TAX CREdiT: TAXonomic ClassifieR Evaluation Tool

A standardized and extensible evaluation framework for taxonomic classifiers

To view static versions of the reports , start here.

Environment

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.

Setup and install

The library code and IPython Notebooks are then installed as follows:

git clone https://github.com/gregcaporaso/tax-credit.git
cd tax-credit/
pip install .

Finally, download and unzip the reference databases:

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

Equipment

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.

Using the Jupyter Notebooks included in this repository

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

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.

Citing

A publication is on its way! For now, if you use any of the data or code included in this repository, please cite https://github.com/caporaso-lab/tax-credit