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[{"authors":["admin"],"categories":null,"content":"I am currently a Postdoctoral Fellow in Dr. Marjorie Brand\u0026rsquo;s lab in Ottawa Hospital Research Institute, after gaining a PhD degree by studying in Dr. Mohan Babu\u0026rsquo;s group, University of Regina. In light of the deluge of omics data in life sciences, my research interests focus on computational approaches in systems biology including developing gene expression database, Bioconductor R package. My recent publication revealed rewiring the mitochondrial interactome during neurogensis using both biochemical fractionation proteomics and single cell RNA-seq.\n","date":-62135596800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":-62135596800,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"https://zqzneptune.github.io/authors/admin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/admin/","section":"authors","summary":"I am currently a Postdoctoral Fellow in Dr. Marjorie Brand\u0026rsquo;s lab in Ottawa Hospital Research Institute, after gaining a PhD degree by studying in Dr. Mohan Babu\u0026rsquo;s group, University of Regina. In light of the deluge of omics data in life sciences, my research interests focus on computational approaches in systems biology including developing gene expression database, Bioconductor R package. My recent publication revealed rewiring the mitochondrial interactome during neurogensis using both biochemical fractionation proteomics and single cell RNA-seq.","tags":null,"title":"Q. (Johnson) Zhang","type":"authors"},{"authors":[],"categories":null,"content":" Click on the Slides button above to view the built-in slides feature. Slides can be added in a few ways:\n Create slides using Academic\u0026rsquo;s Slides feature and link using slides parameter in the front matter of the talk file Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes. Further talk details can easily be added to this page using Markdown and $\\rm \\LaTeX$ math code.\n","date":1906549200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1906549200,"objectID":"96344c08df50a1b693cc40432115cbe3","permalink":"https://zqzneptune.github.io/talk/example/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/talk/example/","section":"talk","summary":"An example talk using Academic's Markdown slides feature.","tags":[],"title":"Example Talk","type":"talk"},{"authors":null,"categories":["scRNA-seq"],"content":"The \u0026ldquo;dropout event\u0026rdquo; was termed from the observations of an abundance of zero expression values from the scRNA-seq data. The recent publishsed study demonstrated that genes with zero counts may not have to be dropout anymore, due to their potential biological variations.\nDroplet scRNA-seq is not zero-inflated Nature Biotechnology (2020), by Valentine Svensson\n","date":1579541174,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1579541174,"objectID":"7ddf0bb65e177da9b85aceb95b491efa","permalink":"https://zqzneptune.github.io/post/2020-01-20-zerodrop/","publishdate":"2020-01-20T11:26:14-06:00","relpermalink":"/post/2020-01-20-zerodrop/","section":"post","summary":"The \u0026ldquo;dropout event\u0026rdquo; was termed from the observations of an abundance of zero expression values from the scRNA-seq data. The recent publishsed study demonstrated that genes with zero counts may not have to be dropout anymore, due to their potential biological variations.\nDroplet scRNA-seq is not zero-inflated Nature Biotechnology (2020), by Valentine Svensson","tags":["scRNA-seq","Droplet","Zero-inflation"],"title":"Dropout or not?","type":"post"},{"authors":null,"categories":["Software","Publication"],"content":"The EPIC toolkit was initially published here: Hu, L. Z., et al. \u0026ldquo;EPIC: software toolkit for elution profile-based inference of protein complexes.\u0026rdquo; Nature methods 16.8 (2019): 737-742. Link to the publication\nForked from the orignial repository, I have created RunEPIC to provide the code to run EPIC locally.