Running MetaCompass

Running MetaCompass

MetaCompass is run via the script: 'go_metacompass.py' found in the base installation directory.

Usage info is as follows:

usage: go_metacompass.py [-h] [-c [CONFIG]] [-S [SAMPLES]] [-P [PAIRED]]
                         [-U [UNPAIRED]] [-d [DB]] [-i [ITERATIONS]]
                         [-r [REF]] [-p [PICKREF]] [-m [MINCOV]]
                         [-g [MINCTGLEN]] [-l [READLEN]] [-f FILTER] [-b] -o
                         [OUTDIR] [-e [SAMPLEID]] [-v] [-k] [-t [THREADS]]
                         [-q [QSUB]] [-F] [-u]

snakemake and metacompass params

optional arguments:
  -h, --help            show this help message and exit

required:
  -c [CONFIG], --config [CONFIG]
                        config (json) file, set read length etc
  -S [SAMPLES], --Samples [SAMPLES]
                        Provide file with fq reads (1 file per line)
  -P [PAIRED], --paired [PAIRED]
                        Provide comma separated list of paired reads
                        (r1.1.fq,r1.2.fq)
  -U [UNPAIRED], --unpaired [UNPAIRED]
                        Provide comma separated list of unpaired reads
                        (r1.fq,r2.fq,r3.fq)

metacompass:
  -d [DB], --db [DB]    marker gene database directory
  -i [ITERATIONS], --iterations [ITERATIONS]
                        num iterations
  -r [REF], --ref [REF]
                        reference genomes
  -p [PICKREF], --pickref [PICKREF]
                        depth or breadth
  -m [MINCOV], --mincov [MINCOV]
                        min coverage to assemble
  -g [MINCTGLEN], --minctglen [MINCTGLEN]
                        min contig length
  -l [READLEN], --readlen [READLEN]
                        max read length
  -f FILTER, --filter FILTER
                        filter recruited genomes with mash (experimental)

output:
  -b, --clobber         clobber output directory (if exists?)
  -o [OUTDIR], --outdir [OUTDIR]
                        output directory? (cwd default)
  -e [SAMPLEID], --sampleid [SAMPLEID]
                        sample id (fq prefix is default)
  -v, --verbose         verbose
  -k, --keepoutput      keep all output generated (default is to delete all
                        but final fasta files)

performance:
  -t [THREADS], --threads [THREADS]
                        num threads
  -q [QSUB], --qsub [QSUB]

snakemake:
  -F, --Force           force snakemake to rerun
  -u, --unlock          unlock snakemake locks

Typical MetaCompass Command Line

A typical MetaCompass command for assembling a metagenomic sample "Sample" containing paired-end and singleton reads would be:

python3 go_metacompass.py -P Sample.1.fastq,Sample.2.fastq -U Sample.singleton.fastq -o Sample_output

where:

• Sample.1.fastq and Sample.2.fastq contain forward and reverse paired-end reads, respectively
• Sample.singleton.fastq contains unpaired reads
• Sample_output is output directory

Parameters and Options to Consider when Running MetaCompas

Rerefence fasta file [-r [REF]]

By default, MetaCompass uses a marker gene approach to select references that are present in the metagenome. However, if the set of genomes in the sample is known or you are trying to assemble a particular known genome from the sample we recommend using this option.

Reference-guided assembly parameres

Choice of coverage to assign reads to genomes [-p [PICKREF]]

A key step in the assembly process is dealing with reads aligning to multiple genomes.

• By default, MetaCompass assign each multi-mapped read to the genome with the highest "breadth" of coverage. This parameter choice works best for low-abundance genomes.
• There is also the option of using "depth" of coverage to assign multi-mapped reads.
• The parameter "all" uses all read mappings to assemble the genomes. This option generates redundancy in the final contigs.

Minimum depth of coverage to assemble each genome [-m [MINCOV]]

An advantage of MetaCompass reference-guided assembly over De novo assembly is the capacity of assembling low-abundance bacterial genomes. After each read is assigned to a particular genome, the contigs are build using a minimum depth of coverage threshold. For best results on low-abundance bacterial genomes, we recommend using MINCOV between 1 and 3.