum\"          \"scanIndex\"               ##  [5] \"dataStorage\"             \"dataOrigin\"              ##  [7] \"centroided\"              \"smoothed\"                ##  [9] \"polarity\"                \"precScanNum\"             ## [11] \"precursorMz\"             \"precursorIntensity\"      ## [13] \"precursorCharge\"         \"collisionEnergy\"         ## [15] \"isolationWindowLowerMz\"  \"isolationWindowTargetMz\" ## [17] \"isolationWindowUpperMz\"  \"spectrum_id\"             ## [19] \"spectrum_name\"           \"date\"                    ## [21] \"authors\"                 \"license\"                 ## [23] \"copyright\"               \"publication\"             ## [25] \"splash\"                  \"compound_id\"             ## [27] \"adduct\"                  \"ionization\"              ## [29] \"ionization_voltage\"      \"fragmentation_mode\"      ## [31] \"collision_energy_text\"   \"instrument\"              ## [33] \"instrument_type\"         \"formula\"                 ## [35] \"exactmass\"               \"smiles\"                  ## [37] \"inchi\"                   \"inchikey\"                ## [39] \"cas\"                     \"pubchem\"                 ## [41] \"synonym\"                 \"precursor_mz_text\"       ## [43] \"compound_name\" mbank_best_match <- mbank_sub[best_match[2]] mbank_best_match$compound_name ## [1] \"Cystine\" #' Add annotations to the experimental spectra sps$name <- mbank_best_match$compound_name sps$formula <- mbank_best_match$formula sps$adduct <- mbank_best_match$adduct sps$collisionEnergy <- 20"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"data-export","dir":"Articles","previous_headings":"Workshop","what":"Data export","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"last want export spectra file MGF format. use MsBackendMgf package provides MsBackendMgf backend adds support MGF file import/export Spectra objects. Data Spectra objects can generally exported export function. format data exported depends specified MsBackend class. using instance MsBackendMgf define export data file MGF format. read first 6 lines resulting file.","code":"library(MsBackendMgf)  #' Export the spectra to a MGF file export(sps, backend = MsBackendMgf(), file = \"Cystine_ce20.mgf\") readLines(\"Cystine_ce20.mgf\", n = 6) ## [1] \"BEGIN IONS\"                                ## [2] \"TITLE=msLevel 2; retentionTime ; scanNum \" ## [3] \"msLevel=2\"                                 ## [4] \"RTINSECONDS=209.93599999998\"               ## [5] \"SCANS=673\"                                 ## [6] \"scanIndex=673\""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"comparing-spectra-against-hmdb","dir":"Articles","previous_headings":"Workshop","what":"Comparing spectra against HMDB","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"addition MsBackendMassbank, provides access MassBank data, also MsBackendHmdb package supporting spectral data public Human Metabolome Database (HMDB). package however yet provide direct access HMDB database , MsBackendHmdbXml backend, allows import MS2 spectra files HMDB format. provided HMDB individual xml files custom file format bundled (can hence downloaded) single archive. alternative, also possible create portable annotation resources using CompoundDb package. packages allows example create annotation databases MoNA, MassBank HMDB. CompDb annotations small compounds different resources become available also Bioconductors AnnotationHub. CompDb database containing compound annotations along (experimental) MS2 spectra provided data release MetaboAnnotationTutorials repository. download SQLite database temporary file. can now load annotation resource using CompDb function CompoundDb package: can now create Spectra object get access MS2 spectra within package: now Spectra object containing MS2 spectra HMDB along compound annotations. spectra variables (including compound annotations) available spectrum : Also , want filter data resource first spectra matching precursor m/z. Unfortunately, HMDB provided spectra’s precursor m/z hence need use containsMz function find spectra containing peak m/z matching m/z ion interest. addition, need use rather large tolerance value (defines maximal acceptable absolute difference m/z values) since experimental spectra HMDB seem recorded well calibrated instrument. total 3904 spectra contain peak required m/z (+/- 0.2 Dalton) can proceed calculate spectral similarities experimental spectra. highest similarity spectra spectra HMDB r max(res). compare two best matching spectra mirror plot, upper panel showing experimental spectrum lower panel best matching MS2 spectrum HMDB.  experimental spectrum seems nicely match reference MS2 HMDB. extract compound identifier best matching HMDB spectrum (stored spectra variable called \"compound_id\") name compound fact, matching spectrum HMDB experimental spectrum L-Cystine.","code":"#' Download the CompDb database using curl library(curl) dbname <- \"CompDb.Hsapiens.HMDB.5.0.sqlite\" db_file <- file.path(tempdir(), dbname) curl_download(     paste0(\"https://github.com/jorainer/MetaboAnnotationTutorials/\",            \"releases/download/2021-11-02/\", dbname),     destfile = db_file) library(CompoundDb) #' Load a CompDb database with compound annotation from HMDB cdb <- CompDb(db_file) cdb ## class: CompDb  ##  data source: HMDB  ##  version: 5.0  ##  organism: Hsapiens  ##  compound count: 217776  ##  MS/MS spectra count: 64920 hmdb <- Spectra(cdb) hmdb ## MSn data (Spectra) with 64920 spectra in a MsBackendCompDb backend: ##         msLevel precursorMz  polarity ##       <integer>   <numeric> <integer> ## 1            NA          NA         1 ## 2            NA          NA         1 ## 3            NA          NA         1 ## 4            NA          NA         1 ## 5            NA          NA         1 ## ...         ...         ...       ... ## 64916        NA          NA         0 ## 64917        NA          NA         0 ## 64918        NA          NA         0 ## 64919        NA          NA         0 ## 64920        NA          NA         1 ##  ... 32 more variables/columns. ##  Use  'spectraVariables' to list all of them. ##  data source: HMDB  ##  version: 5.0  ##  organism: Hsapiens spectraVariables(hmdb) ##  [1] \"msLevel\"                 \"rtime\"                   ##  [3] \"acquisitionNum\"          \"scanIndex\"               ##  [5] \"dataStorage\"             \"dataOrigin\"              ##  [7] \"centroided\"              \"smoothed\"                ##  [9] \"polarity\"                \"precScanNum\"             ## [11] \"precursorMz\"             \"precursorIntensity\"      ## [13] \"precursorCharge\"         \"collisionEnergy\"         ## [15] \"isolationWindowLowerMz\"  \"isolationWindowTargetMz\" ## [17] \"isolationWindowUpperMz\"  \"compound_id\"             ## [19] \"name\"                    \"inchi\"                   ## [21] \"inchikey\"                \"formula\"                 ## [23] \"exactmass\"               \"smiles\"                  ## [25] \"original_spectrum_id\"    \"predicted\"               ## [27] \"splash\"                  \"instrument_type\"         ## [29] \"instrument\"              \"spectrum_id\"             ## [31] \"msms_mz_range_min\"       \"msms_mz_range_max\"       ## [33] \"synonym\" #' Identify spectra containing a peak matching cystine m/z has_mz <- containsMz(hmdb, mz = mz, tolerance = 0.2) #' Subset HMDB hmdb_sub <- hmdb[has_mz] #' Compare HMDB against experimental spectra res <- compareSpectra(hmdb_sub, sps, tolerance = 0.2) best_match <- which(res == max(res), arr.ind = TRUE) ## Specifying a function to draw peak labels label_fun <- function(x) {     format(unlist(mz(x)), digits = 4) } plotSpectraMirror(hmdb_sub[best_match[1]], sps[best_match[2]], tolerance = 0.2,                   labels = label_fun, labelPos = 2, labelOffset = 0.2,                   labelSrt = -30) grid() hmdb_sub[best_match[1]]$compound_id ## [1] \"HMDB0000192\" hmdb_sub[best_match[1]]$name ## [1] \"L-Cystine\""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"an-alternative-way-to-store-ms-data","dir":"Articles","previous_headings":"Workshop","what":"An alternative way to store MS data","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"use cases might useful store data whole MS experiments central place. MsBackendSql backend allows example store data relational database, simple SQLite files MySQL/MariaDB database systems. latter case allow also remote access centrally stored data set. Also, performance, especially large datasets much better MySQL database compared SQLite database used . section show data MS experiment can inserted database can retrieved. create empty SQLite database (temporary file). detailed , SQL database system used instead (resulting eventually even better performance). can next import data example experiment selected database. MsBackendSql allows now access use MS data stored database. simply provide connection database parameter Spectra function use MsBackendSql backend. memory footprint Spectra object small data retrieved database --fly. performance backend also higher example MsBackendMzR. compare performance MsBackendSql present small data set MsBackendDataFrame (data -memory) MsBackendMzR (data retrieved original file). Thus, MsBackendSql similar performance data memory. performance even better smaller subsets data loaded. real world use case backend store large MS experiment central place. Data analysts connect central database, explore subset data ultimately either load subsets data (local) memory changing backend data subset e.g. MsBackendDataFrame store data subset local files e.g. mzML format using export method MsBackendMzR backend.","code":"library(RSQLite) scon <- dbConnect(SQLite(), tempfile()) library(MsBackendSql) ## Create a database containing the full raw data createMsBackendSqlDatabase(scon, fls) ## [1] TRUE sps_db <- Spectra(scon, source = MsBackendSql()) sps_db ## MSn data (Spectra) with 1578 spectra in a MsBackendSql backend: ##        msLevel precursorMz  polarity ##      <integer>   <numeric> <integer> ## 1            1          NA         1 ## 2            1          NA         1 ## 3            1          NA         1 ## 4            1          NA         1 ## 5            1          NA         1 ## ...        ...         ...       ... ## 1574         2    130.9659         1 ## 1575         2    175.1178         1 ## 1576         2    193.9281         1 ## 1577         2    107.9501         1 ## 1578         2     95.0608         1 ##  ... 34 more variables/columns. ##  Use  'spectraVariables' to list all of them. ## Database: /tmp/Rtmp8PSjCo/file72476a12197 print(object.size(sps_db), units = \"MB\") ## 0.1 Mb ## Load the data to different backends sps_mzr <- Spectra(fls, source = MsBackendMzR()) sps_df <- setBackend(sps_mzr, MsBackendDataFrame())  library(microbenchmark) microbenchmark(peaksData(sps_mzr),                peaksData(sps_df),                peaksData(sps_db),                times = 10) ## Unit: milliseconds ##                expr       min        lq      mean   median        uq       max ##  peaksData(sps_mzr) 257.38043 279.94393 409.83186 309.5956 350.96996 956.12619 ##   peaksData(sps_df)  81.88257  98.45020 104.34838 104.2593 118.27750 118.43437 ##   peaksData(sps_db)  47.46898  53.22407  62.45025  60.9762  74.91102  79.79879 ##  neval ##     10 ##     10 ##     10 microbenchmark(peaksData(sps_mzr[1:10]),                peaksData(sps_df[1:10]),                peaksData(sps_db[1:10]),                times = 10) ## Unit: milliseconds ##                      expr      min       lq      mean   median        uq ##  peaksData(sps_mzr[1:10]) 9.665231 9.733448 11.578363 9.886057 10.471554 ##   peaksData(sps_df[1:10]) 4.051564 4.079166  4.226335 4.168067  4.235768 ##   peaksData(sps_db[1:10]) 2.837020 2.988482  3.127493 3.145069  3.250351 ##        max neval ##  24.067594    10 ##   4.777438    10 ##   3.394379    10"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"short-summary","dir":"Articles","previous_headings":"","what":"Short summary","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Spectra provides powerful infrastructure MS data. Independence data analysis data storage functionality allows easily add support additional data types data handling/storage modes. Caching data operations applying --fly allows data manipulations regardless data stored.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"simple use case matching experimental MS2 spectra public database illustrated short tutorial flexibility expandability Spectra package enables seamless integration mass spectrometry data different sources. possible clear separation user functionality (Spectra object) representation data (MsBackend object). Backends MsBackendMgf, MsBackendMassbank MsBackendHmdbXml provide support additional data formats data sources, others, due much lower memory footprint (MsBackendMzR, MsBackendHdf5Peaks), enable analysis also large data sets. importantly however, backends interchangeable affect way users can handle analyze MS data Spectra package. Also, caching data operations within Spectra object applying upon data requests, data operations can applied data resource regardless data stored, even data read-.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"what-else","dir":"Articles","previous_headings":"","what":"What else?","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"MetaboAnnotation (Andrea Vicini, Michael Witting) provide simple functions matching spectra m/z retention time data (Rainer et al. 2022). MsExperiment represent whole MS experiments (incl. Spectra, sample annotation, data quantitation, annotation files etc). msQC (Thomas Naake): QC measures MS data. MsBackendMsp (Steffen Neumann): backend files MSP format. ...","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Johannes Rainer. Author, maintainer. Michael Witting. Author. Laurent Gatto. Author. Sebastian Gibb. Author. Philippine Louail. Contributor.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Rainer J, Witting M, Gatto L, Gibb S (2023). SpectraTutorials: Mass Spectrometry Data Analysis Spectra. R package version 0.9.0, https://jorainer.github.io/SpectraTutorials/.","code":"@Manual{,   title = {SpectraTutorials: Mass Spectrometry Data Analysis with Spectra},   author = {Johannes Rainer and Michael Witting and Laurent Gatto and Sebastian Gibb},   year = {2023},   note = {R package version 0.9.0},   url = {https://jorainer.github.io/SpectraTutorials/}, }"},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"mass-spectrometry-data-analysis-with-spectra","dir":"","previous_headings":"","what":"Mass Spectrometry Data Analysis with Spectra","title":"Mass Spectrometry Data Analysis with Spectra","text":"Tutorials workshops describing explaining infrastructure mass spectrometry (MS) data analysis centered around Bioconductor’s Spectra package. Workshops also given (either -person virtually) various conferences. available, recordings linked respective workshop section. See NEWS.md information current version change log.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Mass Spectrometry Data Analysis with Spectra","text":"-line code evaluation workshops can also run using self-contained docker image R packages server version RStudio (Posit) pre-installed. alternative, rendered versions workshops available SpectraTutorials (navigate Articles drop-menu select workshop interest). installation: Get docker image tutorial docker pull jorainer/spectra_tutorials:latest. Start docker using Enter http://localhost:8787 web browser log username rstudio password bioc. RStudio server version: open R-markdown (.Rmd) files vignettes folder evaluate R code blocks.","code":"docker run \\     -e PASSWORD=bioc \\     -p 8787:8787 \\     jorainer/spectra_tutorials:latest"},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"seamless-integration-of-mass-spectrometry-data-from-different-sources","dir":"","previous_headings":"","what":"Seamless Integration of Mass Spectrometry Data from Different Sources","title":"Mass Spectrometry Data Analysis with Spectra","text":"rendered version","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"short-description","dir":"","previous_headings":"Seamless Integration of Mass Spectrometry Data from Different Sources","what":"Short description","title":"Mass Spectrometry Data Analysis with Spectra","text":"workshop use Spectra : import MS data mzML files, select MS2 spectra certain compound, compare match MS2 spectra reference MS2 spectra public database, annotate spectra export file MGF format.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"description","dir":"","previous_headings":"Seamless Integration of Mass Spectrometry Data from Different Sources","what":"Description","title":"Mass Spectrometry Data Analysis with Spectra","text":"(instructor-led live demo) workshop explains Spectra package shows new infrastructure can used represent analyze Mass Spectrometry (MS) data. use different data type format specific backends guarantees easy expandability infrastructure. illustrated tutorial simple use case, MS/MS (.k.. MS2) spectra, measured MS instrument, compared matched public spectral database containing public domain reference MS2 spectra. video recording workshop BioC2021 conference available . video recording workshop EuroBioc2020 conference available .","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"learning-goals","dir":"","previous_headings":"Seamless Integration of Mass Spectrometry Data from Different Sources > Workshop goals and objectives","what":"Learning goals","title":"Mass Spectrometry Data Analysis with Spectra","text":"Understand import MS data R. Understand basic concept different backends can used Spectra work MS data various sources.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"learning-objectives","dir":"","previous_headings":"Seamless Integration of Mass Spectrometry Data from Different Sources > Workshop goals and objectives","what":"Learning objectives","title":"Mass Spectrometry Data Analysis with Spectra","text":"Import export MS data Spectra. Integrate MS data different resources MS data analysis workflow. Apply different data manipulations MS data represented Spectra object. Use Spectra perform spectra comparisons R.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"pre-requisites","dir":"","previous_headings":"Seamless Integration of Mass Spectrometry Data from Different Sources","what":"Pre-requisites","title":"Mass Spectrometry Data Analysis with Spectra","text":"Basic familiarity R Bioconductor. Basic understanding Mass Spectrometry (MS) data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"rbioconductor-packages-used","dir":"","previous_headings":"Seamless Integration of Mass Spectrometry Data from Different Sources","what":"R/Bioconductor packages used","title":"Mass Spectrometry Data Analysis with Spectra","text":"Spectra MsCoreUtils MsBackendMgf MsBackendMassbank CompoundDb","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"spectra-an-expandable-infrastructure-to-handle-mass-spectrometry-data","dir":"","previous_headings":"","what":"Spectra: an expandable infrastructure to handle mass spectrometry data","title":"Mass Spectrometry Data Analysis with Spectra","text":"rendered version","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"short-description-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data","what":"Short description","title":"Mass Spectrometry Data Analysis with Spectra","text":"Explain different representations mass spectrometry data along properties advantages. Explain caching lazy processing queue Spectra package.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"description-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data","what":"Description","title":"Mass Spectrometry Data Analysis with Spectra","text":"Spectra package provides modern infrastructure MS data handling specifically designed enable extension additional data resources alternative data representations. can realized extending virtual MsBackend class related methods. Implementations MsBackend classes can tailored specific needs, low memory footprint, fast processing, remote data access, also support specific additional data types variables. Importantly, data processing Spectra objects independent backend use due lazy evaluation mechanism caches data manipulations internally. workshop discusses different available data representations MS data along properties, advantages performances. addition, Spectra’s concept lazy evaluation data manipulations presented, well simple caching mechanism data modifications. Finally, explains new MsBackend instances can implemented tested ensure compliance.