\n1. Environment The main function in EPIC was implemented in Python, given the headache caused by various libraries, the Anaconda enrionment was used. Simply install Anaconda Python 2.7 version for your convience, and we will start from there.\n2. Prerequisite Let\u0026rsquo;s create the virtual enrionment:\npath to the anaconda directory/bin/conda create -n EPIC python=2.7 anaconda then we need step up some chanels:\n(EPIC)conda config --add channels defaults (EPIC)conda config --add channels bioconda (EPIC)conda config --add channels conda-forge get R installed:\n(EPIC)conda install r start R:\n(EPIC)R install wccsom for computing WCC scores:\n\u0026gt; install.packages(\u0026quot;kohonen\u0026quot;) \u0026gt; install.packages(\u0026quot;https://cran.r-project.org/src/contrib/Archive/wccsom/wccsom_1.2.11.tar.gz\u0026quot;, type = \u0026quot;source\u0026quot;) \u0026gt; q() install conda packages:\nconda install requests scikit-learn beautifulsoup4 mock numpy rpy2 python -mpip install -U matplotlib lastly, make sure Java is installed, so that ClusterOne.jar could be used.\n3. Run EPIC git clone https://github.com/zqzneptune/RunEPIC.git The main.py in EPIC implemented all the functions:\npython directory to RunEPIC/src/main.py \\ -s 11101001 \\ [Directory to Input Folder/] \\ -c [path to the gold standard file ] \\ [Directory to Output Folder/] \\ -o PrefixName \\ -M RF \\ -n 6 \\ -m COMB \\ -f STRING ","date":1576195994,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1576195994,"objectID":"3eb5b4e7c43222d2c4ba325dcecd7551","permalink":"https://zqzneptune.github.io/post/2019-12-12-runepic/","publishdate":"2019-12-12T18:13:14-06:00","relpermalink":"/post/2019-12-12-runepic/","section":"post","summary":"The EPIC toolkit was initially published here: Hu, L. Z., et al. \u0026ldquo;EPIC: software toolkit for elution profile-based inference of protein complexes.\u0026rdquo; Nature methods 16.8 (2019): 737-742. Link to the publication\nForked from the orignial repository, I have created RunEPIC to provide the code to run EPIC locally.\n1. Environment The main function in EPIC was implemented in Python, given the headache caused by various libraries, the Anaconda enrionment was used.","tags":["Python","Interactome","BF-MS"],"title":"Run EPIC","type":"post"},{"authors":null,"categories":null,"content":"","date":1569024000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1569024000,"objectID":"d191d03425e109bcec18ec618f06dd25","permalink":"https://zqzneptune.github.io/publication/j.isci.2019.08.057/","publishdate":"2019-09-04T00:00:00Z","relpermalink":"/publication/j.isci.2019.08.057/","section":"publication","summary":"2019","tags":["Single Cell RNA-seq","Biochemical Fractionation","Mitochondrial","Interactome","Neurogenesis"],"title":"Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis","type":"publication"},{"authors":null,"categories":null,"content":"","date":1566864000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1566864000,"objectID":"19934a68eeaf33edc440413605d0a145","permalink":"https://zqzneptune.github.io/publication/978-981-13-8367-0_6/","publishdate":"2019-08-27T00:00:00Z","relpermalink":"/publication/978-981-13-8367-0_6/","section":"publication","summary":"2018","tags":["Affinity purification","Protein complex","Mass spectrometry","Mitochondrial","Interactome"],"title":"A Tag-Based Affinity Purification Mass Spectrometry Workflow for Systematic Isolation of the Human Mitochondrial Protein Complexes","type":"publication"},{"authors":null,"categories":["Software"],"content":"This R package implements statistical modelling of affinity purification–mass spectrometry (AP-MS) data to compute confidence scores to identify bona fide protein-protein interactions (PPI).\nInstallation The development version can be installed through github:\ndevtools::install_github(repo=\u0026quot;zqzneptune/SMAD\u0026quot;) library(SMAD) Input Data A demo data.frame was provided as a hint how the input data should strcutured in order to run the scoring functions:\ndata(TestDatInput) colnames(TestDataInput) [1] \u0026quot;idRun\u0026quot; \u0026quot;idBait\u0026quot; \u0026quot;idPrey\u0026quot; \u0026quot;countPrey\u0026quot; \u0026quot;lenPrey\u0026quot; idRun idBait idPrey countPrey lenPrey Unique ID of one AP-MS run Bait ID Prey ID Prey peptide count Protein sequence length of the prey In case of duplcates, a suffix or prefix of e.g. \u0026ldquo;A\u0026rdquo;, \u0026ldquo;B\u0026rdquo; could be added to idRun in order to make \u0026ldquo;idRun-idBait\u0026rdquo; combination unique to each replicate.