A metagenome binning tool based on semi-contrastive learning method using the framework of BYOL (Bootstrap Your Own Latent) model. It only requires positive augmentated pairs for contrastive learning. As input, it integrates k-mer sequence embedding from GenomeFace and sampled coverage profiles using binomial sampling of contigs of augmented pairs for training. The embedding space obtained after training is used on Leiden algorithm to obtain metagenomic bins.
conda env create -n mcdevol_env --file=environment.yml
conda activate mcdevol_env
export PATH=${PATH}/$(pwd)/mcdevol
python mcdevol.py --help
Require glibc2.25. At the moment, McDevol scripts are tested only on Linux system. On cluster node, try creating conda environment using CONDA_OVERRIDE_GLIBC=2.17 CONDA_OVERRIDE_CUDA=10.2 conda env create --file=environment.yml if the default command doesn't work.
usage: mcdevol.py [-h] (-a ABUNDANCE | -i INPUTDIR) -c CONTIGS [-l MINLENGTH] [-o OUTDIR] [-n NCORES] [--abundformat ABUNDFORMAT] [-v] [-f NFRAGMENTS] [--multi_split]
McDevol: An accurate metagenome binning of contigs based on decovolution of abundance and k-mer embedding
optional arguments:
-h, --help show this help message and exit
-a ABUNDANCE, --abundance ABUNDANCE
abundance file in TSV format separated by tabs
-i INPUTDIR, --inputdir INPUTDIR
directory that contains SAM files
-c CONTIGS, --contigs CONTIGS
contigs fasta (or zip)
-l MINLENGTH, --minlength MINLENGTH
minimum length of contigs to be considered for binning
-o OUTDIR, --outdir OUTDIR
output directory
-n NCORES, --ncores NCORES
Number of cores to use
--abundformat ABUNDFORMAT
Format of abundance ('std|metabat', default='std') std:[contigname, s1meancov, s2meancov,...,sNmeancov]; metabat:[contigName, contigLen, totalAvgDepth,
s1meancov, s1varcov,...,sNmeancov, sNvarcov]
-v, --version print version and exit
-f NFRAGMENTS, --nfragments NFRAGMENTS
number of augumented fragments to generate
--multi_split split clusters based on sample id, separator (default='C')
python mcdevol.py -i <samfilesdir> -c <contigseq.fasta> -o <outputdir> --abundformat metabat -n 24
-i and -a are mutually exclusive input.
For multi-sample binning run
python mcdevol.py -i <samfilesdir> -c <contigseq.fasta> -o <outputdir> --abundformat metabat -n 24 --multi-split