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"pre-requisites-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data","what":"Pre-requisites","title":"Mass Spectrometry Data Analysis with Spectra","text":"Basic familiarity R Bioconductor. Basic understanding Mass Spectrometry (MS) data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"rbioconductor-packages-used-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data","what":"R/Bioconductor packages used","title":"Mass Spectrometry Data Analysis with Spectra","text":"Spectra MsBackendSql","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"workshop-goals-and-objectives-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data","what":"Workshop goals and objectives","title":"Mass Spectrometry Data Analysis with Spectra","text":"technical demonstration internals Spectra package design MS infrastructure. ’re demonstrating use cases analysis workflows .","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"learning-goals-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data > Workshop goals and objectives","what":"Learning goals","title":"Mass Spectrometry Data Analysis with Spectra","text":"Understand MS data handled Spectra. Understand differences properties different MsBackend implementations.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"learning-objectives-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data > Workshop goals and objectives","what":"Learning objectives","title":"Mass Spectrometry Data Analysis with Spectra","text":"Learn MS data handled Spectra. Understand data representations/backends fit purpose/use case. Insights internals Spectra MS infrastructure facilitate implementation backend(s).","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"msms-spectra-matching-with-the-metaboannotation-package","dir":"","previous_headings":"","what":"MS/MS Spectra Matching with the MetaboAnnotation Package","title":"Mass Spectrometry Data Analysis with Spectra","text":"rendered version Spectra package provides functionality required annotation identification workflows untargeted LC-MS/MS data, , flexible customizable, might cumbersome beginners analysts accustomed R. fill gap developed MetaboAnnotation package builds upon Spectra provides functions annotation LC-MS LC-MS/MS data sets tailored towards less experienced R user. tutorial shows MetaboAnnotation can used R-based LC-MS/MS annotation workflows. Note also additional tutorial MetaboAnnotation, CompoundDb MetaboCoreUtils packages available: MetaboAnnotationTutorials.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"Mass Spectrometry Data Analysis with Spectra","text":"contributions, see RforMassSpectrometry contributions guideline.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Mass Spectrometry Data Analysis with Spectra","text":"See RforMassSpectrometry Code Conduct.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"acknowledgments","dir":"","previous_headings":"","what":"Acknowledgments","title":"Mass Spectrometry Data Analysis with Spectra","text":"Thank Philippine Louail fixing typos suggesting improvements Spectra: expandable infrastructure handle mass spectrometry data.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-9","dir":"Changelog","previous_headings":"","what":"Version 0.9.0","title":"Version 0.9","text":"Use R 4.3 Bioconductor 3.18.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-8","dir":"Changelog","previous_headings":"","what":"Version 0.8.2","title":"Version 0.8","text":"Add second example caching changes better saving database.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-8-1","dir":"Changelog","previous_headings":"","what":"Version 0.8.1","title":"Version 0.8","text":"Include feedback suggestions Philippine Louail.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-8-2","dir":"Changelog","previous_headings":"","what":"Version 0.8.0","title":"Version 0.8","text":"Add new tutorial Spectra: expandable infrastructure handle mass spectrometry data. Download MassBank data alternative AnnotationHub.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-7","dir":"Changelog","previous_headings":"","what":"Version 0.7.3","title":"Version 0.7","text":"Fix mislabeling columns experimental spectra rows MassBank spectra spectra similarity matrix (thanks CSAMA2023 attendee pointing !).","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-7-1","dir":"Changelog","previous_headings":"","what":"Version 0.7.2","title":"Version 0.7","text":"Use MsBackendSql instead MsqlBackend.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-7-2","dir":"Changelog","previous_headings":"","what":"Version 0.7.1","title":"Version 0.7","text":"Simplify/clarify description spectra data processing.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-7-3","dir":"Changelog","previous_headings":"","what":"Version 0.7.0","title":"Version 0.7","text":"Add section use MsqlBackend compare performance MsBackendMzR MsBackendDataFrame.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-6","dir":"Changelog","previous_headings":"","what":"Version 0.6.3","title":"Version 0.6","text":"Better describe usage alternative SQLite MassBank database file two workshops.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-6-1","dir":"Changelog","previous_headings":"","what":"Version 0.6.2","title":"Version 0.6","text":"Add citation.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-6-2","dir":"Changelog","previous_headings":"","what":"Version 0.6.1","title":"Version 0.6","text":"Adapt changes Spectra >= 1.5.8: export spectraVariableMapping method. Adapt changes CompoundDb >= 0.9.6: \"target_name\" available spectra variable Spectra objects returned CompDb database. Adapt changes MetaboAnnotation >= 0.99.4: also ppm error reported matchMz.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-6-3","dir":"Changelog","previous_headings":"","what":"Version 0.6.0","title":"Version 0.6","text":"Remove Spectra object data HMDB. Use CompDb database HMDB annotations.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-5","dir":"Changelog","previous_headings":"","what":"Version 0.5.0","title":"Version 0.5","text":"Add additional vignette describing spectra matching MetaboAnnotation package integration xcms analysis workflow.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-4","dir":"Changelog","previous_headings":"","what":"Version 0.4.0","title":"Version 0.4","text":"Version used BioC2021 conference. Use MassBank release 2021.03. Restructure document simplify analysis (reduce mzML files 20eV collision energy).","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-3","dir":"Changelog","previous_headings":"","what":"Version 0.3.2","title":"Version 0.3","text":"Use MassBank release 2021.02 add expand descriptive text.","code":""}]
+[{"path":"https://jorainer.github.io/SpectraTutorials/Abstract-EuroBioc2020.html","id":null,"dir":"","previous_headings":"","what":"Abstract","title":"Abstract","text":"Mass spectrometry (MS) data key technology modern proteomics metabolomics experiments. Due continuous improvements MS instrumentation, generated data can easily become large. Also, different additional resources MS data exist, spectra libraries databases, specific file formats sometimes support manipulations original data. Learning experiences r Biocpkg(\"MSnbase\") Bioconductor package developed novel infrastructure handle MS spectral data R, r Biocpkg(\"Spectra\") package. package implements clear separation user functionality code provide, store import mass spectrometry data. Different backends can hence used enable access data various data resources designed specifically large MS data sets. Data manipulations default directly applied data cached lazy processing queue allows analyses also read-data representations. workshop shows expandability new infrastructure enable seamless integration analysis MS data variety input formats illustrated simple matching experimental MS2 spectra public spectral database export data format commonly used exchange MS2 data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/Abstract-EuroBioc2022.html","id":null,"dir":"","previous_headings":"","what":"Abstract","title":"Abstract","text":"Mass spectrometry (MS) data key technology modern proteomics metabolomics experiments. Due continuous improvements MS instrumentation, generated data can easily become large. Also, different additional resources MS data exist, spectra libraries databases, specific file formats sometimes support manipulations original data. Learning experiences r Biocpkg(\"MSnbase\") Bioconductor package developed novel infrastructure handle MS spectral data R, r Biocpkg(\"Spectra\") package. package implements clear separation user functionality code provide, store import mass spectrometry data. Different backends can hence used enable access data various data resources. Depending backend, data representations can memory, disk, local remote. Data manipulations default directly applied data cached lazy processing queue allows analyses also read-data representations. workshop shows new infrastructure can used integrate data variety different input sources simple example fragment (MS2) spectra experiment matched reference MS2 spectra public spectral library. Results finally exported format commonly used exchange MS2 data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/Abstract-EuroBioc2023.html","id":null,"dir":"","previous_headings":"","what":"Spectra: an expandable infrastructure to handle mass spectrometry data","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Mass spectrometry (MS) data key technology modern metabolomics proteomics experiments. Continuous improvements MS instrumentation, larger experiments new technological developments lead ever growing data sizes increased number available variables making standard -memory data handling processing difficult. Spectra package provides modern infrastructure MS data handling specifically designed enable extension additional data resources alternative data representations. can realized extending virtual MsBackend class related methods. Implementations MsBackend classes can tailored specific needs, low memory footprint, fast processing, remote data access, also support specific additional data types variables. Importantly, data processing Spectra objects independent backend use due lazy evaluation mechanism caches data manipulations internally. workshop discusses different available data representations MS data along properties, advantages performances. addition, Spectra’s concept lazy evaluation data manipulations presented, well simple caching mechanism data modifications. Finally, explains new MsBackend instances can implemented tested ensure compliance. Describe: - Concept backends. - Loading MS data. - Backends, -disk, -memory. - Using MsBackendSql store data. - Use MsBackendMassbankSql access data: parallel processing possible. - Different properties backends. - Lazy processing caching.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"abstract","dir":"Articles","previous_headings":"","what":"Abstract","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Mass spectrometry (MS) data key technology modern metabolomics proteomics experiments. Continuous improvements MS instrumentation, larger experiments new technological developments lead ever growing data sizes increased number available variables making standard -memory data handling processing difficult. Spectra package provides modern infrastructure MS data handling specifically designed enable extension additional data resources alternative data representations. can realized extending virtual MsBackend class related methods. Implementations MsBackend classes can tailored specific needs, low memory footprint, fast processing, remote data access, also support specific additional data types variables. Importantly, data processing Spectra objects independent backend use due lazy evaluation mechanism caches data manipulations internally. workshop discusses different available data representations MS data along properties, advantages performances. addition, Spectra’s concept lazy evaluation data manipulations presented, well simple caching mechanism data modifications. Finally, explains new MsBackend instances can implemented tested ensure compliance.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"workshop/tutorial assumes readers familiar mass spectrometry data. See LC-MS/MS nutshell section Seamless Integration Mass Spectrometry Data Different Sources vignette package general introduction MS.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"pre-requisites","dir":"Articles","previous_headings":"Introduction","what":"Pre-requisites","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Basic familiarity R Bioconductor. Basic understanding Mass Spectrometry (MS) data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"installation","dir":"Articles","previous_headings":"Introduction","what":"Installation","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"version tutorial bases package versions available Bioconductor release 3.19. Get docker image tutorial docker pull jorainer/spectra_tutorials:RELEASE_3_19. Start docker using Enter http://localhost:8787 web browser log username rstudio password bioc. Open R-markdown file (vignettes/Spectra-backends.Rmd) RStudio server version web browser evaluate R code blocks.","code":"docker run \\     -e PASSWORD=bioc \\     -p 8787:8787 \\     jorainer/spectra_tutorials:RELEASE_3_19"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"rbioconductor-packages-used","dir":"Articles","previous_headings":"Introduction","what":"R/Bioconductor packages used","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Spectra MsBackendSql","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"workshop-goals-and-objectives","dir":"Articles","previous_headings":"Introduction","what":"Workshop goals and objectives","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"technical demonstration internals Spectra package design MS infrastructure. ’re demonstrating use cases analysis workflows .","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"learning-goals","dir":"Articles","previous_headings":"Introduction > Workshop goals and objectives","what":"Learning goals","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Understand MS data handled Spectra. Understand differences properties different MsBackend implementations.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"learning-objectives","dir":"Articles","previous_headings":"Introduction > Workshop goals and objectives","what":"Learning objectives","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Learn MS data handled Spectra. Understand data representations/backends fit purpose/use case. Insights internals Spectra MS infrastructure facilitate implementation backend(s).","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"the-spectra-package","dir":"Articles","previous_headings":"Workshop","what":"The Spectra package","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Purpose: provide expandable, well tested user-friendly infrastructure mass spectrometry (MS) data. Design: separation code representing main user interface code provide, store read MS data. Spectra: main interface end user. MsBackend: defines data stored managed. user need care data stored. functionality can applied MS data, regardless data stored. enables specialized data storage/representation options: remote data, low memory footprint, high performance.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"creating-and-using-spectra-objects","dir":"Articles","previous_headings":"Workshop","what":"Creating and using Spectra objects","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"MS data consists duplets mass--charge (m/z) intensity values along potential additional information, MS level, retention time, polarity etc. simplest form, MS data consists thus two (aligned) numeric vectors m/z intensity values e.g. data.frame containing potential additional annotations MS spectrum. Spectra terminology, \"mz\" \"intensity\" called peak variables (provide information individual mass peaks), annotations spectra variables (usually single value per spectrum). define m/z intensity values mass spectrum well data.frame additional spectra variables. basic representation MS data R. obvious reasons however better store data single container keeping separate, unrelated, variables. thus create Spectra object MS data. Spectra objects can created Spectra constructor function accepts (among possibilities) data.frame full MS data input. row data.frame expected contain data one spectrum. therefore need add m/z intensity values list numerical vectors data frame. now created Spectra object representing toy MS data. Individual spectra variables can accessed either $ name spectra variable, one dedicated accessor functions. Also, provided spectra variables \"msLevel\" \"polarity\" input data frame, variables available default Spectra object. called core spectra variables can always extracted Spectra object, even defined (case missing values returned). Spectra variables available Spectra object can listed spectraVariables() function: ’ve now got Spectra object representing single MS spectrum - , name class implies, actually designed represent data multiple mass spectra (possibly whole experiment). thus define data.frame, time 3 rows, create Spectra object . Also, define additional spectra variables providing name ID compounds MS2 (fragment) spectrum represents. Now Spectra object representing 3 mass spectra set spectra peak variables. Spectra organized linearly, .e., one . Thus, Spectra length 3 element one spectrum. Subsetting works R object: Spectra behaves similar data.frame elements (rows) individual spectra columns spectra (peaks) variables, can accessed $ operator. Similar data.frame can also add new spectra variables (columns) using $<-. full peak data can extracted peaksData() function, returns list two-dimensional arrays values peak variables. list element represents peak data one spectrum, stored e.g. matrix columns peak variables rows respective values peak. number rows peak variable arrays depends number mass peaks spectrum. number can extracted using lengths(): next extract peaks’ data Spectra subset data second spectrum. Finally, can also visualize peak data spectrum Spectra.  plots also created manually m/z intensity values, built-functions cases efficient.  Experimental MS data generally (ideally) stored files mzML, mzXML CDF format. open file formats can read software. Bioconductor, mzR package allows read/write data /formats. Also Spectra allows represent data raw data files. illustrate create Spectra object two mzML files provided Bioconductor’s msdata package. thus access raw experimental MS data Spectra object. contrast first example , used Spectra constructor providing character vector file names specified source = MsBackendMzR(). anticipates concept backends Spectra package: source parameter Spectra call defines backend use import (represent) data, example MsBackendMzR enables import data mzML files. Like first example, can subset object access spectra variables. Note however Spectra object many spectra variables available (depending provided raw data files). can visualize data.","code":"#' Define simple MS data mz <- c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,         111.0551, 123.0429, 138.0662, 195.0876) int <- c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994)  sv <- data.frame(msLevel = 1L, polarity = 1L) library(Spectra)  #' wrap m/z and intensities into a list and add them to the data.frame sv$mz <- list(mz) sv$intensity <- list(int)  #' Create a `Spectra` object from the data. s <- Spectra(sv) s ## MSn data (Spectra) with 1 spectra in a MsBackendMemory backend: ##     msLevel     rtime scanIndex ##   <integer> <numeric> <integer> ## 1         1        NA        NA ##  ... 