The typical genomics experiment can be divided in two main sections:

• The data reduction from raw FASTQ files to some count tables (bioinformatics)
• The statistical analysis of those count tables (inferential statistics)

The mapping of reads against the co-assembly allows us to quantify the coverage of each contig in each sample.

We can use CoverM to produce an abundance table from a set of BAM files.

Installing CoverM

CoverM is easy installable from BioConda. We installed it in our “metadenovo” environment before, but you can create a dedicated environment for CoverM like this:

# Create and activate the environment
mamba create -n coverm -c conda-forge -c bioconda coverm
conda activate coverm

# Check installation
coverm --version

Running CoverM

We have a set of BAM files, each corresponding to the mapping of one sample against the co-assembly.

# Calculate coverage metrics for all contigs
coverm contig \
  -b ${bam_folder}/*.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.