multi-bam-cov

name	multi-bam-cov
description	Use when you need to count read coverage from multiple BAM files across specific genomic regions defined in a BED, GFF, or VCF file.
disable-model-invocation	true
user-invocable	true

multi-bam-cov

Quick Start

• Command: multiBamCov -bams sample1.bam sample2.bam ... -bed regions.bed [options]
• Local executable: /home/vimalinx/miniforge3/envs/bio/bin/multiBamCov
• Full reference: See references/help.md

When To Use This Tool

• Count alignments from multiple BAMs over the same target interval set in one pass.
• Build per-region sample-by-sample count matrices from BED / GFF / VCF targets.
• Apply mapping-quality, pairing, duplication, and strand filters consistently across many BAMs.
• Compare sample support over defined loci without computing a genome-wide depth profile.

Common Patterns

# 1) Basic multi-sample locus counts
multiBamCov \
  -bams tumor.bam normal.bam \
  -bed targets.bed

# 2) Count only proper pairs with MAPQ >= 20
multiBamCov \
  -bams sample1.bam sample2.bam sample3.bam \
  -bed exons.bed \
  -q 20 \
  -p

# 3) Count split alignments on the same strand
multiBamCov \
  -bams rna1.bam rna2.bam \
  -bed exons.bed \
  -split \
  -s

Recommended Workflow

1. Prepare the region file that defines the reporting frame, because output is one row per input interval.
2. Decide whether the counts should exclude duplicates and failed-QC reads (default) or include them (-D, -F).
3. Set -q, -p, -s / -S, and -split deliberately so all BAMs are counted under the same policy.
4. Import the appended per-BAM count columns into downstream statistical or visualization tooling.

Guardrails

• -bams and -bed are both required.
• This tool reports counts per interval per BAM, not per-base depth tracks like genomeCoverageBed.
• -D and -F widen the reads included in counting; the default excludes duplicates and failed-QC reads.
• -q defaults to 0, so low-quality alignments are included unless you raise the threshold.
• Prefer indexed, queryable BAMs for practical performance on large region sets.