16 more variables/columns. #' Access the MS level spectra variable s$msLevel ## [1] 1 msLevel(s) ## [1] 1 #' List all available spectra variables spectraVariables(s) ##  [1] \"msLevel\"                 \"rtime\"                   ##  [3] \"acquisitionNum\"          \"scanIndex\"               ##  [5] \"dataStorage\"             \"dataOrigin\"              ##  [7] \"centroided\"              \"smoothed\"                ##  [9] \"polarity\"                \"precScanNum\"             ## [11] \"precursorMz\"             \"precursorIntensity\"      ## [13] \"precursorCharge\"         \"collisionEnergy\"         ## [15] \"isolationWindowLowerMz\"  \"isolationWindowTargetMz\" ## [17] \"isolationWindowUpperMz\" #' Extract the retention time spectra variable s$rtime ## [1] NA #' Define spectra variables for 3 MS spectra. sv <- data.frame(     msLevel = c(2L, 2L, 2L),     polarity = c(1L, 1L, 1L),     id = c(\"HMDB0000001\", \"HMDB0000001\", \"HMDB0001847\"),     name = c(\"1-Methylhistidine\", \"1-Methylhistidine\", \"Caffeine\"))  #' Assign m/z and intensity values. sv$mz <- list(     c(109.2, 124.2, 124.5, 170.16, 170.52),     c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),     c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,       111.0551, 123.0429, 138.0662, 195.0876)) sv$intensity <- list(     c(3.407, 47.494, 3.094, 100.0, 13.240),     c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),     c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))  #' Create a Spectra from this data. s <- Spectra(sv) s ## MSn data (Spectra) with 3 spectra in a MsBackendMemory backend: ##     msLevel     rtime scanIndex ##   <integer> <numeric> <integer> ## 1         2        NA        NA ## 2         2        NA        NA ## 3         2        NA        NA ##  ... 18 more variables/columns. #' List available spectra and peaks variables. spectraVariables(s) ##  [1] \"msLevel\"                 \"rtime\"                   ##  [3] \"acquisitionNum\"          \"scanIndex\"               ##  [5] \"dataStorage\"             \"dataOrigin\"              ##  [7] \"centroided\"              \"smoothed\"                ##  [9] \"polarity\"                \"precScanNum\"             ## [11] \"precursorMz\"             \"precursorIntensity\"      ## [13] \"precursorCharge\"         \"collisionEnergy\"         ## [15] \"isolationWindowLowerMz\"  \"isolationWindowTargetMz\" ## [17] \"isolationWindowUpperMz\"  \"id\"                      ## [19] \"name\" peaksVariables(s) ## [1] \"mz\"        \"intensity\" length(s) ## [1] 3 #' Extract the 2nd spectrum s[2] ## MSn data (Spectra) with 1 spectra in a MsBackendMemory backend: ##     msLevel     rtime scanIndex ##   <integer> <numeric> <integer> ## 1         2        NA        NA ##  ... 18 more variables/columns. s$msLevel ## [1] 2 2 2 #' Add a new spectra variable. s$new_variable <- c(\"a\", \"b\", \"c\") spectraVariables(s) ##  [1] \"msLevel\"                 \"rtime\"                   ##  [3] \"acquisitionNum\"          \"scanIndex\"               ##  [5] \"dataStorage\"             \"dataOrigin\"              ##  [7] \"centroided\"              \"smoothed\"                ##  [9] \"polarity\"                \"precScanNum\"             ## [11] \"precursorMz\"             \"precursorIntensity\"      ## [13] \"precursorCharge\"         \"collisionEnergy\"         ## [15] \"isolationWindowLowerMz\"  \"isolationWindowTargetMz\" ## [17] \"isolationWindowUpperMz\"  \"id\"                      ## [19] \"name\"                    \"new_variable\" #' Get the number of peaks per spectrum. lengths(s) ## [1] 5 7 9 #' Extract the peaks matrix of the second spectrum peaksData(s)[[2]] ##          mz intensity ## [1,]  83.10     6.685 ## [2,]  96.12     4.381 ## [3,]  97.14     3.022 ## [4,] 109.14    16.708 ## [5,] 124.08   100.000 ## [6,] 125.10     4.565 ## [7,] 170.16    40.643 #' Plot the second spectrum. plotSpectra(s[2]) plot(mz(s)[[2]], intensity(s)[[2]], type = \"h\",      xlab = \"m/z\", ylab = \"intensity\") #' Import MS data from 2 mzML files. fls <- dir(system.file(\"sciex\", package = \"msdata\"), full.names = TRUE) s2 <- Spectra(fls, source = MsBackendMzR()) s2 ## MSn data (Spectra) with 1862 spectra in a MsBackendMzR backend: ##        msLevel     rtime scanIndex ##      <integer> <numeric> <integer> ## 1            1     0.280         1 ## 2            1     0.559         2 ## 3            1     0.838         3 ## 4            1     1.117         4 ## 5            1     1.396         5 ## ...        ...       ...       ... ## 1858         1   258.636       927 ## 1859         1   258.915       928 ## 1860         1   259.194       929 ## 1861         1   259.473       930 ## 1862         1   259.752       931 ##  ... 33 more variables/columns. ##  ## file(s): ## 20171016_POOL_POS_1_105-134.mzML ## 20171016_POOL_POS_3_105-134.mzML #' Available spectra variables spectraVariables(s2) ##  [1] \"msLevel\"                  \"rtime\"                    ##  [3] \"acquisitionNum\"           \"scanIndex\"                ##  [5] \"dataStorage\"              \"dataOrigin\"               ##  [7] \"centroided\"               \"smoothed\"                 ##  [9] \"polarity\"                 \"precScanNum\"              ## [11] \"precursorMz\"              \"precursorIntensity\"       ## [13] \"precursorCharge\"          \"collisionEnergy\"          ## [15] \"isolationWindowLowerMz\"   \"isolationWindowTargetMz\"  ## [17] \"isolationWindowUpperMz\"   \"peaksCount\"               ## [19] \"totIonCurrent\"            \"basePeakMZ\"               ## [21] \"basePeakIntensity\"        \"ionisationEnergy\"         ## [23] \"lowMZ\"                    \"highMZ\"                   ## [25] \"mergedScan\"               \"mergedResultScanNum\"      ## [27] \"mergedResultStartScanNum\" \"mergedResultEndScanNum\"   ## [29] \"injectionTime\"            \"filterString\"             ## [31] \"spectrumId\"               \"ionMobilityDriftTime\"     ## [33] \"scanWindowLowerLimit\"     \"scanWindowUpperLimit\" #' Access the MS levels for the first 6 spectra s2$msLevel |> head() ## [1] 1 1 1 1 1 1 #' Plot the 4th spectrum. plotSpectra(s2[4])"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"use-of-different-backends-with-spectra-why-and-how","dir":"Articles","previous_headings":"Workshop","what":"Use of different backends with Spectra, why and how?","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Spectra objects behave way, .e. data can accessed fashion, actually use different backends thus data representations: first example Spectra uses MsBackendMemory backend , name tells, keeps MS data memory (within backend object). contrast, second Spectra represents experimental MS data two mzML files uses MsBackendMzR backend. backend loads general spectra data (variables) memory imports peak data -demand original data files needed/requested (similar -disk mode discussed (Gatto, Gibb, Rainer 2020)). advantage offline storage mode lower memory footprint enables also analysis large data experiments standard computers. different backends? Reason 1: support additional file formats. backend class defines functionality import/export data /specific file formats. Spectra class stays data storage agnostic e.g. import/export routines need implemented class. Support additional file formats can implemented independently Spectra package can contributed different developers. Examples: MsBackendMgf (defined MsBackendMgf) adds support MS files MGF file format. MsBackendMsp (defined MsBackendMsp adds support MSP files. MsBackendRawFileReader (defined MsBackendRawFileReader) adds support MS data files Thermo Fisher Scientific’s raw data file format. import data files certain file format, respective backend able handle data needs specified parameter source Spectra constructor call: Reason 2: support different implementations store represent data. includes data stored stored. Specialized backends can defined , example, optimized high performance low memory demand. Examples: MsBackendMemory (defined Spectra): keeps MS data memory optimized fast efficient access peaks data matrices (.e., m/z intensity values spectra). MsBackendMzR (defined Spectra): keeps spectra variables memory retrieves peaks data --fly original data files upon request. guarantees lower memory footprint enables also analysis large MS experiments. MsBackendSql (defined MsBackendSql package): MS data stored SQL database. minimal memory requirements data (whether spectra peaks variables) need retrieved database. Depending SQL database system used, backend also allow remote data access. evaluate illustrate properties different backends create Spectra objects data, using different backends. use setBackend function change backend Spectra object. call loaded MS data original data files memory. third option next store full MS data SQL database using/changing MsBackendOfflineSql backend defined MsBackendSql package. setBackend() call need provide connection information database contain data. includes database driver (parameter drv, depending database system), database name (parameter dbname) well eventual additional connection information like host, username, port password. parameters required depends SQL database used hence driver (see also ?dbConnect information parameters). example store data SQLite database. thus set drv = SQLite() provide parameter dbname name SQLite database file (yet exist). addition, importantly, disable parallel processing BPPARAM = SerialParam() since SQL databases don’t support parallel data import. Note: efficient way import MS data data files SQL database createMsBackendSqlDatabase() MsBackendSql package. Also, larger data sets suggested use advanced powerful SQL database systems (e.g. MySQL/MariaDB SQL databases). now 3 Spectra objects representing MS data, using different backends. first comparison evaluate size memory: expected, size Spectra object MsBackendMemory backend largest three. Since MsBackendOfflineSql backend keeps primary keys spectra memory ’s memory requirements particularly small thus ideal represent even large MS experiments. next evaluate performance extract spectra variables. compare time needed extract retention times 3 Spectra objects using microbenchmark() function. register(SerialParam()) globally disable parallel processing Spectra ensure results independent . Highest performance can seen Spectra object MsBackendMemory backend, followed Spectra object MsBackendMzR backend, extraction spectra variables slowest Spectra MsBackendOfflineSql. , expected MsBackendOfflineSql needs retrieve data database two backends keep retention times spectra (along general spectra metadata) memory. next also evaluate performance extract peaks data, .e. individual m/z intensity values spectra two files. MsBackendMemory outperforms two backends also comparison. MsBackendMzR MsBackendOfflineSql need import data, MsBackendMzR original mzML files MsBackendOfflineSql SQL database (present data efficient). last evaluate performance subset 3 Spectra objects 10 random spectra. , MsBackendOfflineSql clear advantage two backends, needs subset integer vector primary keys (data keeps memory), MsBackendMemory needs subset full data, MsBackendMzR data frame spectra variables.","code":"class(s@backend) ## [1] \"MsBackendMemory\" ## attr(,\"package\") ## [1] \"Spectra\" class(s2@backend) ## [1] \"MsBackendMzR\" ## attr(,\"package\") ## [1] \"Spectra\" #' Import MS data from mzML files s_mzr <- Spectra(fls, source = MsBackendMzR()) #' Change the backend to MsBackendMemory s_mem <- setBackend(s_mzr, MsBackendMemory()) #' Change the backend to a SQL representation of the data. library(MsBackendSql) library(RSQLite) s_db <- setBackend(s_mzr, MsBackendOfflineSql(), drv = SQLite(),                    dbname = \"ms_backend_sql_test.sqlite\",                    BPPARAM = SerialParam()) #' Compare memory footprint. print(object.size(s_mem), units = \"MB\") ## 53.2 Mb print(object.size(s_mzr), units = \"MB\") ## 0.4 Mb print(object.size(s_db), units = \"MB\") ## 0.1 Mb #' Compare performance to extract the retention times. register(SerialParam()) library(microbenchmark) microbenchmark(     rtime(s_mem),     rtime(s_mzr),     rtime(s_db),     times = 13 ) ## Unit: microseconds ##          expr      min       lq     mean   median       uq       max neval ##  rtime(s_mem)   11.382   11.993   21.114   15.088   30.166    33.803    13 ##  rtime(s_mzr)  373.938  401.499  477.649  437.677  467.292   894.068    13 ##   rtime(s_db) 6960.776 7191.957 7600.690 7263.411 7319.565 11846.993    13 #' Compare performance to extract the peaks data. microbenchmark(     peaksData(s_mem),     peaksData(s_mzr),     peaksData(s_db),     times = 7 ) ## Unit: microseconds ##              expr        min          lq        mean     median          uq ##  peaksData(s_mem)    322.071    514.7805    618.1203    631.118    746.0615 ##  peaksData(s_mzr) 506008.305 507137.8500 544841.2223 518705.426 519729.7670 ##   peaksData(s_db)  98960.197 107680.4265 230061.3011 315034.282 322345.5675 ##         max neval ##     851.969     7 ##  735439.591     7 ##  336382.641     7 #' Compare performance to subset the Spectra. idx <- sample(seq_along(s_mem), 10) microbenchmark(     s_mem[idx],     s_mzr[idx],     s_db[idx] ) ## Unit: microseconds ##        expr     min       lq     mean   median       uq      max neval ##  s_mem[idx] 229.318 242.9035 251.5439 250.0265 255.5270  348.079   100 ##  s_mzr[idx] 655.824 674.9290 698.9652 688.8905 707.5500 1134.296   100 ##   s_db[idx] 135.533 148.0010 169.9489 153.9575 161.9975 1632.686   100"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"performance-considerations-and-tweaks","dir":"Articles","previous_headings":"Workshop > Use of different backends with Spectra, why and how?","what":"Performance considerations and tweaks","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Keeping data memory (e.g. using MsBackendMemory) obvious performance advantages, might possible MS experiments. -disk backends MsBackendMzR MsBackendOfflineSql allow, due lower memory footprint, analyze also large data sets. optimized build-option Spectra methods load process MS data small chunks contrast loading processing full data whole. performance offline backends MsBackendMzR MsBackendSql/MsBackendOfflineSql depend also /O capabilities hard disks containing data files , latter, addition configuration SQL database system used.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"data-manipulation-and-the-lazy-evaluation-queue","dir":"Articles","previous_headings":"Workshop","what":"Data manipulation and the lazy evaluation queue","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"data representations might, design way data handled stored, allow changing existing adding new values. MsBackendMzR backend example retrieves peaks data directly raw data files don’t want manipulation m/z intensity values directly written back original data files. Also backends, specifically provide access spectral reference databases (MsBackendMassbankSql MsBackendMassbank package MsBackendCompDb CompoundDb package), supposed allow changes information stored databases. Data manipulations however integral part data analysis thus need backends mechanism allows changes data without propagating original data resource. backend classes caching mechanism implemented enables adding, changing, deleting spectra variables. illustrate add new spectra variable \"new_variable\" 3 Spectra objects. new spectra variable now added 3 Spectra objects: MsBackendMemory stores spectra variables data.frame within object. new spectra variable thus simply added new column data.frame. Warning: NEVER access manipulate slots backend class directly like can easily result data corruption. Similarly, also MsBackendMzR stores data spectra variables (peaks variables!) within DataFrame inside object. Adding new spectra variable thus also added new column DataFrame. Note: use DataFrame instead data.frame MsBackendMzR explains performance differences subset access spectra variables ’ve seen backend MsBackendMemory . MsBackendOfflineSql implements support changes spectra variables differently: object contains vector available database column names addition (initially empty) data.frame cache changes spectra variables. spectra variable deleted, respective column name removed character vector available column names. new spectra variable added (like example ) values existing spectra variable changed, spectra variable, respectively values, stored (new column) caching data frame: Actually, want change data directly database? fairly simple add new column database table change values . actually two reasons : firstly don’t want change raw data might stored database (e.g. database contains reference MS2 spectra, case Spectra MassBank data) secondly, want avoid following situation: imagine create copy s_db variable change retention times variable: Writing changes back database also change retention time values original s_db variable obvious reasons desired. Note can also used MsBackendOfflineSql backends cache spectra variables memory faster access. Note: MsBackendCached class Spectra package provides internal data.frame-based caching mechanism described . MsBackendOfflineSql class directly extends backend hence inherits mechanism database-specific additional functionality needed implemented. Thus, MsBackend implementation extending MsBackendCached base class automatically inherit caching mechanism need implement . Examples backends extending MsBackendCached , among others, MsBackendSql, MsBackendOfflineSql, MsBackendMassbankSql. Analysis MS data requires, addition changing adding spectra variables also possibility manipulate peaks’ data (.e. m/z intensity values individual mass peaks spectra). simple example remove mass peaks intensity 100 data set compare number peaks per spectra operation.  