MicrobiomeUtils

Summary

A lightweight Python utility to generate beta-diversity distance matrix + pcoa and taxonomic summary from a feature table. The stated goal of this utility is to avoid using .biom tables whose usage was enforced in later QIIME releases. This utility enables the computation of beta diversity metrics, emperor interactive figures and taxonomic summaries without going through the complex installation process inherent to QIIME or dealing with .qza files. Functions for alpha diversity metrics generation were not implemented in this utility because it can be already efficiently acomplished using the Rarefaction Tool Kit (RTK) package - https://github.com/hildebra/Rarefaction/ - which I recommend.

Installation

This utility was written in Python 3.9.0 using scikit-bio v0.5.6. Once Python 3.9.0 is installed, run pip install numpy, pip install "scikit-bio==0.5.6" or pip install scikit-bio. Also install emperor: pip install emperor. Then clone this repo : git clone https://github.com/jtremblay/microbiomeutils.git and run microbiomeutils.py with the appropriate arguments as described below:

Help

microbiomeutils.py -h

microbiomeutils.py betadiv -h

microbiomeutils.py pcoa -h

microbiomeutils.py emperor -h

microbiomeutils.py taxsum -h

Data format

Takes in input a tab-separated value (tsv) feature table with just one header line that can start with : '#FEATURE_ID', '#OTU ID', '#FEATURE ID', '#FEATURE' or '#FEATUREID' string. Script won't work with multi-line header. See example input file in ./data/feature_table.tsv

Computing Bray-Curtis beta-diversity

microbiomeutils.py betadiv -i ./data/feature_table.tsv -m bray-curtis > ./data/bc_res.tsv

microbiomeutils.py pcoa -i ./data/bc_res.tsv > ./data/bc_res_coords.tsv

Computing Weighted-Unifrac beta-diversity

microbiomeutils.py betadiv -i ./data/feature_table.tsv -m weighted-unifrac --infile-tree ./data/tree.fasttree > ./data/wuf_res.tsv

microbiomeutils.py pcoa -i ./data/wuf_res.tsv > ./data/wuf_res_coords.tsv

Computing taxonomic summaries

microbiomeutils.py taxsum -i ./data/feature_table.tsv -l 1 > data/taxonomy_L1.tsv

microbiomeutils.py taxsum -i ./data/feature_table.tsv -l 2 > data/taxonomy_L2.tsv

microbiomeutils.py taxsum -i ./data/feature_table.tsv -l 3 > data/taxonomy_L3.tsv

microbiomeutils.py taxsum -i ./data/feature_table.tsv -l 4 > data/taxonomy_L4.tsv

microbiomeutils.py taxsum -i ./data/feature_table.tsv -l 5 > data/taxonomy_L5.tsv

microbiomeutils.py taxsum -i ./data/feature_table.tsv -l 6 > data/taxonomy_L6.tsv

microbiomeutils.py taxsum -i ./data/feature_table.tsv -l 7 > data/taxonomy_L7.tsv

Generating 3d PCoA plots with emperor

microbiomeutils.py emperor -i ./data/wuf_res_coords.tsv -m ./data/mapping_file.tsv -o ./data/weighted_unifrac_3d_plot

Citation

If you use microbiomeutils in your work, please cite:

Tremblay, Julien

microbiomeutils 0.9.4 : Microbiome utilities

https://github.com/jtremblay/microbiomeutils