This repository contains all data and Jupyter notebooks for the tax-credit project, a standardized and extensible evaluation framework for assessing short-read taxonomic classifiers.
To view static versions of the reports, start here.
This repository contains Python 3 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 seen in the QIIME-1 taxonomy assignment notebook.
To clone this repository:
git clone https://github.com/caporaso-lab/tax-credit-data.git
The tax-credit python 3 package is hosted in the tax-credit-code repository.
git clone https://github.com/caporaso-lab/tax-credit-code.git
cd tax-credit-code
pip install .
OR
pip install https://github.com/caporaso-lab/tax-credit-code/archive/master.zip
To extend the 16S/ITS classification results you will need reference data sets:
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
To view and interact with Jupyter Notebook, change into the /tax-credit-data/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:
Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, Huttley GA, Caporaso JG. (2017) Optimizing taxonomic classification of marker gene sequences with QIIME 2's q2-feature-classifier. Microbiome 6:90 https://doi.org/10.1186/s40168-018-0470-z