operation reduce number peaks considerably. repeat operation now also two backends. number peaks also reduced two backends although keep peak data memory , discussed , allow changes data file (database) containing original data. caching mechanisms described work well spectra variables (generally) limited manageable sizes. MS peaks data (m/z intensity values individual peaks MS spectra) however represent much larger data volumes preventing cache store changes directly -memory, especially data large experiments high resolution instruments. reason, instead caching changes values within object, cache actual data manipulation operation(s). function modify peaks data, along parameters, automatically added internal lazy processing queue within Spectra object call one Spectra’s (peaks) data manipulation functions. mechanism implemented directly Spectra class backends thus implement . calling filterIntensity() , data actually modified, function filter peaks data added processig queue. function along possibly defined parameters added ProcessingStep object: , advisable directly access internal slots Spectra object! function accessed : parameters : time peaks data accessed (like intensity() call example ), Spectra object first request raw peaks data backend, check processing queue , empty, apply contained cached processing steps peaks data returning user. nice side effect, subset data accessed, data manipulations need applied requested data subset, full data. can positive impact overall performance: Next number common peaks data manipulation methods already implemented Spectra, make use processing queue, also addProcessing() function allows apply user-provided function peaks data (using lazy evaluation mechanism). simple example define function scales intensities spectrum total intensity sum per spectrum 1. Functions addProcessing() expected take peaks array input return peaks array result. Note also ... function definition required, internally additional parameters, spectrum’s MS level, default passed along function (see also addProcessing() documentation entry ?Spectra information advanced examples). validation calculate sum intensities first 6 spectra. intensities thus scaled total intensity sum per spectra 1. Note , common operation MS2 data (fragment spectra), scaling function generally used (quantitative) MS1 data (example ). Finally, since lazy evaluation mechanism changing actual peaks data, can also undo data manipulations. simple reset() call Spectra object restore data object initial state (fact simply clears processing queue). Along potential chaching mechanisms spectra variables, implementation lazy-evaluation queue allows apply also data manipulations MS data data resources inherently read-(reference libraries fragment spectra) thus enable analysis MS data independently data stored.","code":"#' Assign new spectra variables. s_mem$new_variable <- seq_along(s_mem) s_mzr$new_variable <- seq_along(s_mem) s_db$new_variable <- seq_along(s_mem) #' Show spectra variables for one of the Spectra spectraVariables(s_mzr) ##  [1] \"msLevel\"                  \"rtime\"                    ##  [3] \"acquisitionNum\"           \"scanIndex\"                ##  [5] \"dataStorage\"              \"dataOrigin\"               ##  [7] \"centroided\"               \"smoothed\"                 ##  [9] \"polarity\"                 \"precScanNum\"              ## [11] \"precursorMz\"              \"precursorIntensity\"       ## [13] \"precursorCharge\"          \"collisionEnergy\"          ## [15] \"isolationWindowLowerMz\"   \"isolationWindowTargetMz\"  ## [17] \"isolationWindowUpperMz\"   \"peaksCount\"               ## [19] \"totIonCurrent\"            \"basePeakMZ\"               ## [21] \"basePeakIntensity\"        \"ionisationEnergy\"         ## [23] \"lowMZ\"                    \"highMZ\"                   ## [25] \"mergedScan\"               \"mergedResultScanNum\"      ## [27] \"mergedResultStartScanNum\" \"mergedResultEndScanNum\"   ## [29] \"injectionTime\"            \"filterString\"             ## [31] \"spectrumId\"               \"ionMobilityDriftTime\"     ## [33] \"scanWindowLowerLimit\"     \"scanWindowUpperLimit\"     ## [35] \"new_variable\" #' Direct access to the backend's data. s_mem@backend@spectraData |> head() ##   msLevel rtime acquisitionNum scanIndex dataStorage ## 1       1 0.280              1         1    <memory> ## 2       1 0.559              2         2    <memory> ## 3       1 0.838              3         3    <memory> ## 4       1 1.117              4         4    <memory> ## 5       1 1.396              5         5    <memory> ## 6       1 1.675              6         6    <memory> ##                                                                    dataOrigin ## 1 /usr/local/lib/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML ## 2 /usr/local/lib/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML ## 3 /usr/local/lib/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML ## 4 /usr/local/lib/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML ## 5 /usr/local/lib/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML ## 6 /usr/local/lib/R/site-library/msdata/sciex/20171016_POOL_POS_1_105-134.mzML ##   centroided smoothed polarity precScanNum precursorMz precursorIntensity ## 1      FALSE       NA        1          NA          NA                 NA ## 2      FALSE       NA        1          NA          NA                 NA ## 3      FALSE       NA        1          NA          NA                 NA ## 4      FALSE       NA        1          NA          NA                 NA ## 5      FALSE       NA        1          NA          NA                 NA ## 6      FALSE       NA        1          NA          NA                 NA ##   precursorCharge collisionEnergy isolationWindowLowerMz ## 1              NA              NA                     NA ## 2              NA              NA                     NA ## 3              NA              NA                     NA ## 4              NA              NA                     NA ## 5              NA              NA                     NA ## 6              NA              NA                     NA ##   isolationWindowTargetMz isolationWindowUpperMz peaksCount totIonCurrent ## 1                      NA                     NA        578        898185 ## 2                      NA                     NA       1529       1037012 ## 3                      NA                     NA       1600       1094971 ## 4                      NA                     NA       1664       1135015 ## 5                      NA                     NA       1417       1106233 ## 6                      NA                     NA       1602       1181489 ##   basePeakMZ basePeakIntensity ionisationEnergy    lowMZ   highMZ mergedScan ## 1   124.0860            154089                0 105.0435 133.9837         NA ## 2   124.0859            182690                0 105.0275 133.9836         NA ## 3   124.0859            196650                0 105.0376 133.9902         NA ## 4   124.0859            203502                0 105.0376 133.9853         NA ## 5   124.0859            191715                0 105.0347 133.9885         NA ## 6   124.0859            213400                0 105.0420 133.9836         NA ##   mergedResultScanNum mergedResultStartScanNum mergedResultEndScanNum ## 1                  NA                       NA                     NA ## 2                  NA                       NA                     NA ## 3                  NA                       NA                     NA ## 4                  NA                       NA                     NA ## 5                  NA                       NA                     NA ## 6                  NA                       NA                     NA ##   injectionTime filterString                             spectrumId ## 1             0         <NA> sample=1 period=1 cycle=1 experiment=1 ## 2             0         <NA> sample=1 period=1 cycle=2 experiment=1 ## 3             0         <NA> sample=1 period=1 cycle=3 experiment=1 ## 4             0         <NA> sample=1 period=1 cycle=4 experiment=1 ## 5             0         <NA> sample=1 period=1 cycle=5 experiment=1 ## 6             0         <NA> sample=1 period=1 cycle=6 experiment=1 ##   ionMobilityDriftTime scanWindowLowerLimit scanWindowUpperLimit new_variable ## 1                   NA                   NA                   NA            1 ## 2                   NA                   NA                   NA            2 ## 3                   NA                   NA                   NA            3 ## 4                   NA                   NA                   NA            4 ## 5                   NA                   NA                   NA            5 ## 6                   NA                   NA                   NA            6 s_mzr@backend@spectraData ## DataFrame with 1862 rows and 26 columns ##      scanIndex acquisitionNum   msLevel  polarity peaksCount totIonCurrent ##      <integer>      <integer> <integer> <integer>  <integer>     <numeric> ## 1            1              1         1         1        578        898185 ## 2            2              2         1         1       1529       1037012 ## 3            3              3         1         1       1600       1094971 ## 4            4              4         1         1       1664       1135015 ## 5            5              5         1         1       1417       1106233 ## ...        ...            ...       ...       ...        ...           ... ## 1858       927            927         1         1       3528        418159 ## 1859       928            928         1         1       3662        429930 ## 1860       929            929         1         1       3442        413427 ## 1861       930            930         1         1       3564        447033 ## 1862       931            931         1         1       3517        441025 ##          rtime basePeakMZ basePeakIntensity ionisationEnergy     lowMZ ##      <numeric>  <numeric>         <numeric>        <numeric> <numeric> ## 1        0.280    124.086            154089                0   105.043 ## 2        0.559    124.086            182690                0   105.028 ## 3        0.838    124.086            196650                0   105.038 ## 4        1.117    124.086            203502                0   105.038 ## 5        1.396    124.086            191715                0   105.035 ## ...        ...        ...               ...              ...       ... ## 1858   258.636    123.090             28544                0   105.008 ## 1859   258.915    123.090             29164                0   105.009 ## 1860   259.194    123.090             28724                0   105.010 ## 1861   259.473    123.091             28743                0   105.012 ## 1862   259.752    123.091             27503                0   105.012 ##         highMZ mergedScan mergedResultScanNum mergedResultStartScanNum ##      <numeric>  <integer>           <integer>                <integer> ## 1      133.984         NA                  NA                       NA ## 2      133.984         NA                  NA                       NA ## 3      133.990         NA                  NA                       NA ## 4      133.985         NA                  NA                       NA ## 5      133.989         NA                  NA                       NA ## ...        ...        ...                 ...                      ... ## 1858   134.000         NA                  NA                       NA ## 1859   134.000         NA                  NA                       NA ## 1860   134.000         NA                  NA                       NA ## 1861   133.998         NA                  NA                       NA ## 1862   133.997         NA                  NA                       NA ##      mergedResultEndScanNum injectionTime filterString             spectrumId ##                   <integer>     <numeric>  <character>            <character> ## 1                        NA             0           NA sample=1 period=1 cy.. ## 2                        NA             0           NA sample=1 period=1 cy.. ## 3                        NA             0           NA sample=1 period=1 cy.. ## 4                        NA             0           NA sample=1 period=1 cy.. ## 5                        NA             0           NA sample=1 period=1 cy.. ## ...                     ...           ...          ...                    ... ## 1858                     NA             0           NA sample=1 period=1 cy.. ## 1859                     NA             0           NA sample=1 period=1 cy.. ## 1860                     NA             0           NA sample=1 period=1 cy.. ## 1861                     NA             0           NA sample=1 period=1 cy.. ## 1862                     NA             0           NA sample=1 period=1 cy.. ##      centroided ionMobilityDriftTime scanWindowLowerLimit scanWindowUpperLimit ##       <logical>            <numeric>            <numeric>            <numeric> ## 1         FALSE                   NA                   NA                   NA ## 2         FALSE                   NA                   NA                   NA ## 3         FALSE                   NA                   NA                   NA ## 4         FALSE                   NA                   NA                   NA ## 5         FALSE                   NA                   NA                   NA ## ...         ...                  ...                  ...                  ... ## 1858      FALSE                   NA                   NA                   NA ## 1859      FALSE                   NA                   NA                   NA ## 1860      FALSE                   NA                   NA                   NA ## 1861      FALSE                   NA                   NA                   NA ## 1862      FALSE                   NA                   NA                   NA ##                 dataStorage             dataOrigin new_variable ##                 <character>            <character>    <integer> ## 1    /usr/local/lib/R/sit.. /usr/local/lib/R/sit..            1 ## 2    /usr/local/lib/R/sit.. /usr/local/lib/R/sit..            2 ## 3    /usr/local/lib/R/sit.. /usr/local/lib/R/sit..            3 ## 4    /usr/local/lib/R/sit.. /usr/local/lib/R/sit..            4 ## 5    /usr/local/lib/R/sit.. /usr/local/lib/R/sit..            5 ## ...                     ...                    ...          ... ## 1858 /usr/local/lib/R/sit.. /usr/local/lib/R/sit..         1858 ## 1859 /usr/local/lib/R/sit.. /usr/local/lib/R/sit..         1859 ## 1860 /usr/local/lib/R/sit.. /usr/local/lib/R/sit..         1860 ## 1861 /usr/local/lib/R/sit.. /usr/local/lib/R/sit..         1861 ## 1862 /usr/local/lib/R/sit.. /usr/local/lib/R/sit..         1862 s_db@backend@localData |> head() ##   new_variable ## 1            1 ## 2            2 ## 3            3 ## 4            4 ## 5            5 ## 6            6 #' Change values in a copy of the variable s_db2 <- s_db rtime(s_db2) <- rtime(s_db2) + 10 #' Retention times of original copy should NOT change rtime(s_db) |> head() ## [1] 0.280 0.559 0.838 1.117 1.396 1.675 rtime(s_db2) |> head() ## [1] 10.280 10.559 10.838 11.117 11.396 11.675 s_db2 <- s_db  #' Cache an existing spectra variable in memory s_db2$rtime <- rtime(s_db2)  microbenchmark(     rtime(s_db),     rtime(s_db2),     times = 17 ) ## Unit: milliseconds ##          expr      min       lq     mean   median       uq      max neval ##   rtime(s_db) 7.126546 7.206836 7.404466 7.287036 7.411337 8.363142    17 ##  rtime(s_db2) 3.103891 3.168001 3.411078 3.298283 3.410914 4.949414    17 #' Remove all peaks with an intensity below 100 s_mem <- filterIntensity(s_mem, intensity = 100)  #' Compare the number of peaks before/after boxplot(list(before = lengths(s_mzr), after = lengths(s_mem)),         ylab = \"number of peaks\") #' Repeast filtering for the two other Spectra objects. s_mzr <- filterIntensity(s_mzr, intensity = 100) s_db <- filterIntensity(s_db, intensity = 100) #' Evaluate that subsetting worked for all. median(lengths(s_mem)) ## [1] 433 median(lengths(s_mzr)) ## [1] 433 median(lengths(s_db)) ## [1] 433 #' Show the processing queue. s_db@processingQueue ## [[1]] ## Object of class \"ProcessingStep\" ##  Function: user-provided function ##  Arguments: ##   o intensity = 100Inf ##   o msLevel = 1 #' Access a processing step's function s_db@processingQueue[[1L]]@FUN ## function (x, spectrumMsLevel, intensity = c(0, Inf), msLevel = spectrumMsLevel,  ##     ...)  ## { ##     if (!spectrumMsLevel %in% msLevel || !length(x))  ##         return(x) ##     x[which(between(x[, \"intensity\"], intensity)), , drop = FALSE] ## } ## <bytecode: 0x55d51d043af0> ## <environment: namespace:Spectra> #' Access the parameters for that function s_db@processingQueue[[1L]]@ARGS ## $intensity ## [1] 100 Inf ##  ## $msLevel ## [1] 1 #' Access intensity values and extract those of the 1st spectrum. intensity(s_db)[[1L]] ##   [1]    412    412    412    412    412    412    412    412    412   1237 ##  [11]    412    412    412    412    412    412    412    412    412    412 ##  [21]    412    412    824    412    412    412    412    412    412    412 ##  [31]    412    412    412    412    412    412   1237    412    412    412 ##  [41]    412    412    412    412    412    412    412    412    412    412 ##  [51]    824    412    412    412    412    412    412    412    824   1649 ##  [61]   2061   1237    412    412    412    412    412    412   2061   7008 ##  [71]  17727  22674  23910  12780   6184   3710   2061   1237    412    412 ##  [81]    412   1662    412   1258    861    854    845    873    842    412 ##  [91]    860   1307    882    442  12347  55349 132662 154089 115667  76817 ## [101]  41233  26904  11577   6155   2650   3966    872    461    461    433 ## [111]    412    442    430    461    412    412    412    412    412    426 ## [121]    449    412    412    824    412    412    433    412    412    412 ## [131]    412    412   1684    412    412    412   1649   1649   2061   1649 ## [141]   4947   6184   9482  10718   5359   2886   1649   1649   1237    412 ## [151]    412    412    412    412    412    412    412    412    412    412 ## [161]    412    412    412    412    412    412    412    412    412    412 ## [171]    412    412    412    412    412    412    412    412    412    412 ## [181]    412    412    412    412    412    412    412    412    412    412 ## [191]    412    412    412    412    824    412    412    412    412    412 ## [201]    412    412    412    412    412    412    412    824    412    412 ## [211]    412    412    412    412    412    412    412    412    412    412 ## [221]    412    824    824   1237   1649    824    412    412    412    412 ## [231]    824    412    824   1237    412    412    412    412    412    412 ## [241]    412    412    412    412    412    412    412 #' Compare applying the processing queue to either 10 spectra or the #' full set of spectra. microbenchmark(     intensity(s_mem[1:10]),     intensity(s_mem)[1:10],     times = 7) ## Unit: milliseconds ##                    expr        min         lq       mean     median         uq ##  intensity(s_mem[1:10])   3.338558   3.469012   3.768346   3.688882   4.095887 ##  intensity(s_mem)[1:10] 101.128773 102.028226 137.921287 104.121477 108.341070 ##         max neval ##    4.221185     7 ##  339.460163     7 #' Define a function that scales intensities scale_sum <- function(x, ...) {     x[, \"intensity\"] <- x[, \"intensity\"] / sum(x[, \"intensity\"], na.rm = TRUE)     x }  #' Add this function to the processing queue s_mem <- addProcessing(s_mem, scale_sum) s_mzr <- addProcessing(s_mzr, scale_sum) s_db <- addProcessing(s_db, scale_sum) sum(intensity(s_mem)) |> head() ## [1] 1 1 1 1 1 1 sum(intensity(s_mzr)) |> head() ## [1] 1 1 1 1 1 1 sum(intensity(s_db)) |> head() ## [1] 1 1 1 1 1 1 #' Restore the Spectra object to its initial state. s_db <- reset(s_db) median(lengths(s_db)) ## [1] 1547 sum(intensity(s_db)) |> head() ## [1]  898185 1037012 1094971 1135015 1106233 1181489"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"implementing-your-own-msbackend","dir":"Articles","previous_headings":"Workshop","what":"Implementing your own MsBackend","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"New backends Spectra extend MsBackend class implement methods defined infrastructure. Extending classes, MsBackendMemory MsBackendCached can help reducing number methods concepts needed implement. detailed example description provided Creating new MsBackend classes vignette - otherwise: open issue Spectra github repo. Add following code testthat.R file ensure compliance MsBackend definition:","code":"test_suite <- system.file(\"test_backends\", \"test_MsBackend\",                         package = \"Spectra\")  #' Assign an instance of the developed `MsBackend` to variable `be`. be <- my_be test_dir(test_suite, stop_on_failure = TRUE)"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"final-words","dir":"Articles","previous_headings":"Workshop","what":"Final words","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"designed support large data sets. easily extendable. support variety MS data file formats data representations. caching mechanism processing queue allow data analysis also read-data resources.