We used Bamtocov in the tutorial, this page about CoverM just does the same thing but with a different tool. CoverM is especially handy if you want to delegate the mapping.

CoverM is a popular tool used in metagenomics hence our choice to make a dedicated paragraph to cover it :)

Installing CoverM

We installed CoverM inside the “mapping” conda environment, otherwise it’s easy installable from BioConda.

# Activate the environment
conda activate mapping

# Check installation
coverm --version

Running CoverM

We have three BAM files in bams/, each corresponding to the mapping of one sample against the co-assembly.

# Calculate coverage metrics for all contigs
coverm contig \
  -b /*.bam \
  -m count mean covered_fraction tpm \
  -o abundance_table.tsv \
  --exclude-supplementary \
  -t 8

Some explanations about the parameters used:

• contig: is a subcommand which tells CoverM to calculate metrics at the contig level
• -b: specifies the input BAM files (you can specify multiple files, also using wildcards)
• -m: specifies the metrics to calculate, in this case:
  • count: the number of reads mapped to the contig
  • mean: mean coverage depth across the contig
  • covered_fraction: proportion of contig covered by reads (0-1)
  • tpm: Transcripts Per Million (normalized abundance)
• -o: specifies the output file name
• --exclude-supplementary: excludes supplementary alignments from the calculations
• -t: number of threads to use

Output File

The output is a TSV file with the following structure:

Each sample gets one column per requested metric. In this case, with 3 samples and 4 metrics, you’ll have 13 columns total (1 for contig names + 3 samples × 4 metrics).

Note: Raw counts are the most useful value to perform statistical analyses, while TPM values are useful for a first “eyeballing” comparison of abundances between samples, as they account for both sequencing depth and contig length.