MAGpy

MAGpy is a Snakemake pipeline for downstream analysis of metagenome-assembled genomes (MAGs) (pronounced mag-pie)

Citation

Robert Stewart, Marc Auffret, Tim Snelling, Rainer Roehe, Mick Watson (2018) MAGpy: a reproducible pipeline for the downstream analysis of metagenome-assembled genomes (MAGs). Bioinformatics bty905, bty905

Clean your MAGs

There are a few things you will need to do before you run MAGpy, and these are due to limitations imposed by the software MAGpy runs, rather than by MAGpy itself.

These are:

• the names of contigs in your MAGs must be globally unique. Some assemblers, e.g. Megahit, output very generic contig names e.g. "scaffold_22" which, if you have assembled multiple samples, may be duplicated in your MAGs. This is not allowed. BioPython and/or BioPerl can help you rename your contigs
• The MAG FASTA files must start with a letter
• The MAG FASTA files should not have any "." characters in them, other than the final . before the file extension e.f. mag1.faa is fine, mag.1.faa is not

NEW RELEASE - June 2021

• updated to Sourmash 4.1.1
• updated to PhyloPhlAn 3.0.2
• updated to DIAMOND 2.0.9

Install conda

Skip if you already have it. Instructions are here

Clone the repo

git clone https://github.com/WatsonLab/MAGpy.git
cd MAGpy

Install Snakemake and mamba

Skip if you already have them

conda env create -f envs/install.yaml 
conda activate magpy_install

Run tests and install conda envs:

snakemake -rp -s MAGpy --cores 1 --use-conda test

Build the databases

This will build a DIAMOND database of the whole of UniProt TREMBL, so you will need to give it a lot of resources (RAM) - try 256Gb.

rm -rf magpy_dbs
snakemake -rp -s MAGpy --cores 16 --use-conda setup

Run MAGpy

snakemake -rp -s MAGpy --use-conda --cores 8

For large workflows, I recommend you use cluster or cloud execution.

Also, for any large number of MAGs, PhyloPhlAn will take a long time - e.g. a few weeks for a couple of thousand MAGs.