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-backends.html","id":"acknowledgments","dir":"Articles","previous_headings":"","what":"Acknowledgments","title":"Spectra: an expandable infrastructure to handle mass spectrometry data","text":"Thank Philippine Louail fixing typos suggesting improvements.","code":""},{"path":[]},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-matching-with-MetaboAnnotation.html","id":"introduction","dir":"Articles","previous_headings":"Overview","what":"Introduction","title":"MS/MS Spectra Matching with the `MetaboAnnotation` Package","text":"Spectra package provides functionality required annotation identification workflows untargeted LC-MS/MS data, , flexible customizable, might cumbersome beginners analysts accustomed R. fill gap developed MetaboAnnotation package builds upon Spectra provides functions annotation LC-MS LC-MS/MS data sets tailored towards less experienced R user (Rainer et al. 2022).","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-matching-with-MetaboAnnotation.html","id":"convenient-spectra-matching-using-metaboannotation","dir":"Articles","previous_headings":"Overview","what":"Convenient spectra matching using MetaboAnnotation","title":"MS/MS Spectra Matching with the `MetaboAnnotation` Package","text":"example use case match experimental MS2 spectra DDA experiment pesticide mix reference spectra MassBank. load experimental data file distributed via msdata R package. next restrict data set MS2 spectra addition clean spectra removing peaks spectrum intensity lower 5% largest peak intensity spectrum. Finally, single-peak spectra removed. leads data set consisting 2451 spectra. next connect MassBank database (release 2023.11, running within docker image) create Spectra object representing data. Alternatively, MassBank spectral libraries also provided Bioconductor’s AnnotationHub can conveniently loaded . code first loads AnnotationHub resource loads data one MassBank release. database cached locally, avoiding thus re-download data future use. suggested apply processing applied query spectra also target (reference) spectra. Thus filter also reference spectra MassBank intensity filter. now directly calculate similarities 2451 experimental (query) MS2 spectra 103525 MassBank reference (target) spectra using compareSpectra() method, computationally intense similarity score calculated query target spectrum. alternative use matchSpectra() function MetaboAnnotation package allows restrict similarity calculations query target spectra similar m/z precursor ion (considering also similar retention time available reference spectra). create CompareSpectraParam object setting parameter requirePrecursor = TRUE (restrict similarity calculations query target spectra similar precursor m/z) ppm = 10 (m/z difference query target precursor within 10 ppm). Parameter THRESHFUN enables define threshold function defines spectra considered matching. function used MS2 spectra similarity (calculated default dotproduct function) larger equal 0.8 considered matching. next call matchSpectra() parameter object pass pest mbank query target Spectra, respectively. result get MatchedSpectra object contains query target spectra well matching result (.e. information query spectrum matches target spectrum based similarity score). can use query() target() functions access query target Spectra objects matches() extract matching information. display first 6 rows matrix. Functions whichQuery() whichTarget() return (unique) indices query target spectra matched. can see query spectra (39 2451 spectra) matched. part large proportion spectra MassBank precursor m/z available requirePrecursor = TRUE considered similarity calculation. Setting requirePrecursor = FALSE calculate similarity spectra (even missing precursor information) calculations take much longer. MatchedSpectra object inherits much functionality Spectra object. spectraVariables() function returns example available spectra variables, query well target Spectra. variable names latter prefixed target_ discriminate variable names query. can access individual spectra variables using $ variable name, multiple variables spectraData() function. extract retention time, precursor m/z query spectrum, precursor m/z target spectrum well similarity score object using spectraData() function. returned DataFrame contains matching information full data set, .e. query spectrum hence, returns NA values query spectra matched target spectrum. Note also query spectra matching multiple target spectra represented multiple rows (one matching target spectrum). ’re interested query spectra matching least one target spectrum hence subset matching result using whichQuery() function. Subsetting MatchedSpectra always relative query, .e. mtch[4] subset object matching results 4th query spectrum. now extract matching information data subset: can also return compound names matching spectra. Depending whether annotations retrieved MassBank MySQL database version AnnotationHub need use different spectra variable name compound name. can also visually inspect matches using mirror plots. create mirror plots first query spectrum matching reference spectra. better visualization scale peak intensities spectra total intensity sum one setting scalePeaks = TRUE.  Spectra matching results also manually, interactively, evaluated validated validateMatchedSpectra() function. Summarizing, matchSpectra() function enables thus convenient spectra matching MS data represented Spectra objects. result, MatchedSpectra object returned , addition matching results, contains also query target spectra. Pre-filtering spectra prior actual spectra similarity calculation can reduce running time matchSpectra() call might also miss potential matches. Note addition precursor m/z-based pre-filter also retention time pre-filtering available (see ?matchSpectra information). Also, advanced matching approach available MatchForwardReverseParam calculates addition forward score also reverse similarity match.","code":"library(Spectra) library(pander)  #' Load the pesticide mix data fl <- system.file(\"TripleTOF-SWATH\", \"PestMix1_DDA.mzML\", package = \"msdata\") pest <- Spectra(fl) #' restrict to MS2 data and remove intensities with intensity lower 5% pest <- filterMsLevel(pest, msLevel = 2L)  #' Remove peaks with an intensity below 5% or the spectra's BPC low_int <- function(x, ...) {     x > max(x, na.rm = TRUE) * 0.05 } pest <- filterIntensity(pest, intensity = low_int)  #' Remove peaks with a single peak pest <- pest[lengths(pest) > 1] library(RMariaDB) library(MsBackendMassbank)  #' Connect to the MassBank MySQL database con <- dbConnect(MariaDB(), user = \"massbank\", dbname = \"MassBank\",                  host = \"localhost\", pass = \"massbank\") mbank <- Spectra(con, source = MsBackendMassbankSql()) #' Load the AnnotationHub resource library(AnnotationHub) ah <- AnnotationHub()  #' List available MassBank release query(ah, \"MassBank\") ## AnnotationHub with 6 records ## # snapshotDate(): 2024-04-29 ## # $dataprovider: MassBank ## # $species: NA ## # $rdataclass: CompDb ## # additional mcols(): taxonomyid, genome, description, ## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags, ## #   rdatapath, sourceurl, sourcetype  ## # retrieve records with, e.g., 'object[[\"AH107048\"]]'  ##  ##              title                                 ##   AH107048 | MassBank CompDb for release 2021.03   ##   AH107049 | MassBank CompDb for release 2022.06   ##   AH111334 | MassBank CompDb for release 2022.12.1 ##   AH116164 | MassBank CompDb for release 2023.06   ##   AH116165 | MassBank CompDb for release 2023.09   ##   AH116166 | MassBank CompDb for release 2023.11 #' Load the MassBank release 2023.11 and get a Spectra object mbank <- ah[[\"AH116166\"]] |>     Spectra() #' Remove low intensity peaks and subsequently remove #' spectra with less than 2 peaks mbank <- filterIntensity(mbank, intensity = low_int) mbank <- mbank[lengths(mbank) > 1] library(MetaboAnnotation) prm <- CompareSpectraParam(ppm = 10, requirePrecursor = TRUE,                            THRESHFUN = function(x) which(x >= 0.8)) mtch <- matchSpectra(pest, mbank, param = prm) mtch ## Object of class MatchedSpectra  ## Total number of matches: 113  ## Number of query objects: 2451 (39 matched) ## Number of target objects: 103525 (69 matched) head(matches(mtch)) ##   query_idx target_idx     score ## 1       433      97386 0.9471924 ## 2       433      97387 0.9258791 ## 3       433      97388 0.8188538 ## 4       435      97386 0.9745761 ## 5       435      97387 0.8573438 ## 6       493      68124 0.9089015 whichQuery(mtch) ##  [1]  433  435  493  496  497  571  682  685  686  805  807  809  810  819  829 ## [16]  983 1095 1127 1266 1281 1283 1454 1457 1480 1706 1828 1830 1834 1839 1874 ## [31] 1882 1901 1906 2038 2040 2043 2050 2124 2309 sum(is.na(precursorMz(mbank))) ## [1] 23519 spectraVariables(mtch) ##  [1] \"msLevel\"                        \"rtime\"                          ##  [3] \"acquisitionNum\"                 \"scanIndex\"                      ##  [5] \"dataStorage\"                    \"dataOrigin\"                     ##  [7] \"centroided\"                     \"smoothed\"                       ##  [9] \"polarity\"                       \"precScanNum\"                    ## [11] \"precursorMz\"                    \"precursorIntensity\"             ## [13] \"precursorCharge\"                \"collisionEnergy\"                ## [15] \"isolationWindowLowerMz\"         \"isolationWindowTargetMz\"        ## [17] \"isolationWindowUpperMz\"         \"peaksCount\"                     ## [19] \"totIonCurrent\"                  \"basePeakMZ\"                     ## [21] \"basePeakIntensity\"              \"ionisationEnergy\"               ## [23] \"lowMZ\"                          \"highMZ\"                         ## [25] \"mergedScan\"                     \"mergedResultScanNum\"            ## [27] \"mergedResultStartScanNum\"       \"mergedResultEndScanNum\"         ## [29] \"injectionTime\"                  \"filterString\"                   ## [31] \"spectrumId\"                     \"ionMobilityDriftTime\"           ## [33] \"scanWindowLowerLimit\"           \"scanWindowUpperLimit\"           ## [35] \".original_query_index\"          \"target_msLevel\"                 ## [37] \"target_rtime\"                   \"target_acquisitionNum\"          ## [39] \"target_scanIndex\"               \"target_dataStorage\"             ## [41] \"target_dataOrigin\"              \"target_centroided\"              ## [43] \"target_smoothed\"                \"target_polarity\"                ## [45] \"target_precScanNum\"             \"target_precursorMz\"             ## [47] \"target_precursorIntensity\"      \"target_precursorCharge\"         ## [49] \"target_collisionEnergy\"         \"target_isolationWindowLowerMz\"  ## [51] \"target_isolationWindowTargetMz\" \"target_isolationWindowUpperMz\"  ## [53] \"target_compound_id\"             \"target_formula\"                 ## [55] \"target_exactmass\"               \"target_smiles\"                  ## [57] \"target_inchi\"                   \"target_inchikey\"                ## [59] \"target_cas\"                     \"target_pubchem\"                 ## [61] \"target_name\"                    \"target_spectrum_id\"             ## [63] \"target_spectrum_name\"           \"target_date\"                    ## [65] \"target_authors\"                 \"target_license\"                 ## [67] \"target_copyright\"               \"target_publication\"             ## [69] \"target_splash\"                  \"target_precursorMz_text\"        ## [71] \"target_adduct\"                  \"target_ionization\"              ## [73] \"target_ionization_voltage\"      \"target_fragmentation_mode\"      ## [75] \"target_collisionEnergy_text\"    \"target_instrument\"              ## [77] \"target_instrument_type\"         \"target_original_spectrum_id\"    ## [79] \"target_predicted\"               \"target_msms_mz_range_min\"       ## [81] \"target_msms_mz_range_max\"       \"target_synonym\"                 ## [83] \"score\" spectraData(mtch, c(\"rtime\", \"precursorMz\", \"target_precursorMz\", \"score\")) ## DataFrame with 2525 rows and 4 columns ##          rtime precursorMz target_precursorMz     score ##      <numeric>   <numeric>          <numeric> <numeric> ## 1        7.216    137.9639                 NA        NA ## 2       13.146     56.9419                 NA        NA ## 3       13.556     89.9449                 NA        NA ## 4       23.085    207.0294                 NA        NA ## 5       27.385    121.0990                 NA        NA ## ...        ...         ...                ...       ... ## 2521   895.182    137.9850                 NA        NA ## 2522   895.472     56.0495                 NA        NA ## 2523   896.252    142.9611                 NA        NA ## 2524   896.662     53.0129                 NA        NA ## 2525   898.602     91.5022                 NA        NA mtch <- mtch[whichQuery(mtch)] mtch ## Object of class MatchedSpectra  ## Total number of matches: 113  ## Number of query objects: 39 (39 matched) ## Number of target objects: 103525 (69 matched) spectraData(mtch, c(\"rtime\", \"precursorMz\", \"target_precursorMz\", \"score\")) ## DataFrame with 113 rows and 4 columns ##         rtime precursorMz target_precursorMz     score ##     <numeric>   <numeric>          <numeric> <numeric> ## 1     326.703     235.143            235.144  0.947192 ## 2     326.703     235.143            235.144  0.925879 ## 3     326.703     235.143            235.144  0.818854 ## 4     327.113     235.144            235.144  0.974576 ## 5     327.113     235.144            235.144  0.857344 ## ...       ...         ...                ...       ... ## 109   565.646     425.214            425.215  0.947080 ## 110   565.646     425.214            425.215  0.853670 ## 111   570.825     373.040            373.041  0.879824 ## 112   596.584     313.039            313.039  0.818335 ## 113   873.144     136.112            136.112  0.804731 #' Get the name of the column containing the compound name name_col <- intersect(spectraVariables(mtch),                       c(\"target_name\", \"target_compound_name\"))  pandoc.table(style = \"rmarkdown\",     as.data.frame(spectraData(mtch, c(\"rtime\", name_col, \"score\")))) #' plot the results for the first query spectrum plotSpectraMirror(mtch[1], ppm = 10, scalePeaks = TRUE)"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-matching-with-MetaboAnnotation.html","id":"ms2-spectra-matching-in-an-xcms-workflow","dir":"Articles","previous_headings":"Overview","what":"MS2 spectra matching in an xcms workflow","title":"MS/MS Spectra Matching with the `MetaboAnnotation` Package","text":"LC-MS/MS-based untargeted metabolomics (small compound mass spectrometry experiments general) quantification compounds performed MS1 MS2 data used identification features. Quantification MS1 data requires chromatographic peak detection step can performed using functionality xcms package. load thus xcms package import full MS data using readMsExperiment function. next perform chromatographic peak detection using centWave algorithm (see LC-MS/MS data analysis xcms vignette xcms package details chromatographic peak detection settings). total 99 chromatographic peaks identified. display first 6 . can now extract MS2 spectra chromatographic peak chromPeakSpectra() function. function identifies MS2 spectra recorded instrument retention time within retention time precursor m/z within m/z boundaries chromatographic peak. setting return.type = \"Spectra\" ensure data returned newer Spectra format hence enabling simplified spectra matching functionality presented . Spectra variable peak_id contains identified chromatographic peak (.e. row name chromPeaks). next, like previous section, clean spectra removing peaks intensity 5% largest peak intensity per spectrum removing spectra single peak. addition also scale peak intensities per spectrum using scalePeaks() function. spectra similarity scoring algorithms independent absolute peak intensities, peak scaling improve graphical visualization results. perform peak scaling experimental well reference spectra MassBank. Next perform spectra matching parameters previous section. , restrict MatchedSpectra query spectra matched. table lists compound names matching spectra chromatographic peaks. can also directly plot matching (query target) spectra using plotSpectraMirror() function subsetting MatchedSpectra object query spectrum interest. plot third query spectrum matching target spectra.  Summarizing, chromPeakSpectra() featureSpectra() functions, xcms allows return MS data Spectra objects enables, shown simple example, perform MS2 spectra matching using Spectra well MetaboAnnotation packages hence simplifying MS/MS-based annotation LC-MS features xcms.","code":"library(xcms) library(MsExperiment) pest_all <- readMsExperiment(fl) cwp <- CentWaveParam(snthresh = 5, noise = 100, ppm = 10,                      peakwidth = c(3, 30)) pest_all <- findChromPeaks(pest_all, param = cwp) head(chromPeaks(pest_all)) ##            mz    mzmin    mzmax      rt   rtmin   rtmax       into      intb ## CP01 142.9926 142.9921 142.9931 130.615 125.856 134.241  1113.8028  1106.229 ## CP02 221.0918 221.0906 221.0925 240.897 236.657 246.984   756.6935   744.779 ## CP03 220.0985 220.0978 220.0988 240.897 237.187 246.327  2060.5921  2052.549 ## CP04 219.0957 219.0950 219.0962 241.018 236.253 246.327 15172.6662 15133.811 ## CP05 153.0659 153.0655 153.0663 330.591 325.373 334.400  2148.7134  2141.943 ## CP06 235.1447 235.1441 235.1454 330.591 326.431 334.400  2836.2675  2829.627 ##           maxo  sn sample ## CP01  346.7006 102      1 ## CP02  212.5239  21      1 ## CP03  585.3036 151      1 ## CP04 4877.1162 367      1 ## CP05  784.9196 114      1 ## CP06 1006.9720 110      1 pest_ms2 <- chromPeakSpectra(pest_all, return.type = \"Spectra\") pest_ms2 ## MSn data (Spectra) with 158 spectra in a MsBackendMzR backend: ##       msLevel     rtime scanIndex ##     <integer> <numeric> <integer> ## 1           2   128.237      1000 ## 2           2   128.737      1008 ## 3           2   129.857      1023 ## 4           2   237.869      1812 ## 5           2   241.299      1846 ## ...       ...       ...       ... ## 154         2   575.255      5115 ## 155         2   596.584      5272 ## 156         2   592.424      5236 ## 157         2   596.054      5266 ## 158         2   873.714      7344 ##  ... 