\nRun scoring 1. CompPASS Comparative Proteomic Analysis Software Suite (CompPASS) is based on spoke model. This algorithm was developed by Dr. Mathew Sowa for defining the human deubiquitinating enzyme interaction landscape (Sowa, Mathew E., et al., 2009). The implementation of this algorithm was inspired by Dr. Sowa\u0026rsquo;s online tutorial. The output includes Z-score, S-score, D-score and WD-score. In its implementation in BioPlex 1.0 (Huttlin, Edward L., et al., 2015) and BioPlex 2.0 (Huttlin, Edward L., et al., 2017), a naive Bayes classifier that learns to distinguish true interacting proteins from non-specific background and false positive identifications was included in the compPASS pipline. This function was optimized from the source code.\nThe input data.frame, datInput, should include:idRun, idBait, idPrey and countPrey.\ndatScore \u0026lt;- CompPASS(datInput) 2. DICE The Dice coefficient is used to score the interaction scores across prey pair-wise combinations, which was proposed by (Bing Zhang et al., 2008)\nThe input data.frame, datInput, should include:idRun and idPrey.\ndatScore \u0026lt;- DICE(datInput) 3. Hart Hart scoring algorithm is based on a hypergeometric distribution error model (Hart et al., 2007).\nThe input data.frame, datInput, should include:idRun and idPrey.\ndatScore \u0026lt;- Hart(datInput) 4. HGScore HGScore algorithm is based on a hypergeometric distribution error model (Hart et al., 2007) with incorporation of NSAF (Zybailov, Boris, et al., 2006). This algorithm was first introduced to predict the protein complex network of Drosophila melanogaster (Guruharsha, K. G., et al., 2011). This scoring algorithm was based on matrix model.\nThe input data.frame, datInput, should include:idRun, idPrey, countPrey and lenPrey.\ndatScore \u0026lt;- HG(datInput) 5. PE PE incorporated both spoke and matrix model as repored in (Sean R. Collins, et al., 2007).\nThe input data.frame, datInput, should include:idRun, idBait and idPrey.\ndatScore \u0026lt;- PE(datInput) License MIT @ Qingzhou Zhang\n","date":1558537994,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1558537994,"objectID":"7288a74d1fd5f425fb7d0f945a123196","permalink":"https://zqzneptune.github.io/post/2019-05-22-smad/","publishdate":"2019-05-22T09:13:14-06:00","relpermalink":"/post/2019-05-22-smad/","section":"post","summary":"This R package implements statistical modelling of affinity purification–mass spectrometry (AP-MS) data to compute confidence scores to identify bona fide protein-protein interactions (PPI).\nInstallation The development version can be installed through github:\ndevtools::install_github(repo=\u0026quot;zqzneptune/SMAD\u0026quot;) library(SMAD) Input Data A demo data.frame was provided as a hint how the input data should strcutured in order to run the scoring functions:\ndata(TestDatInput) colnames(TestDataInput) [1] \u0026quot;idRun\u0026quot; \u0026quot;idBait\u0026quot; \u0026quot;idPrey\u0026quot; \u0026quot;countPrey\u0026quot; \u0026quot;lenPrey\u0026quot; idRun idBait idPrey countPrey lenPrey Unique ID of one AP-MS run Bait ID Prey ID Prey peptide count Protein sequence length of the prey In case of duplcates, a suffix or prefix of e.","tags":["R","Proteomics","Interactome","AP-MS"],"title":"Statistical Modelling of AP-MS Data (SMAD)","type":"post"},{"authors":[],"categories":[],"content":"Create slides in Markdown with Academic Academic | Documentation\n Features Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Controls Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot; if porridge == \u0026quot;blueberry\u0026quot;: print(\u0026quot;Eating...\u0026quot;) Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\n Fragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}} Press Space to play!