An easy-to-use workflow for generating context specific genome-scale metabolic models and predicting metabolic interactions within microbial communities directly from metagenomic data.
metaGEM is a Snakemake workflow that integrates an array of existing bioinformatics and metabolic modeling tools, for the purpose of predicting metabolic interactions within bacterial communities of microbiomes. From whole metagenome shotgun datasets, metagenome assembled genomes (MAGs) are reconstructed, which are then converted into genome-scale metabolic models (GEMs) for in silico simulations. Additional outputs include abundance estimates, taxonomic assignment, growth rate estimation, pangenome analysis, and eukaryotic MAG identification.
You can access the metaGEM-generated results for the publication here.
You can set up and use metaGEM on the cloud by following along the google colab notebook.
Please note that google colab does not provide the computational resources necessary to fully run metaGEM on a real dataset. This notebook demonstrates how to set up and use metaGEM by perfoming the first steps in the workflow on a toy dataset.
You can set up metaGEM on your cluster with just one line of code 😉
git clone https://github.com/franciscozorrilla/metaGEM.git && cd metaGEM && rm -r .git && bash env_setup.sh
Congratulations, you can now start using metaGEM. Verify your installation by using the check task:
bash metaGEM.sh --task check
Please consult the setup page in the wiki for further configuration instructions.
Run metaGEM without any arguments to see usage instructions:
bash metaGEM.sh
Usage: bash metaGEM.sh [-t|--task TASK]
[-j|--nJobs NUMBER OF JOBS]
[-c|--cores NUMBER OF CORES]
[-m|--mem GB RAM]
[-h|--hours MAX RUNTIME]
[-l|--local]
Options:
-t, --task Specify task to complete:
SETUP
createFolders
downloadToy
organizeData
check
CORE WORKFLOW
fastp
megahit
crossMap
concoct
metabat
maxbin
binRefine
binReassemble
extractProteinBins
carveme
memote
organizeGEMs
smetana
extractDnaBins
gtdbtk
abundance
BONUS
grid
prokka
roary
eukrep
eukcc
VISUALIZATION (in development)
stats
qfilterVis
assemblyVis
binningVis
taxonomyVis
modelVis
interactionVis
growthVis
-j, --nJobs Specify number of jobs to run in parallel
-c, --nCores Specify number of cores per job
-m, --mem Specify memory in GB required for job
-h, --hours Specify number of hours to allocated to job runtime
-l, --local Run jobs on local machine for non-cluster usage
metaGEM can be used to explore your own gut microbiome sequencing data from at-home-test-kit services such as unseen bio. The following tutorial showcases the metaGEM workflow on two unseenbio samples.
Refer to the wiki for additional usage tips, frequently asked questions, and implementation details.
- Quality filter reads with fastp
- Assembly with megahit
- Draft bin sets with CONCOCT, MaxBin2, and MetaBAT2
- Refine & reassemble bins with metaWRAP
- Taxonomic assignment with GTDB-tk
- Relative abundances with bwa and samtools
- Reconstruct & evaluate genome-scale metabolic models with CarveMe and memote
- Species metabolic coupling analysis with SMETANA
- Growth rate estimation with GRiD, SMEG or CoPTR
- Pangenome analysis with roary
- Eukaryotic draft bins with EukRep and EukCC
If you want to see any new additional or alternative tools incorporated into the metaGEM workflow please raise an issue or create a pull request. Snakemake allows workflows to be very flexible, so adding new rules is as easy as filling out the following template and adding it to the Snakefile:
rule package-name:
input:
rules.rulename.output
output:
f'{config["path"]["root"]}/{config["folder"]["X"]}/{{IDs}}/output.file'
message:
"""
Helpful and descriptive message detailing goal of this rule/package.
"""
shell:
"""
# Well documented command line instructions go here
# Load conda environment
set +u;source activate {config[envs][package]};set -u;
# Run tool
package-name -i {input} -o {output}
"""
The metaGEM workflow was used in the following publication(s):
Plastic-degrading potential across the global microbiome correlates with recent pollution trends
Jan Zrimec, Mariia Kokina, Sara Jonasson, Francisco Zorrilla, Aleksej Zelezniak
bioRxiv 2020.12.13.422558; doi: https://doi.org/10.1101/2020.12.13.422558
metaGEM: reconstruction of genome scale metabolic models directly from metagenomes
Francisco Zorrilla, Filip Buric, Kiran R Patil, Aleksej Zelezniak
Nucleic Acids Research, 2021; gkab815, https://doi.org/10.1093/nar/gkab815
Please reach out with any comments, concerns, or discussions regarding metaGEM.