34 more variables/columns. ##  ## file(s): ## PestMix1_DDA.mzML ## Processing: ##  Filter: select MS level(s) 2 [Fri May 17 14:12:02 2024] ##  Merge 1 Spectra into one [Fri May 17 14:12:02 2024] pest_ms2$peak_id ##   [1] \"CP01\" \"CP01\" \"CP01\" \"CP04\" \"CP04\" \"CP05\" \"CP05\" \"CP06\" \"CP06\" \"CP08\" ##  [11] \"CP08\" \"CP11\" \"CP11\" \"CP12\" \"CP12\" \"CP13\" \"CP13\" \"CP13\" \"CP13\" \"CP14\" ##  [21] \"CP14\" \"CP14\" \"CP14\" \"CP18\" \"CP22\" \"CP22\" \"CP22\" \"CP22\" \"CP22\" \"CP25\" ##  [31] \"CP25\" \"CP25\" \"CP25\" \"CP25\" \"CP26\" \"CP26\" \"CP26\" \"CP26\" \"CP26\" \"CP26\" ##  [41] \"CP33\" \"CP33\" \"CP34\" \"CP34\" \"CP34\" \"CP34\" \"CP34\" \"CP35\" \"CP35\" \"CP35\" ##  [51] \"CP35\" \"CP35\" \"CP36\" \"CP41\" \"CP41\" \"CP41\" \"CP42\" \"CP42\" \"CP42\" \"CP42\" ##  [61] \"CP42\" \"CP44\" \"CP44\" \"CP46\" \"CP46\" \"CP46\" \"CP46\" \"CP47\" \"CP47\" \"CP47\" ##  [71] \"CP48\" \"CP48\" \"CP48\" \"CP50\" \"CP51\" \"CP51\" \"CP51\" \"CP52\" \"CP52\" \"CP52\" ##  [81] \"CP53\" \"CP53\" \"CP53\" \"CP53\" \"CP53\" \"CP57\" \"CP57\" \"CP57\" \"CP57\" \"CP57\" ##  [91] \"CP59\" \"CP59\" \"CP60\" \"CP60\" \"CP61\" \"CP61\" \"CP63\" \"CP63\" \"CP63\" \"CP63\" ## [101] \"CP64\" \"CP64\" \"CP64\" \"CP64\" \"CP64\" \"CP65\" \"CP66\" \"CP66\" \"CP66\" \"CP66\" ## [111] \"CP67\" \"CP67\" \"CP67\" \"CP69\" \"CP69\" \"CP69\" \"CP71\" \"CP71\" \"CP71\" \"CP71\" ## [121] \"CP72\" \"CP72\" \"CP72\" \"CP73\" \"CP81\" \"CP81\" \"CP81\" \"CP81\" \"CP82\" \"CP82\" ## [131] \"CP82\" \"CP85\" \"CP85\" \"CP88\" \"CP88\" \"CP89\" \"CP89\" \"CP89\" \"CP90\" \"CP90\" ## [141] \"CP90\" \"CP91\" \"CP91\" \"CP91\" \"CP93\" \"CP93\" \"CP93\" \"CP93\" \"CP93\" \"CP93\" ## [151] \"CP94\" \"CP94\" \"CP94\" \"CP94\" \"CP95\" \"CP98\" \"CP98\" \"CP99\" #' Remove peaks with an intensity below 5% pest_ms2 <- filterIntensity(pest_ms2, intensity = low_int)  #' Remove peaks with a single peak pest_ms2 <- pest_ms2[lengths(pest_ms2) > 1] pest_ms2 <- scalePeaks(pest_ms2) mbank <- scalePeaks(mbank) pest_match <- matchSpectra(pest_ms2, mbank, param = prm) pest_match ## Object of class MatchedSpectra  ## Total number of matches: 33  ## Number of query objects: 155 (12 matched) ## Number of target objects: 103525 (26 matched) pest_match <- pest_match[whichQuery(pest_match)] #' Get the name of the column containing the compound name name_col <- intersect(spectraVariables(pest_match),                       c(\"target_name\", \"target_compound_name\")) pandoc.table(     style = \"rmarkdown\",     as.data.frame(spectraData(pest_match, c(\"peak_id\", \"rtime\", name_col)))) plotSpectraMirror(pest_match[3])"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/Spectra-matching-with-MetaboAnnotation.html","id":"session-information","dir":"Articles","previous_headings":"","what":"Session information","title":"MS/MS Spectra Matching with the `MetaboAnnotation` Package","text":"","code":"sessionInfo() ## R version 4.4.0 (2024-04-24) ## Platform: x86_64-pc-linux-gnu ## Running under: Ubuntu 22.04.4 LTS ##  ## Matrix products: default ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3  ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0 ##  ## locale: ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C             ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        ##  ## time zone: Etc/UTC ## tzcode source: system (glibc) ##  ## attached base packages: ## [1] stats4    stats     graphics  grDevices utils     datasets  methods   ## [8] base      ##  ## other attached packages: ##  [1] MsExperiment_1.6.0       xcms_4.2.1               MetaboAnnotation_1.8.1   ##  [4] CompoundDb_1.8.0         AnnotationFilter_1.28.0  AnnotationHub_3.12.0     ##  [7] BiocFileCache_2.12.0     dbplyr_2.5.0             MsBackendMassbank_1.12.0 ## [10] RMariaDB_1.3.1           pander_0.6.5             Spectra_1.14.0           ## [13] ProtGenerics_1.36.0      S4Vectors_0.42.0         BiocGenerics_0.50.0      ## [16] BiocParallel_1.38.0      BiocStyle_2.32.0         ##  ## loaded via a namespace (and not attached): ##   [1] RColorBrewer_1.1-3          jsonlite_1.8.8              ##   [3] MultiAssayExperiment_1.30.1 magrittr_2.0.3              ##   [5] MALDIquant_1.22.2           rmarkdown_2.27              ##   [7] fs_1.6.4                    zlibbioc_1.50.0             ##   [9] ragg_1.3.2                  vctrs_0.6.5                 ##  [11] memoise_2.0.1               RCurl_1.98-1.14             ##  [13] base64enc_0.1-3             progress_1.2.3              ##  [15] htmltools_0.5.8.1           S4Arrays_1.4.0              ##  [17] curl_5.2.1                  SparseArray_1.4.3           ##  [19] mzID_1.42.0                 sass_0.4.9                  ##  [21] bslib_0.7.0                 htmlwidgets_1.6.4           ##  [23] desc_1.4.3                  plyr_1.8.9                  ##  [25] impute_1.78.0               lubridate_1.9.3             ##  [27] cachem_1.1.0                igraph_2.0.3                ##  [29] iterators_1.0.14            mime_0.12                   ##  [31] lifecycle_1.0.4             pkgconfig_2.0.3             ##  [33] Matrix_1.7-0                R6_2.5.1                    ##  [35] fastmap_1.2.0               GenomeInfoDbData_1.2.12     ##  [37] MatrixGenerics_1.16.0       clue_0.3-65                 ##  [39] digest_0.6.35               pcaMethods_1.96.0           ##  [41] rsvg_2.6.0                  colorspace_2.1-0            ##  [43] AnnotationDbi_1.66.0        textshaping_0.3.7           ##  [45] GenomicRanges_1.56.0        RSQLite_2.3.6               ##  [47] filelock_1.0.3              fansi_1.0.6                 ##  [49] timechange_0.3.0            httr_1.4.7                  ##  [51] abind_1.4-5                 compiler_4.4.0              ##  [53] doParallel_1.0.17           bit64_4.0.5                 ##  [55] withr_3.0.0                 DBI_1.2.2                   ##  [57] highr_0.10                  MASS_7.3-60.2               ##  [59] ChemmineR_3.56.0            rappdirs_0.3.3              ##  [61] DelayedArray_0.30.1         rjson_0.2.21                ##  [63] mzR_2.38.0                  tools_4.4.0                 ##  [65] PSMatch_1.8.0               glue_1.7.0                  ##  [67] QFeatures_1.14.1            grid_4.4.0                  ##  [69] cluster_2.1.6               reshape2_1.4.4              ##  [71] generics_0.1.3              gtable_0.3.5                ##  [73] preprocessCore_1.66.0       tidyr_1.3.1                 ##  [75] hms_1.1.3                   MetaboCoreUtils_1.12.0      ##  [77] xml2_1.3.6                  utf8_1.2.4                  ##  [79] XVector_0.44.0              foreach_1.5.2               ##  [81] BiocVersion_3.19.1          pillar_1.9.0                ##  [83] stringr_1.5.1               limma_3.60.0                ##  [85] dplyr_1.1.4                 lattice_0.22-6              ##  [87] bit_4.0.5                   tidyselect_1.2.1            ##  [89] Biostrings_2.72.0           knitr_1.46                  ##  [91] gridExtra_2.3               IRanges_2.38.0              ##  [93] SummarizedExperiment_1.34.0 xfun_0.44                   ##  [95] Biobase_2.64.0              statmod_1.5.0               ##  [97] MSnbase_2.30.1              matrixStats_1.3.0           ##  [99] DT_0.33                     stringi_1.8.4               ## [101] UCSC.utils_1.0.0            lazyeval_0.2.2              ## [103] yaml_2.3.8                  evaluate_0.23               ## [105] codetools_0.2-20            MsCoreUtils_1.16.0          ## [107] tibble_3.2.1                BiocManager_1.30.23         ## [109] affyio_1.74.0               cli_3.6.2                   ## [111] systemfonts_1.1.0           munsell_0.5.1               ## [113] jquerylib_0.1.4             MassSpecWavelet_1.70.0      ## [115] Rcpp_1.0.12                 GenomeInfoDb_1.40.0         ## [117] png_0.1-8                   XML_3.99-0.16.1             ## [119] parallel_4.4.0              pkgdown_2.0.9               ## [121] ggplot2_3.5.1               blob_1.2.4                  ## [123] prettyunits_1.2.0           bitops_1.0-7                ## [125] MsFeatures_1.12.0           affy_1.82.0                 ## [127] scales_1.3.0                ncdf4_1.22                  ## [129] purrr_1.0.2                 crayon_1.5.2                ## [131] rlang_1.1.3                 vsn_3.72.0                  ## [133] KEGGREST_1.44.0"},{"path":[]},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"description","dir":"Articles","previous_headings":"Overview","what":"Description","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Mass spectrometry (MS) data key technology modern proteomics metabolomics experiments. Due continuous improvements MS instrumentation, generated data can easily become large. Also, additional resources MS data exist, spectra libraries databases, specific file formats database systems sometimes support manipulations original data. Learning experiences MSnbase Bioconductor package, Spectra package developed provide even flexible expandable infrastructure MS data R. package implements clear separation user interface code provide, store import MS data. latter defined MsBackend interface allows implementation data type, format storage-dependent backends. Backends can thus implemented specific file types data resources different ways represent MS data (e.g. memory -disk data representations described (Gatto, Gibb, Rainer 2020)). also supposed interchangeable hence allowing user switch backends without affecting analysis. enable processing also large MS data sets, data manipulations default directly applied data cached lazy evaluation queue allows analyses also read-data representations. workshop shows expandability new infrastructure enable seamless integration analysis MS data variety input formats. illustrated simple comparison matching experimental MS2 spectra public spectral databases export data format commonly used exchange MS2 data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"pre-requisites","dir":"Articles","previous_headings":"Overview","what":"Pre-requisites","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Basic familiarity R Bioconductor. Basic understanding Mass Spectrometry (MS) data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"installation-and-participation","dir":"Articles","previous_headings":"Overview","what":"Installation and Participation","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"present version tutorial bases package versions available Bioconductor release 3.19. Get docker image tutorial docker pull jorainer/spectra_tutorials:RELEASE_3_19. Start docker using Enter http://localhost:8787 web browser log username rstudio password bioc. Open R-markdown file (vignettes/analyzing-MS-data--different-sources--Spectra.Rmd) RStudio server version web browser evaluate R code blocks.","code":"docker run \\     -e PASSWORD=bioc \\     -p 8787:8787 \\     jorainer/spectra_tutorials:RELEASE_3_19"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"rbioconductor-packages-used","dir":"Articles","previous_headings":"Overview","what":"R/Bioconductor packages used","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Spectra MsCoreUtils MsBackendMgf MsBackendMassbank CompoundDb","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"learning-goals","dir":"Articles","previous_headings":"Overview > Workshop goals and objectives","what":"Learning goals","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Understand import MS data R. Understand basic concept backends Spectra can used work MS data various sources.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"learning-objectives","dir":"Articles","previous_headings":"Overview > Workshop goals and objectives","what":"Learning objectives","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Import export MS data Spectra. Integrate MS data different resources MS data analysis workflow. Apply different data manipulations MS data represented Spectra object. Use Spectra perform spectra comparisons R.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"lc-msms-in-a-nutshell","dir":"Articles","previous_headings":"Workshop","what":"LC-MS/MS in a nutshell","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Mass spectrometry (MS) instruments measure mass--charge ratios (m/z) abundances ions. resulting m/z intensity values stored/represented spectrum. compounds charged, need ionized first (e.g. electro spray ionization (ESI)). MS usually combined another separation technique, liquid chromatography (LC). adds another dimension data: retention time (rt). LC-MS data represented multiple spectra, spectrum retention time. LC-MS measure features characterized m/z retention time - still don’t know molecule actually measured. Create addition fragment (MS/MS) spectra ions get information structure. Commonly used method: collision induced dissociation (CID). collision chamber filled e.g. N2, ions get fragmented spectrum fragments recorded (Steckel Schlosser 2019). Comparing matching fragment spectra reference helps identifying compound.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"the-spectra-package","dir":"Articles","previous_headings":"Workshop","what":"The Spectra package","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Purpose: provide expandable, well tested user-friendly infrastructure mass spectrometry (MS) data. Design: separation user interface (functions) code provide, store read MS data. Advantage: support different ways store handle data without affecting user functionality: functionality Spectra object independent data , either -memory, -disk, locally remote. Expandable: implementation additional backend classes (extending MsBackend virtual class) possible add support additional input file formats data representations without needing change Spectra package object. workshop : import MS data mzML files, select MS2 spectra certain compound, compare match MS2 spectra reference MS2 spectra public database, annotate spectra export file MGF format.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"ms-data-import-and-handling","dir":"Articles","previous_headings":"Workshop","what":"MS data import and handling","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"import MS data mzML files provided within package. files contain MSn data mix 8 standard compounds (added either water pool human serum samples) measured HILIC-based LC-MS/MS setup. MS2 data generated data dependent acquisition using collision energy 20eV. data import representation experimental data use MsBackendMzR backend supports import (export) data common raw mass spectrometry file formats (.e. mzML, mzXML CDF). MS data now represented Spectra object, can thought data.frame rows individual spectra columns spectra variables (\"rtime\", .e. retention time). spectraVariables() function lists available variables within Spectra object. spectra variable can accessed either via $ name using dedicated access function (preferred way). extract retention times first spectra using either $rtime function rtime(). Spectra object contains information total 1578 spectra 2 mzML files. using MsBackendMzR backend, general information spectrum kept memory resulting low memory footprint. spectra’s m/z intensity values can accessed mz() intensity() functions also peaksData() function returns list numeric matrices m/z intensity values. using MsBackendMzR backend, retrieved demand original data files time functions called. can also load full data memory changing backend MsBackendMzR MsBackendDataFrame. affect way use Spectra object : operations functions available, independently way data stored (.e. backend used). size Spectra object now larger, since full data loaded memory. subset Spectra objects, can use either [ one many available filter* functions (usually efficient [). subset sps_all arbitrary spectra simply using: workshop, next want identify experimental data MS2 spectra generated ion matches m/z [M+H]+ ion metabolite cystine (one standards added sample mix measured present experimental data). thus use filterPrecursorMzValues() function subset data MS2 spectra matching m/z (accepting difference m/z 10 parts-per-million (ppm)). total 6 spectra matched target precursor m/z.","code":"library(Spectra)  #' Define the input files fls <- dir(system.file(\"mzML\", package = \"SpectraTutorials\"),            full.names = TRUE)  #' Import the data sps_all <- Spectra(fls, source = MsBackendMzR()) #' List all available spectra variables (attributes) spectraVariables(sps_all) ##  [1] \"msLevel\"                  \"rtime\"                    ##  [3] \"acquisitionNum\"           \"scanIndex\"                ##  [5] \"dataStorage\"              \"dataOrigin\"               ##  [7] \"centroided\"               \"smoothed\"                 ##  [9] \"polarity\"                 \"precScanNum\"              ## [11] \"precursorMz\"              \"precursorIntensity\"       ## [13] \"precursorCharge\"          \"collisionEnergy\"          ## [15] \"isolationWindowLowerMz\"   \"isolationWindowTargetMz\"  ## [17] \"isolationWindowUpperMz\"   \"peaksCount\"               ## [19] \"totIonCurrent\"            \"basePeakMZ\"               ## [21] \"basePeakIntensity\"        \"ionisationEnergy\"         ## [23] \"lowMZ\"                    \"highMZ\"                   ## [25] \"mergedScan\"               \"mergedResultScanNum\"      ## [27] \"mergedResultStartScanNum\" \"mergedResultEndScanNum\"   ## [29] \"injectionTime\"            \"filterString\"             ## [31] \"spectrumId\"               \"ionMobilityDriftTime\"     ## [33] \"scanWindowLowerLimit\"     \"scanWindowUpperLimit\" #' Access the spectras' retention time sps_all$rtime |> head() ## [1] 0.273 0.570 0.873 1.183 1.491 1.798 rtime(sps_all) |> head() ## [1] 0.273 0.570 0.873 1.183 1.491 1.798 print(object.size(sps_all), units = \"MB\") ## 0.4 Mb ## Show the first 6 lines of the first spectrum's peak matrix peaksData(sps_all)[[1L]] |> head() ##            mz intensity ## [1,] 50.22647  383.7802 ## [2,] 52.97586  377.5944 ## [3,] 59.97846  518.6014 ## [4,] 60.04677 4716.1259 ## [5,] 60.06897  181.5105 ## [6,] 61.02565  250.6573 #' Change backend to a MsBackendDataFrame: load data into memory sps_all <- setBackend(sps_all, MsBackendDataFrame()) print(object.size(sps_all), units = \"MB\") ## 16.5 Mb sps_all[c(4, 2, 5)] ## MSn data (Spectra) with 3 spectra in a MsBackendDataFrame backend: ##     msLevel     rtime scanIndex ##   <integer> <numeric> <integer> ## 1         1     1.183         4 ## 2         1     0.570         2 ## 3         1     1.491         5 ##  ... 33 more variables/columns. ## Processing: ##  Switch backend from MsBackendMzR to MsBackendDataFrame [Fri May 17 14:09:40 2024] #' Define the m/z ratio for an ion of cystine mz <- 241.0311  #' Subset the dataset to MS2 spectra matching the m/z sps <- filterPrecursorMzValues(sps_all, mz = mz, ppm = 10) sps ## MSn data (Spectra) with 6 spectra in a MsBackendDataFrame backend: ##     msLevel     rtime scanIndex ##   <integer> <numeric> <integer> ## 1         2   209.936       673 ## 2         2   220.072       714 ## 3         2   231.604       734 ## 4         2   215.089       761 ## 5         2   225.739       781 ## 6         2   240.020       804 ##  ... 33 more variables/columns. ## Processing: ##  Switch backend from MsBackendMzR to MsBackendDataFrame [Fri May 17 14:09:40 2024] ##  Filter: select spectra with precursor m/z matching 241.0311 [Fri May 17 14:09:40 2024]"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"data-processing-and-manipulation","dir":"Articles","previous_headings":"Workshop","what":"Data processing and manipulation","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"plotSpectra() function can used visualize spectra. plot first spectrum data subset.  raw MS2 spectrum contains many low intensity peaks, likely representing noise. Thus next filter spectra removing peaks intensity smaller 5% maximum intensity spectrum (.e. base peak intensity). end define function takes intensity values spectrum input returns logical value whether peak retained (TRUE) (FALSE). function passed filterIntensity() function perform actual filtering spectra. filtering, spectra cleaner:  addition want replace absolute peak intensities values relative spectrum’s highest peak intensity. end define function takes peak matrix input (.e. numeric matrix columns containing m/z intensity values) returns two-column numeric matrix. function passed parameter FUN addProcessing() function perform desired data processing. addProcessing() function allows add user-defined data manipulation operation MS data (.e. peaks matrices) Spectra object. Note , scaling peak intensities, also dedicated function, scalePeaks(), available. show effect data processing extract intensities first spectrum: intensity values now 0 100.","code":"#' Plot the first spectrum plotSpectra(sps[1]) #' Define a filtering function low_int <- function(x, ...) {     x > max(x, na.rm = TRUE) * 0.05 } #' Apply the function to filter the spectra sps <- filterIntensity(sps, intensity = low_int) #' Plot the first spectrum after filtering plotSpectra(sps[1]) #' Define a function to scale the intensities. `x` is expected to be a #' *peak matrix*, i.e. numeric matrix with columns m/z and intensity scale_int <- function(x, ...) {     maxint <- max(x[, \"intensity\"], na.rm = TRUE)     x[, \"intensity\"] <- 100 * x[, \"intensity\"] / maxint     x } #' *Apply* the function to the data sps <- addProcessing(sps, scale_int) #' Get the intensities after scaling peaksData(sps)[[1L]] ##             mz  intensity ## [1,]  74.02289  13.439402 ## [2,] 120.01111  93.631648 ## [3,] 122.02667  49.287979 ## [4,] 151.98277 100.000000 ## [5,] 153.99924   9.422415 ## [6,] 177.99880   6.581111 ## [7,] 195.02556  12.969807 ## [8,] 241.03074  54.633224"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"spectrum-data-comparison","dir":"Articles","previous_headings":"Workshop","what":"Spectrum data comparison","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"next perform pairwise comparison subsetted spectra using dot product similarity measure. Prior actual similarity calculation, peaks individual spectra matched (.e. determined peak one spectrum correspond spectrum based mass--charge ratios). specify ppm = 20 peaks difference m/z smaller 20ppm considered matching. default, compareSpectra() uses (normalized) dot-product calculate similarity spectra, variety different algorithms can used instead (see Spectra vignette information). pairwise spectra similarities represented heatmap (note RStudio docker might crash pheatmap() call - avoid add filename = \"hm.pdf\" heatmap call).  