\nOne Two Three \n A fragment can accept two optional parameters:\n class: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}} Press the S key to view the speaker notes!\n Only the speaker can read these notes Press S key to view Themes black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026quot;/img/boards.jpg\u0026quot; \u0026gt;}} {{\u0026lt; slide background-color=\u0026quot;#0000FF\u0026quot; \u0026gt;}} {{\u0026lt; slide class=\u0026quot;my-style\u0026quot; \u0026gt;}} Custom CSS Example Let\u0026rsquo;s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; } Questions? Ask\nDocumentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"https://zqzneptune.github.io/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Academic's Slides feature.","tags":[],"title":"Slides","type":"slides"},{"authors":null,"categories":["Awsome"],"content":"A collection of resources regarding affinity purification mass spectrometry proteomics for the identification of protein-protein interactions.\nAlgorithms Year Algorithm Publication Implementation 2006 SAI (socio-affinity index) Anne-Claude Gavin et al., Nature 2007 partial least squares based regression model Rob M Ewing et al., Mol Syst Biol 2007 PE (purification enrichment) Sean R. Collins et al., Molecular \u0026amp; Cellular Proteomics SMAD 2007 Hart G Traver Hart et al., BMC Bioinformatics SMAD 2008 Dice coefficient Bing Zhang et al., Bioinformatics SMAD 2008 PP-NSAF Mihaela E. Sardiu et al., PNAS 2009 CompPASS Mathew E.Sowa et al., Cell SMAD 2010 Decontaminator MathieLavallée-Adam et al. J. Proteome Res. 2011 MiST Stefanie Jäger et al., Nature mist 2011 HGScore K.G.Guruharsha et al., Cell SMAD 2011 SAINT Hyungwon Choi et al., Nature Methods SAINT 2015 SFINX Kevin Titeca et al. J. Proteome Res. SFINX Datasets Year Species Abstract Publication 2002 S.cerevisiae 725 bait proteins, one-step immuno-affinity purification based on the Flag epitope tag Yuen Ho et al., Nature 2002 S.cerevisiae tandem-affinity purification processed 1,739 genes Anne-Claude Gavin et al., Nature 2006 S.cerevisiae Genome-wide TAP–MS, first introduced Anne-Claude Gavin et al., Nature 2009 H.sapiens Human Deubiquitinating Enzyme Interaction Landscape(DUB) Mathew E.Sowa et al., Cell 2010 S.cerevisiae kinase and phosphatase interaction (KPI) network Ashton Breitkreutz et al., Science 2009 H.sapiens autophagy interaction network (AIN) Christian Behrends et al., Nature 2011 D.melanogaster a Drosophila protein interaction map (DPiM) K.G.Guruharsha et al., Cell 2012 S.cerevisiae membrane-protein complexes in yeast Mohan Babu et al., Nature 2014 H.sapiens A comprehensive chromatin-related protein-protein interaction map Edyta Marcon et al., Cell Reports 2015 H.sapiens BioPlex v1 Edward L. Huttlin et al., Cell 2015 H.sapiens Autism Spectrum Disorders related Jingjing Li et al., Cell Systems 2016 H.sapiens PPI mapping of 50 unannotated mitochondrial proteins Brendan J.Floyd et al., Molecular Cell 2016 H.sapiens Comprehensive mitochondrial sirtuin interactome Wen Yang et al., Cell 2017 H.sapiens BioPlex v2 Edward L. Huttlin et al., Cell 2017 E.coli Global landscape of cell envelope protein complexes Mohan Babu et al., Nature Biotech 2017 H.sapiens A Map of Human Mitochondrial Protein Interactions related to Neurodegeneration Ramy H.Malty et al., Cell Systems ","date":1531063394,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1531063394,"objectID":"9437dbc4b77c086996be518e755ea4a3","permalink":"https://zqzneptune.github.io/post/2018-07-08-apms/","publishdate":"2018-07-08T09:23:14-06:00","relpermalink":"/post/2018-07-08-apms/","section":"post","summary":"A collection of resources regarding affinity purification mass spectrometry proteomics for the identification of protein-protein interactions.