To view static versions of the reports presented in Bokulich, et al., (Microbiome, under review), 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 create a conda environment with the necessary dependencies. This requires installing miniconda 3 to manage parallel python environments. After miniconda (or another conda version) is installed, proceed with installing QIIME 2.

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 IPython Notebooks are then installed as follows:

cd $HOME/projects
git clone https://github.com/gregcaporaso/short-read-tax-assignment.git
cd $HOME/projects/short-read-tax-assignment/code
sudo pip install .

To run the unit tests, you should install run:

cd $HOME/projects/short-read-tax-assignment/code
nosetests .

Finally, download and unzip the reference databases:

cd $HOME/ref_dbs/
wget https://unite.ut.ee/sh_files/sh_qiime_release_20.11.2016.zip
wget ftp://greengenes.microbio.me/greengenes_release/gg_13_5/gg_13_8_otus.tar.gz
unzip sh_qiime_release_20.11.2016.zip
tar -xzf gg_13_8_otus.tar.gz

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). All analyses presented in tax-credit were run in a single afternoon 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

To view and interact with Jupyter Notebook, change into the /short-read-tax-assignment/ipynb directory and run Jupyter Notebooks from the terminal with the command:

jupyter notebook index.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.

If you use any of the data or code included in this repository, please cite with:

Bokulich NA, Rideout JR, Kopylova E, Bolyen E, Patnode J, Ellett Z, McDonald D, Wolfe B, Maurice CF, Dutton RJ, Turnbaugh PJ, Knight R, Caporaso JG. (2015) A standardized, extensible framework for optimizing classification improves marker-gene taxonomic assignments. PeerJ PrePrints 3:e1156 https://dx.doi.org/10.7287/peerj.preprints.934v1