similarity selected experimental MS2 spectra high (fact 0.88) suggesting fragment spectra original compound.","code":"#' Pairwise comparison of all spectra sim_mat <- compareSpectra(sps, ppm = 20) sim_mat ##           [,1]      [,2]      [,3]      [,4]      [,5]      [,6] ## [1,] 1.0000000 0.9717669 0.8907129 0.9721095 0.8795912 0.8818719 ## [2,] 0.9717669 1.0000000 0.9992127 0.9964783 0.9525726 0.9486682 ## [3,] 0.8907129 0.9992127 1.0000000 0.9947650 0.9518138 0.9478201 ## [4,] 0.9721095 0.9964783 0.9947650 1.0000000 0.9543584 0.9494561 ## [5,] 0.8795912 0.9525726 0.9518138 0.9543584 1.0000000 0.9994800 ## [6,] 0.8818719 0.9486682 0.9478201 0.9494561 0.9994800 1.0000000 library(pheatmap) hm <- pheatmap(sim_mat, breaks = seq(0, 1, length.out = 101))"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"comparing-spectra-against-massbank","dir":"Articles","previous_headings":"Workshop","what":"Comparing spectra against MassBank","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Although precursor m/z spectra matches m/z cystine, can still exclude represent fragments ions different compounds (m/z cystine). Matching experimental spectra public spectral library can used first step identification process untargeted metabolomics data. Several (public) spectral libraries small molecules available, : MassBank MassBank North America MoNa Human Metabolom Database HMDB GNPS … databases MsBackend interfaces already implemented allowing inclusion data directly R-based analysis workflows. Access MassBank data example possible MsBackendMassbank package. package provides MsBackendMassbank import/export MassBank files well MsBackendMassbankSql backend directly interfaces MassBank MySQL database. connect local installation MassBank MySQL database (release 2023.11 provided within docker image tutorial). next load MsBackendMassbank package initialize Spectra object MsBackendMassbankSql backend providing database connection. Alternatively, MySQL database system available tutorial can run within docker, MassBank data can also retrieved Bioconductor’s AnnotationHub. case MsBackendCompDb backend (defined CompoundDb) used instead, backends retrieve data SQL database properties. list MassBank releases/versions available Bioconductor’s AnnotationHub. can retrieve respective release (2021.03 case) access spectra data code . Note AnnotationHub cache downloaded database locally subsequent call download database . Spectra object mbank represents now MS data MassBank database total 86576 spectra. fact, mbank object contain MS data, primary keys spectra MassBank database. Hence, also relatively low memory footprint. operation Spectra object backend load requested data database demand. Calling intensity() Spectra object example retrieve intensity values spectra database. extract peaks matrix first spectrum. can see, also MassBank provides absolute intensities spectrum. data scale, however like scale values 0 100, just like experimental data. thus apply data processing operation also MassBank Spectra object. worked, although (obvious reasons) allowed change m/z intensity values within MassBank database. possible? works, data manipulation operation Spectra object cached within object’s lazy evaluation queue operation applied m/z intensity values --fly whenever data requested (.e. whenever mz() intensity() called ). side effect, also allows undo certain data operations simply calling reset() Spectra object. next want compare experimental spectra spectra MassBank. Instead comparing 86576 available spectra, first filter MassBank database spectra precursor m/z matching one [M+H]+ ion cystine. Note , alternative filterPrecursorMzValues() used , also use containsMz() function screen spectra containing actual peak matching precursor m/z. left us 12 spectra calculate similarity experimental spectra using compareSpectra() function. result got (normalized dot product) similarity score tested MassBank spectrum (columns) experimental spectrum (rows).  get high similarity scores, also lower ones. next determine best matching pair comparisons. best_match variable contains now index best matching MassBank experimental spectrum. visualize two using mirror plot showing best matches experimental spectra (upper panel) fragment spectra MassBank (lower panel). Matching peaks highlighted blue color. Plotting functions Spectra highly customizable example add m/z individual peak annotation intensity peak higher 5.  comparison plot also two spectra low similarity score.  Since identify MassBank spectrum high similarity experimental spectra also like know compound spectrum actually represents. Spectra objects generally flexible can arbitrarily many additional annotation fields (.e. spectra variables) spectrum. Thus, use spectraVariables() function list variables available MassBank Spectra object. fact, addition spectra specific information like instrument measured ionization voltage used, get also information originating compound name (\"compound_name\"), chemical formula (\"formula\") INChI key (\"inchikey\"). thus next subset mbank_sub best matching spectrum display associated compound name. Note database provided AnnotationHub contain compound names column/spectra variable \"name\" instead \"compound_name\", thus, used option download MassBank data AnnotationHub , use mbank_best_match$name instead mbank_best_match$compound_name . Indeed, experimental cystine spectrum matches (one) cystine spectrum MassBank. next add name chemical formula spectrum experimental spectra. also set collision energy 20eV assign ion/adduct cystine reference spectrum created. Spectra allows adding new (replacing existing) spectra variables using $<-. Note: convenient spectra matching functionality designed less experienced R users available MetaboAnnotation package. second tutorial available within github repository/R package additional tutorials can found MetaboAnnotationTutorials repository/R package.","code":"library(RMariaDB) #' Connect to the MassBank MySQL database con <- dbConnect(MariaDB(), user = \"massbank\", dbname = \"MassBank\",                  host = \"localhost\", pass = \"massbank\") library(MsBackendMassbank) #' Access the spectra data in MassBank mbank <- Spectra(con, source = MsBackendMassbankSql()) mbank ## MSn data (Spectra) with 86576 spectra in a MsBackendMassbankSql backend: ##         msLevel precursorMz  polarity ##       <integer>   <numeric> <integer> ## 1             2         506         0 ## 2            NA          NA         1 ## 3            NA          NA         0 ## 4            NA          NA         1 ## 5            NA          NA         0 ## ...         ...         ...       ... ## 86572         2     449.380         1 ## 86573         2     426.022         0 ## 86574         2     131.060         0 ## 86575         2     183.170         1 ## 86576         2     358.270         0 ##  ... 42 more variables/columns. ##  Use  'spectraVariables' to list all of them. library(AnnotationHub) ah <- AnnotationHub() query(ah, \"MassBank\") ## AnnotationHub with 6 records ## # snapshotDate(): 2024-04-29 ## # $dataprovider: MassBank ## # $species: NA ## # $rdataclass: CompDb ## # additional mcols(): taxonomyid, genome, description, ## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags, ## #   rdatapath, sourceurl, sourcetype  ## # retrieve records with, e.g., 'object[[\"AH107048\"]]'  ##  ##              title                                 ##   AH107048 | MassBank CompDb for release 2021.03   ##   AH107049 | MassBank CompDb for release 2022.06   ##   AH111334 | MassBank CompDb for release 2022.12.1 ##   AH116164 | MassBank CompDb for release 2023.06   ##   AH116165 | MassBank CompDb for release 2023.09   ##   AH116166 | MassBank CompDb for release 2023.11 mb <- ah[[\"AH107048\"]] mbank <- Spectra(mb) print(object.size(mbank), units = \"MB\") ## 6.6 Mb #' Get peaks matrix for the first spectrum peaksData(mbank[1])[[1L]] ##             mz    intensity ##  [1,] 146.7608     0.980184 ##  [2,] 158.8635   938.145447 ##  [3,] 174.9888    56.718353 ##  [4,] 176.8688   847.169678 ##  [5,] 213.9867   133.479233 ##  [6,] 221.0402     3.189743 ##  [7,] 238.9681    76.114944 ##  [8,] 246.3159     2.592528 ##  [9,] 255.0206     2.059480 ## [10,] 256.8184     1.597428 ## [11,] 272.9973   801.059082 ## [12,] 290.9484     4.848598 ## [13,] 293.9753     1.233919 ## [14,] 310.1210    33.710808 ## [15,] 352.9528    49.103123 ## [16,] 370.9348   461.860779 ## [17,] 390.0576     4.611422 ## [18,] 408.0003 23963.912109 ## [19,] 409.0284     0.884388 ## [20,] 426.1263    69.098648 ## [21,] 470.0574     2.051622 ## [22,] 487.9893   812.386597 ## [23,] 585.8845     1.093564 #' Scale intensities for all MassBank spectra mbank <- addProcessing(mbank, scale_int) peaksData(mbank[1])[[1L]] ##             mz    intensity ##  [1,] 146.7608 4.090250e-03 ##  [2,] 158.8635 3.914826e+00 ##  [3,] 174.9888 2.366824e-01 ##  [4,] 176.8688 3.535189e+00 ##  [5,] 213.9867 5.570010e-01 ##  [6,] 221.0402 1.331061e-02 ##  [7,] 238.9681 3.176232e-01 ##  [8,] 246.3159 1.081847e-02 ##  [9,] 255.0206 8.594089e-03 ## [10,] 256.8184 6.665973e-03 ## [11,] 272.9973 3.342773e+00 ## [12,] 290.9484 2.023292e-02 ## [13,] 293.9753 5.149072e-03 ## [14,] 310.1210 1.406732e-01 ## [15,] 352.9528 2.049045e-01 ## [16,] 370.9348 1.927318e+00 ## [17,] 390.0576 1.924319e-02 ## [18,] 408.0003 1.000000e+02 ## [19,] 409.0284 3.690499e-03 ## [20,] 426.1263 2.883446e-01 ## [21,] 470.0574 8.561298e-03 ## [22,] 487.9893 3.390042e+00 ## [23,] 585.8845 4.563378e-03 #' Filter MassBank for spectra of precursor ion mbank_sub <- filterPrecursorMzValues(mbank, mz = mz, ppm = 10) mbank_sub ## MSn data (Spectra) with 12 spectra in a MsBackendMassbankSql backend: ##       msLevel precursorMz  polarity ##     <integer>   <numeric> <integer> ## 1           2     241.031         1 ## 2           2     241.031         1 ## 3           2     241.031         1 ## 4           2     241.031         1 ## 5           2     241.031         1 ## ...       ...         ...       ... ## 8           2     241.031         1 ## 9           2     241.031         1 ## 10          2     241.031         1 ## 11          2     241.031         1 ## 12          2     241.031         1 ##  ... 42 more variables/columns. ##  Use  'spectraVariables' to list all of them. ## Lazy evaluation queue: 1 processing step(s) ## Processing: ##  Filter: select spectra with precursor m/z matching 241.0311 [Fri May 17 14:09:48 2024] #' Compare MassBank subset to experimental spectra res <- compareSpectra(sps, mbank_sub, ppm = 20) res ##       RP014401  RP014402    RP014403  CE000600  CE000598  CE000602  CE000595 ## [1,] 0.2026317 0.3343503 0.024658599 0.9021127 0.9069832 0.7714246 0.4711052 ## [2,] 0.1807144 0.2933160 0.021440961 0.8863867 0.8655798 0.7507946 0.4307388 ## [3,] 0.1801990 0.2892779 0.005165072 0.8862688 0.8541779 0.7335706 0.4406949 ## [4,] 0.1596113 0.3067826 0.005668103 0.8666230 0.8760503 0.7770975 0.4382448 ## [5,] 0.1784321 0.3608116 0.005392068 0.8792278 0.9101864 0.7761744 0.5016645 ## [6,] 0.1871595 0.3629835 0.005564441 0.8849054 0.8705634 0.7200598 0.5052482 ##       CE000599  CE000596  CE000597  CE000603  CE000601 ## [1,] 0.5101048 0.7358534 0.2843629 0.6267950 0.6085564 ## [2,] 0.5304578 0.7415840 0.3069911 0.6079285 0.5669921 ## [3,] 0.5379880 0.7480320 0.3157130 0.6220260 0.5458613 ## [4,] 0.4738241 0.6957804 0.2580864 0.5819616 0.6049588 ## [5,] 0.4913249 0.7185230 0.2783447 0.6469762 0.6055037 ## [6,] 0.5013606 0.7278495 0.2864854 0.6554185 0.5523657 pheatmap(res) best_match <- which(res == max(res), arr.ind = TRUE) best_match ##      row col ## [1,]   5   5 #' Specifying a function to draw peak labels label_fun <- function(x) {     ints <- unlist(intensity(x))     mzs <- format(unlist(mz(x)), digits = 4)     mzs[ints < 5] <- \"\"     mzs } plotSpectraMirror(sps[best_match[1]], mbank_sub[best_match[2]],                   ppm = 20, labels = label_fun, labelPos = 2,                   labelOffset = 0.2, labelSrt = -30) grid() plotSpectraMirror(sps[3], mbank_sub[1],                   ppm = 20, labels = label_fun, labelPos = 2,                   labelOffset = 0.2, labelSrt = -30) grid() #' What variables are available in MassBank spectraVariables(mbank_sub) ##  [1] \"msLevel\"                 \"rtime\"                   ##  [3] \"acquisitionNum\"          \"scanIndex\"               ##  [5] \"dataStorage\"             \"dataOrigin\"              ##  [7] \"centroided\"              \"smoothed\"                ##  [9] \"polarity\"                \"precScanNum\"             ## [11] \"precursorMz\"             \"precursorIntensity\"      ## [13] \"precursorCharge\"         \"collisionEnergy\"         ## [15] \"isolationWindowLowerMz\"  \"isolationWindowTargetMz\" ## [17] \"isolationWindowUpperMz\"  \"spectrum_id\"             ## [19] \"spectrum_name\"           \"date\"                    ## [21] \"authors\"                 \"license\"                 ## [23] \"copyright\"               \"publication\"             ## [25] \"splash\"                  \"compound_id\"             ## [27] \"adduct\"                  \"ionization\"              ## [29] \"ionization_voltage\"      \"fragmentation_mode\"      ## [31] \"collision_energy_text\"   \"instrument\"              ## [33] \"instrument_type\"         \"formula\"                 ## [35] \"exactmass\"               \"smiles\"                  ## [37] \"inchi\"                   \"inchikey\"                ## [39] \"cas\"                     \"pubchem\"                 ## [41] \"synonym\"                 \"precursor_mz_text\"       ## [43] \"compound_name\" mbank_best_match <- mbank_sub[best_match[2]] mbank_best_match$compound_name ## [1] \"Cystine\" #' Add annotations to the experimental spectra sps$name <- mbank_best_match$compound_name sps$formula <- mbank_best_match$formula sps$adduct <- mbank_best_match$adduct sps$collisionEnergy <- 20"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"data-export","dir":"Articles","previous_headings":"Workshop","what":"Data export","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"last want export spectra file MGF format. use MsBackendMgf package provides MsBackendMgf backend adds support MGF file import/export Spectra objects. Data Spectra objects can generally exported export() function. format data exported depends specified MsBackend class. using instance MsBackendMgf define export data file MGF format. read first 6 lines resulting file.","code":"library(MsBackendMgf)  #' Export the spectra to a MGF file export(sps, backend = MsBackendMgf(), file = \"Cystine_ce20.mgf\") readLines(\"Cystine_ce20.mgf\", n = 6) ## [1] \"BEGIN IONS\"                                ## [2] \"TITLE=msLevel 2; retentionTime ; scanNum \" ## [3] \"msLevel=2\"                                 ## [4] \"RTINSECONDS=209.93599999998\"               ## [5] \"SCANS=673\"                                 ## [6] \"scanIndex=673\""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"comparing-spectra-against-hmdb","dir":"Articles","previous_headings":"Workshop","what":"Comparing spectra against HMDB","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"addition MsBackendMassbank, provides access MassBank data, also MsBackendHmdb package supporting spectral data public Human Metabolome Database (HMDB). package however yet provide direct access HMDB database , MsBackendHmdbXml backend, allows import MS2 spectra files HMDB format. provided HMDB individual xml files custom file format bundled (can hence downloaded) single archive. alternative, also possible create portable annotation resources using CompoundDb package. packages allows example create annotation databases MoNA, MassBank HMDB. CompDb annotations small compounds different resources become available also Bioconductors AnnotationHub. CompDb database containing compound annotations along (experimental) MS2 spectra provided data release MetaboAnnotationTutorials repository. download SQLite database temporary file. can now load annotation resource using CompDb function CompoundDb package: can now create Spectra object get access MS2 spectra within package: now Spectra object containing MS2 spectra HMDB along compound annotations. spectra variables (including compound annotations) available spectrum : Also , want filter data resource first spectra matching precursor m/z. Unfortunately, HMDB provided spectra’s precursor m/z hence need use containsMz() function find spectra containing peak m/z matching m/z ion interest. addition, need use rather large tolerance value (defines maximal acceptable absolute difference m/z values) since experimental spectra HMDB seem recorded well calibrated instrument. total 3904 spectra contain peak required m/z (+/- 0.2 Dalton) can proceed calculate spectral similarities experimental spectra. highest similarity spectra spectra HMDB r max(res). compare two best matching spectra mirror plot, upper panel showing experimental spectrum lower panel best matching MS2 spectrum HMDB.  experimental spectrum seems nicely match reference MS2 HMDB. extract compound identifier best matching HMDB spectrum (stored spectra variable called \"compound_id\") name compound fact, matching spectrum HMDB experimental spectrum L-Cystine.","code":"#' Download the CompDb database using curl library(curl) dbname <- \"CompDb.Hsapiens.HMDB.5.0.sqlite\" db_file <- file.path(tempdir(), dbname) curl_download(     paste0(\"https://github.com/jorainer/MetaboAnnotationTutorials/\",            \"releases/download/2021-11-02/\", dbname),     destfile = db_file) library(CompoundDb) #' Load a CompDb database with compound annotation from HMDB cdb <- CompDb(db_file) cdb ## class: CompDb  ##  data source: HMDB  ##  version: 5.0  ##  organism: Hsapiens  ##  compound count: 217776  ##  MS/MS spectra count: 64920 hmdb <- Spectra(cdb) hmdb ## MSn data (Spectra) with 64920 spectra in a MsBackendCompDb backend: ##         msLevel precursorMz  polarity ##       <integer>   <numeric> <integer> ## 1            NA          NA         1 ## 2            NA          NA         1 ## 3            NA          NA         1 ## 4            NA          NA         1 ## 5            NA          NA         1 ## ...         ...         ...       ... ## 64916        NA          NA         0 ## 64917        NA          NA         0 ## 64918        NA          NA         0 ## 64919        NA          NA         0 ## 64920        NA          NA         1 ##  ... 