To view static versions of the reports , start here.
This repository contains python-3 code and Jupyter notebooks, but some taxonomy assignment methods (e.g., using QIIME-1 legacy methods) may require different python or software versions. Hence, we use conda parallel environments to support comparison of myriad methods in a single framework.
The first step is to install conda and install QIIME2 following the instructions provided here.
An example of how to load different environments to support other methods can be see in the QIIME-1 taxonomy assignment notebook.
The library code and Jupyter Notebooks are then installed as follows:
git clone https://github.com/gregcaporaso/tax-credit.git
cd tax-credit/
conda create -n tax-credit pip
pip install .
The analyses included here can all be run in standard, modern laptop, provided you don't mind waiting a few hours on the most memory-intensive step (taxonomy classification of millions of sequences). With the exception of the q2-feature-classifier naive-bayes* classifier sweeps, which were run on a high-performance cluster, all analyses presented in tax-credit were run in a single day using a MacBook Pro with the following specifications:
OS OS X 10.11.6 "El Capitan"
Processor 2.3 GHz Intel Core i7
Memory 8 GB 1600 MHz DDR3
If you intend to perform extensive parameter sweeps on a classifier (e.g., several hundred or more parameter combinations), you may want to consider running these analyses using cluster resources, if available.
To view and interact with Jupyter Notebook, change into the /tax-credit/ipynb directory and run Jupyter Notebooks from the terminal with the command:
jupyter notebook index.ipynb
An example notebook made by K. Silliman is called tax-credit_example.ipynb
The notebooks menu should open in your browser. From the main index, you can follow the menus to browse different analyses, or use File --> Open from the notebook toolbar to access the full file tree.