MetGEMs

Table of contents

• Introduction

• Getting Started

  • Installation
  • General usage
  • Getting help
  • Citing MetGEM

• Limitations

Introduction

MetGEM is a software package for predicting functional composition of microbial communities using metabolic models.

Getting Started

Installation

MetGEM has been tested with python 3.7 in linux environment only. We highly recommend to install MetGEM with pip in the conda environment.

conda create -n metgem_env python=3.7
conda activate metgem_env
git clone https://github.com/yumyai/MetGEM
cd MetGEM
pip install  -e .

When all is done, running metgem should print out the list of available commands.

>metgem
METGEM - Community Metabolic Network predictor from

Usage: metgem <command> [options] <arguments>

Available command
markp - Calculate reaction
listmodel - List available model

General usage

MetGEM takes two tab-delimited tables as an input

1. ASV/OTU table that list the abundance of ASV/OTU of each sample. example
2. Taxonomy table. example

MetGEM came with prebuilt functional models based on AGORA's model.

Quick usage

1) Download input data

We will use the example files for a quick demonstration how MetGEMs works.

wget https://raw.githubusercontent.com/yumyai/MetGEM/master/examples/feature-table.tsv -O otutab.tsv
wget https://raw.githubusercontent.com/yumyai/MetGEM/master/examples/taxonomy_gg.tsv -O taxtab.tsv

2) Convert ASVs table into KO profiles and EC profiles

MetGEMs come bundle with all kind of models. listmodel command can be used to list all models include with the MetGEMs. The commands to produce KO profile abundance and EC profile abundance are identicle except for the model part.

metgem markp -i otutab.tsv -t taxtab.tsv -m k_core -o output_ko.tsv
metgem markp -i otutab.tsv -t taxtab.tsv -m e_core -o output_ec.tsv

The result should be in output_ko.tsv and output_ec.tsv as KO abundance table and EC numbers abundance table respectively. There are others type of option available (pan, pan-weight), but we found that core and core-weight usually provide a good estimation in most situations.

Use cases

Convert taxonomy abundance table into KO IDs abundance table.

metgem markp -i otutab.tsv -t taxtab.tsv -m k_core -o output_ko.tsv

Convert taxonomy abundance table into EC numbers abundance table.

metgem markp -i otutab.tsv -t taxtab.tsv -m e_core -o output_ec.tsv

Getting help

If you encounter bugs or having futher questions, you can create an issue at the Issues page. I will get in touch as soon as possible.

Citing MetGEM

TODO.

Limitations

• There is no way to independently assign a taxonomic weight outside include directly into a model.
• KO <-> EC conversion is not supported. While it is trivial, we notice that most EC <-> KO conversion are not 1-1 mapping so we would not recommend.