32 more variables/columns. ##  Use  'spectraVariables' to list all of them. ##  data source: HMDB  ##  version: 5.0  ##  organism: Hsapiens spectraVariables(hmdb) ##  [1] \"msLevel\"                 \"rtime\"                   ##  [3] \"acquisitionNum\"          \"scanIndex\"               ##  [5] \"dataStorage\"             \"dataOrigin\"              ##  [7] \"centroided\"              \"smoothed\"                ##  [9] \"polarity\"                \"precScanNum\"             ## [11] \"precursorMz\"             \"precursorIntensity\"      ## [13] \"precursorCharge\"         \"collisionEnergy\"         ## [15] \"isolationWindowLowerMz\"  \"isolationWindowTargetMz\" ## [17] \"isolationWindowUpperMz\"  \"compound_id\"             ## [19] \"name\"                    \"inchi\"                   ## [21] \"inchikey\"                \"formula\"                 ## [23] \"exactmass\"               \"smiles\"                  ## [25] \"original_spectrum_id\"    \"predicted\"               ## [27] \"splash\"                  \"instrument_type\"         ## [29] \"instrument\"              \"spectrum_id\"             ## [31] \"msms_mz_range_min\"       \"msms_mz_range_max\"       ## [33] \"synonym\" #' Identify spectra containing a peak matching cystine m/z has_mz <- containsMz(hmdb, mz = mz, tolerance = 0.2) #' Subset HMDB hmdb_sub <- hmdb[has_mz] #' Compare HMDB against experimental spectra res <- compareSpectra(hmdb_sub, sps, tolerance = 0.2) best_match <- which(res == max(res), arr.ind = TRUE) ## Specifying a function to draw peak labels label_fun <- function(x) {     format(unlist(mz(x)), digits = 4) } plotSpectraMirror(hmdb_sub[best_match[1]], sps[best_match[2]], tolerance = 0.2,                   labels = label_fun, labelPos = 2, labelOffset = 0.2,                   labelSrt = -30) grid() hmdb_sub[best_match[1]]$compound_id ## [1] \"HMDB0000192\" hmdb_sub[best_match[1]]$name ## [1] \"L-Cystine\""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"an-alternative-way-to-store-ms-data","dir":"Articles","previous_headings":"Workshop","what":"An alternative way to store MS data","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"use cases might useful store data whole MS experiments central place. MsBackendSql backend allows example store data relational database, simple SQLite files MySQL/MariaDB database systems. latter case allow also remote access centrally stored data set. Also, performance, especially large datasets much better MySQL database compared SQLite database used . section show data MS experiment can inserted database can retrieved. create empty SQLite database (temporary file). detailed , SQL database system used instead (resulting eventually even better performance). can next import data example experiment selected database. MsBackendSql MsBackendOfflineSql backends allow now access use MS data stored database. MsBackendOfflineSql keep active connection database reconnects database needed. slightly lower performance, allows object serialized, e.g. saved reloaded disk using save() load() functions. access data within SQL database simply name database parameter Spectra() function use MsBackendOfflineSql backend. MsBackendOfflineSql need specify SQL driver, case, since using SQLite database, SQLite() well name database (SQLite file name database). memory footprint Spectra object small data retrieved database --fly. performance backend also higher example MsBackendMzR. compare performance MsBackendOfflineSql present small data set MsBackendDataFrame (data -memory) MsBackendMzR (data retrieved original file). Note recent versions Spectra package higher-performance -memory backend available (MsBackendMemory). Thus, MsBackendOfflineSql similar performance data memory. performance even better smaller subsets data loaded. real world use case backend store large MS experiment central place. Data analysts connect central database, explore subset data ultimately either load subsets data (local) memory changing backend data subset e.g. MsBackendDataFrame store data subset local files e.g. mzML format using export() method MsBackendMzR backend.","code":"library(RSQLite) db_file <- tempfile() scon <- dbConnect(SQLite(), db_file) library(MsBackendSql) ## Create a database containing the full raw data createMsBackendSqlDatabase(scon, fls) ## [1] TRUE dbDisconnect(scon) sps_db <- Spectra(db_file, drv = SQLite(),                   source = MsBackendOfflineSql()) sps_db ## MSn data (Spectra) with 1578 spectra in a MsBackendOfflineSql backend: ##        msLevel precursorMz  polarity ##      <integer>   <numeric> <integer> ## 1            1          NA         1 ## 2            1          NA         1 ## 3            1          NA         1 ## 4            1          NA         1 ## 5            1          NA         1 ## ...        ...         ...       ... ## 1574         2    107.9501         1 ## 1575         1          NA         1 ## 1576         2     95.0608         1 ## 1577         1          NA         1 ## 1578         1          NA         1 ##  ... 34 more variables/columns. ##  Use  'spectraVariables' to list all of them. ## Database: /tmp/RtmpscQFqX/fileeae479f47ad print(object.size(sps_db), units = \"MB\") ## 0.1 Mb ## Load the data to different backends sps_mzr <- Spectra(fls, source = MsBackendMzR()) sps_df <- setBackend(sps_mzr, MsBackendDataFrame())  library(microbenchmark) microbenchmark(peaksData(sps_mzr),                peaksData(sps_df),                peaksData(sps_db),                times = 10) ## Unit: milliseconds ##                expr       min        lq      mean    median        uq ##  peaksData(sps_mzr) 272.61497 279.93407 425.31103 309.83662 317.51824 ##   peaksData(sps_df)  82.79383  96.04517 106.78148 100.13293 128.23367 ##   peaksData(sps_db)  52.89115  59.55610  69.26605  65.85904  85.16414 ##         max neval ##  1548.86966    10 ##   133.89797    10 ##    86.16455    10 microbenchmark(peaksData(sps_mzr[1:10]),                peaksData(sps_df[1:10]),                peaksData(sps_db[1:10]),                times = 10) ## Unit: milliseconds ##                      expr       min        lq      mean    median        uq ##  peaksData(sps_mzr[1:10]) 10.723417 11.186059 12.923746 11.655220 12.190033 ##   peaksData(sps_df[1:10])  3.539373  3.737403  3.965834  3.951337  4.275036 ##   peaksData(sps_db[1:10])  3.621757  4.224282  4.603907  4.474016  4.874835 ##        max neval ##  23.846750    10 ##   4.371947    10 ##   6.294283    10"},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"short-summary","dir":"Articles","previous_headings":"","what":"Short summary","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"Spectra provides powerful infrastructure MS data. Independence data analysis data storage functionality allows easily add support additional data types data handling/storage modes. Caching data operations applying --fly allows data manipulations regardless data stored.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"simple use case matching experimental MS2 spectra public database illustrated short tutorial flexibility expandability Spectra package enables seamless integration mass spectrometry data different sources. possible clear separation user functionality (Spectra object) representation data (MsBackend object). Backends MsBackendMgf, MsBackendMassbank MsBackendHmdbXml provide support additional data formats data sources, others, due much lower memory footprint (MsBackendMzR, MsBackendHdf5Peaks), enable analysis also large data sets. importantly however, backends interchangeable affect way users can handle analyze MS data Spectra package. Also, caching data operations within Spectra object applying upon data requests, data operations can applied data resource regardless data stored, even data read-.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/articles/analyzing-MS-data-from-different-sources-with-Spectra.html","id":"what-else","dir":"Articles","previous_headings":"","what":"What else?","title":"Seamless Integration of Mass Spectrometry Data from Different Sources with the `Spectra` Package","text":"MetaboAnnotation (Andrea Vicini, Michael Witting) provide simple functions matching spectra m/z retention time data (Rainer et al. 2022). MsExperiment represent whole MS experiments (incl. Spectra, sample annotation, data quantitation, annotation files etc). MsQuality (Thomas Naake): QC measures MS data (Naake, Rainer, Huber 2023). MsBackendMsp (Steffen Neumann): backend files MSP format. ...","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Johannes Rainer. Author, maintainer. Michael Witting. Author. Laurent Gatto. Author. Sebastian Gibb. Author. Philippine Louail. Contributor.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Rainer J, Witting M, Gatto L, Gibb S (2024). SpectraTutorials: Mass Spectrometry Data Analysis Spectra. R package version 1.0.0, https://jorainer.github.io/SpectraTutorials/.","code":"@Manual{,   title = {SpectraTutorials: Mass Spectrometry Data Analysis with Spectra},   author = {Johannes Rainer and Michael Witting and Laurent Gatto and Sebastian Gibb},   year = {2024},   note = {R package version 1.0.0},   url = {https://jorainer.github.io/SpectraTutorials/}, }"},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"mass-spectrometry-data-analysis-with-spectra","dir":"","previous_headings":"","what":"Mass Spectrometry Data Analysis with Spectra","title":"Mass Spectrometry Data Analysis with Spectra","text":"Tutorials workshops describing explaining infrastructure mass spectrometry (MS) data analysis centered around Bioconductor’s Spectra package. Workshops also given (either -person virtually) various conferences. available, recordings linked respective workshop section. See NEWS.md information current version change log.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Mass Spectrometry Data Analysis with Spectra","text":"-line code evaluation workshops can also run using self-contained docker image R packages server version RStudio (Posit) pre-installed. current docker container bases (includes) official docker container Bioconductor release 3.19. alternative, rendered versions workshops available SpectraTutorials (navigate Articles drop-menu select workshop interest). installation: Get docker image tutorial docker pull jorainer/spectra_tutorials:RELEASE_3_19. Start docker using Enter http://localhost:8787 web browser log username rstudio password bioc. RStudio server version: open R-markdown (.Rmd) files vignettes folder evaluate R code blocks.","code":"docker run \\     -e PASSWORD=bioc \\     -p 8787:8787 \\     jorainer/spectra_tutorials:RELEASE_3_19"},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"seamless-integration-of-mass-spectrometry-data-from-different-sources","dir":"","previous_headings":"","what":"Seamless Integration of Mass Spectrometry Data from Different Sources","title":"Mass Spectrometry Data Analysis with Spectra","text":"rendered version","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"short-description","dir":"","previous_headings":"Seamless Integration of Mass Spectrometry Data from Different Sources","what":"Short description","title":"Mass Spectrometry Data Analysis with Spectra","text":"workshop use Spectra : import MS data mzML files, select MS2 spectra certain compound, compare match MS2 spectra reference MS2 spectra public database, annotate spectra export file MGF format.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"description","dir":"","previous_headings":"Seamless Integration of Mass Spectrometry Data from Different Sources","what":"Description","title":"Mass Spectrometry Data Analysis with Spectra","text":"(instructor-led live demo) workshop explains Spectra package shows new infrastructure can used represent analyze Mass Spectrometry (MS) data. use different data type format specific backends guarantees easy expandability infrastructure. illustrated tutorial simple use case, MS/MS (.k.. MS2) spectra, measured MS instrument, compared matched public spectral database containing public domain reference MS2 spectra. video recording workshop BioC2021 conference available . video recording workshop EuroBioc2020 conference available .","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"learning-goals","dir":"","previous_headings":"Seamless Integration of Mass Spectrometry Data from Different Sources > Workshop goals and objectives","what":"Learning goals","title":"Mass Spectrometry Data Analysis with Spectra","text":"Understand import MS data R. Understand basic concept different backends can used Spectra work MS data various sources.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"learning-objectives","dir":"","previous_headings":"Seamless Integration of Mass Spectrometry Data from Different Sources > Workshop goals and objectives","what":"Learning objectives","title":"Mass Spectrometry Data Analysis with Spectra","text":"Import export MS data Spectra. Integrate MS data different resources MS data analysis workflow. Apply different data manipulations MS data represented Spectra object. Use Spectra perform spectra comparisons R.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"pre-requisites","dir":"","previous_headings":"Seamless Integration of Mass Spectrometry Data from Different Sources","what":"Pre-requisites","title":"Mass Spectrometry Data Analysis with Spectra","text":"Basic familiarity R Bioconductor. Basic understanding Mass Spectrometry (MS) data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"rbioconductor-packages-used","dir":"","previous_headings":"Seamless Integration of Mass Spectrometry Data from Different Sources","what":"R/Bioconductor packages used","title":"Mass Spectrometry Data Analysis with Spectra","text":"Spectra MsCoreUtils MsBackendMgf MsBackendMassbank CompoundDb","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"spectra-an-expandable-infrastructure-to-handle-mass-spectrometry-data","dir":"","previous_headings":"","what":"Spectra: an expandable infrastructure to handle mass spectrometry data","title":"Mass Spectrometry Data Analysis with Spectra","text":"rendered version","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"short-description-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data","what":"Short description","title":"Mass Spectrometry Data Analysis with Spectra","text":"Explain different representations mass spectrometry data along properties advantages. Explain caching lazy processing queue Spectra package.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"description-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data","what":"Description","title":"Mass Spectrometry Data Analysis with Spectra","text":"Spectra package provides modern infrastructure MS data handling specifically designed enable extension additional data resources alternative data representations. can realized extending virtual MsBackend class related methods. Implementations MsBackend classes can tailored specific needs, low memory footprint, fast processing, remote data access, also support specific additional data types variables. Importantly, data processing Spectra objects independent backend use due lazy evaluation mechanism caches data manipulations internally. workshop discusses different available data representations MS data along properties, advantages performances. addition, Spectra’s concept lazy evaluation data manipulations presented, well simple caching mechanism data modifications. Finally, explains new MsBackend instances can implemented tested ensure compliance.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"pre-requisites-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data","what":"Pre-requisites","title":"Mass Spectrometry Data Analysis with Spectra","text":"Basic familiarity R Bioconductor. Basic understanding Mass Spectrometry (MS) data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"rbioconductor-packages-used-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data","what":"R/Bioconductor packages used","title":"Mass Spectrometry Data Analysis with Spectra","text":"Spectra MsBackendSql","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"workshop-goals-and-objectives-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data","what":"Workshop goals and objectives","title":"Mass Spectrometry Data Analysis with Spectra","text":"technical demonstration internals Spectra package design MS infrastructure. ’re demonstrating use cases analysis workflows .","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"learning-goals-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data > Workshop goals and objectives","what":"Learning goals","title":"Mass Spectrometry Data Analysis with Spectra","text":"Understand MS data handled Spectra. Understand differences properties different MsBackend implementations.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"learning-objectives-1","dir":"","previous_headings":"Spectra: an expandable infrastructure to handle mass spectrometry data > Workshop goals and objectives","what":"Learning objectives","title":"Mass Spectrometry Data Analysis with Spectra","text":"Learn MS data handled Spectra. Understand data representations/backends fit purpose/use case. Insights internals Spectra MS infrastructure facilitate implementation backend(s).","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"msms-spectra-matching-with-the-metaboannotation-package","dir":"","previous_headings":"","what":"MS/MS Spectra Matching with the MetaboAnnotation Package","title":"Mass Spectrometry Data Analysis with Spectra","text":"rendered version Spectra package provides functionality required annotation identification workflows untargeted LC-MS/MS data, , flexible customizable, might cumbersome beginners analysts accustomed R. fill gap developed MetaboAnnotation package builds upon Spectra provides functions annotation LC-MS LC-MS/MS data sets tailored towards less experienced R user. tutorial shows MetaboAnnotation can used R-based LC-MS/MS annotation workflows. Note also additional tutorial MetaboAnnotation, CompoundDb MetaboCoreUtils packages available: MetaboAnnotationTutorials.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"Mass Spectrometry Data Analysis with Spectra","text":"contributions, see RforMassSpectrometry contributions guideline.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Mass Spectrometry Data Analysis with Spectra","text":"See RforMassSpectrometry Code Conduct.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/index.html","id":"acknowledgments","dir":"","previous_headings":"","what":"Acknowledgments","title":"Mass Spectrometry Data Analysis with Spectra","text":"Thank Philippine Louail fixing typos suggesting improvements Spectra: expandable infrastructure handle mass spectrometry data.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-10","dir":"Changelog","previous_headings":"","what":"Version 1.0","title":"Version 1.0","text":"Use R 4.4 Bioconductor 3.19.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-9","dir":"Changelog","previous_headings":"","what":"Version 0.9.0","title":"Version 0.9","text":"Use R 4.3 Bioconductor 3.18.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-8","dir":"Changelog","previous_headings":"","what":"Version 0.8.2","title":"Version 0.8","text":"Add second example caching changes better saving database.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-8-1","dir":"Changelog","previous_headings":"","what":"Version 0.8.1","title":"Version 0.8","text":"Include feedback suggestions Philippine Louail.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-8-2","dir":"Changelog","previous_headings":"","what":"Version 0.8.0","title":"Version 0.8","text":"Add new tutorial Spectra: expandable infrastructure handle mass spectrometry data. Download MassBank data alternative AnnotationHub.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-7","dir":"Changelog","previous_headings":"","what":"Version 0.7.3","title":"Version 0.7","text":"Fix mislabeling columns experimental spectra rows MassBank spectra spectra similarity matrix (thanks CSAMA2023 attendee pointing !).","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-7-1","dir":"Changelog","previous_headings":"","what":"Version 0.7.2","title":"Version 0.7","text":"Use MsBackendSql instead MsqlBackend.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-7-2","dir":"Changelog","previous_headings":"","what":"Version 0.7.1","title":"Version 0.7","text":"Simplify/clarify description spectra data processing.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-7-3","dir":"Changelog","previous_headings":"","what":"Version 0.7.0","title":"Version 0.7","text":"Add section use MsqlBackend compare performance MsBackendMzR MsBackendDataFrame.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-6","dir":"Changelog","previous_headings":"","what":"Version 0.6.3","title":"Version 0.6","text":"Better describe usage alternative SQLite MassBank database file two workshops.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-6-1","dir":"Changelog","previous_headings":"","what":"Version 0.6.2","title":"Version 0.6","text":"Add citation.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-6-2","dir":"Changelog","previous_headings":"","what":"Version 0.6.1","title":"Version 0.6","text":"Adapt changes Spectra >= 1.5.8: export spectraVariableMapping method. Adapt changes CompoundDb >= 0.9.6: \"target_name\" available spectra variable Spectra objects returned CompDb database. Adapt changes MetaboAnnotation >= 0.99.4: also ppm error reported matchMz.","code":""},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-6-3","dir":"Changelog","previous_headings":"","what":"Version 0.6.0","title":"Version 0.6","text":"Remove Spectra object data HMDB. Use CompDb database HMDB annotations.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-5","dir":"Changelog","previous_headings":"","what":"Version 0.5.0","title":"Version 0.5","text":"Add additional vignette describing spectra matching MetaboAnnotation package integration xcms analysis workflow.","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-4","dir":"Changelog","previous_headings":"","what":"Version 0.4.0","title":"Version 0.4","text":"Version used BioC2021 conference. Use MassBank release 2021.03. Restructure document simplify analysis (reduce mzML files 20eV collision energy).","code":""},{"path":[]},{"path":"https://jorainer.github.io/SpectraTutorials/news/index.html","id":"version-0-3","dir":"Changelog","previous_headings":"","what":"Version 0.3.2","title":"Version 0.3","text":"Use MassBank release 2021.02 add expand descriptive text.","code":""}]