\nAlgorithms Year Algorithm Publication Implementation 2006 SAI (socio-affinity index) Anne-Claude Gavin et al., Nature 2007 partial least squares based regression model Rob M Ewing et al., Mol Syst Biol 2007 PE (purification enrichment) Sean R. Collins et al., Molecular \u0026amp; Cellular Proteomics SMAD 2007 Hart G Traver Hart et al.","tags":["AP-MS","Interactome"],"title":"Awsome affinity purification mass spectrometry (AP-MS)","type":"post"},{"authors":null,"categories":null,"content":"","date":1511740800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1511740800,"objectID":"b08780d0d7efa313f23283a4f8efba74","permalink":"https://zqzneptune.github.io/publication/nbt.4024/","publishdate":"2017-11-27T00:00:00Z","relpermalink":"/publication/nbt.4024/","section":"publication","summary":"2017","tags":["AP-MS","E.coli","Interactome","Landscape"],"title":"Global landscape of cell envelope protein complexes in Escherichia coli","type":"publication"},{"authors":null,"categories":null,"content":"","date":1510876800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1510876800,"objectID":"fce5f6f4ccb031c1dd5a51a68cafc9c5","permalink":"https://zqzneptune.github.io/publication/j.cels.2017.10.010/","publishdate":"2017-11-08T00:00:00Z","relpermalink":"/publication/j.cels.2017.10.010/","section":"publication","summary":"2017","tags":["AP-MS","Mitochondrial","Interactome","Neurodegeneration"],"title":"A Map of Human Mitochondrial Protein Interactions Linked to Neurodegeneration Reveals New Mechanisms of Redox Homeostasis and NF-κB Signaling","type":"publication"},{"authors":null,"categories":null,"content":"","date":1505952000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1505952000,"objectID":"6aa1d9e09ed86cdcf2c7437194c129fc","permalink":"https://zqzneptune.github.io/publication/oncotarget.22413/","publishdate":"2017-09-21T00:00:00Z","relpermalink":"/publication/oncotarget.22413/","section":"publication","summary":"2017","tags":["Whole Exome Sequencing","Mitochondrial","Interactome","Renal Oncocytoma"],"title":"Renal oncocytoma characterized by the defective complex I of the respiratory chain boosts the synthesis of the ROS scavenger glutathione","type":"publication"},{"authors":null,"categories":null,"content":"","date":1412640000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1412640000,"objectID":"c0a8cbb408ad43c03def7c0e376932f7","permalink":"https://zqzneptune.github.io/publication/rged/","publishdate":"2014-09-24T00:00:00Z","relpermalink":"/publication/rged/","section":"publication","summary":"2014","tags":["Database","Gene Expression"],"title":"Renal Gene Expression Database (RGED) a relational database of gene expression profiles in kidney disease","type":"publication"},{"authors":null,"categories":["Database","Publication"],"content":"The Renal Gene Expression Database (RGED) is online. Number of samples colleceted in the database reached around ~10,000, including DNA microarray and RNA-seq experiments.\nJust go ahead to this URL: http://rged.wall-eva.net/\n","date":1406049194,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1406049194,"objectID":"a7313c4b7d36d710550bcf974cf8d75c","permalink":"https://zqzneptune.github.io/post/2014-09-23-rged/","publishdate":"2014-07-23T01:13:14+08:00","relpermalink":"/post/2014-09-23-rged/","section":"post","summary":"The Renal Gene Expression Database (RGED) is online. Number of samples colleceted in the database reached around ~10,000, including DNA microarray and RNA-seq experiments.\nJust go ahead to this URL: http://rged.wall-eva.net/","tags":["Renal Disease","R","Gene Expresssion","PHP"],"title":"Hello, RGED!","type":"post"},{"authors":null,"categories":null,"content":" Sample Description Sample Name Species Tissue Description 384-M Mus musculus Kidney Healthy control 403-P Mus musculus Kidney PKD1 Knock out Cell phases assignment Number of cells Distribution of total UMI count Distribution of reads in mitochondrial genes ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"c11126d85af193998d7225d43bae4a35","permalink":"https://zqzneptune.github.io/project/01_qc_preprocess/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/project/01_qc_preprocess/","section":"project","summary":" Sample Description Sample Name Species Tissue Description 384-M Mus musculus Kidney Healthy control 403-P Mus musculus Kidney PKD1 Knock out Cell phases assignment Number of cells Distribution of total UMI count Distribution of reads in mitochondrial genes ","tags":null,"title":"1. QC and Preprocessing","type":"project"}]