This is the multicov pipeline from the Sequana project

If you already have all requirements, install the package using pip:

pip install sequana_multicov --upgrade

Scan BED files in a directory and set up the pipeline (replace DATAPATH with your input directory):

sequana_multicov --input-directory DATAPATH

To provide a reference FASTA file for GC content plots:

sequana_multicov --input-directory DATAPATH --reference-file genome.fa

To provide a GenBank annotation file for event annotation:

sequana_multicov --input-directory DATAPATH --annotation-file genome.gbk

This creates a multicov/ directory with the pipeline and configuration file. Execute the pipeline locally:

cd multicov
sh multicov.sh

If you are familiar with Snakemake, you can also run the pipeline directly:

snakemake -s multicov.rules --cores 4 --stats stats.txt

See .sequana/profile/config.yaml to tune Snakemake behaviour (cores, cluster settings, etc.).

With apptainer, initiate the working directory as follows:

sequana_multicov --input-directory DATAPATH  --apptainer-prefix ~/.sequana/apptainers

Images are downloaded in the shared location. Then:

cd multicov
sh multicov.sh

BED files must have 3 or 4 tab-separated columns:

chr1    1    10
chr1    2    11
...
chr2    1    20
chr2    2    21
...

where the first column is the chromosome/contig name, the second is the position (1-based, sorted), and the third is the coverage depth. An optional fourth column may contain a filtered coverage signal (shown in reports but not used in the analysis).

If you only have BAM files, convert them with:

samtools depth -aa input.bam > output.bed

For a specific chromosome only:

samtools depth -aa -r chr1 input.bam > chr1.bed

For CRAM files, convert to BAM first:

samtools view -@ 4 -T reference.fa -b -o out.bam in.cram

This pipeline requires the following executables:

• sequana_coverage — from the Sequana package (installed automatically)
• multiqc — aggregated HTML report across samples

Install all dependencies at once:

mamba env create -f environment.yml

This pipeline runs sequana_coverage in parallel across all input BED files. For each sample it produces a standalone HTML report with:

• coverage plots and running-median normalisation
• ROI (region of interest) detection using z-score thresholds
• CNV clustering
• GC content overlay (when a reference FASTA is provided)
• Event annotation (when a GenBank file is provided)

On success, a summary.html is generated listing all samples with direct links to their individual reports, plus a MultiQC report aggregating key statistics across samples.

For very large genomes the --binning and --chunksize options can be used to reduce memory usage.

Here is the latest documented configuration file to be used with the pipeline. Each rule used in the pipeline may have a section in the configuration file.

To contribute to this project, please take a look at the Contributing Guidelines first. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.