covsnap

Coverage inspector for targeted sequencing QC (hg38)

covsnap computes per-target and per-exon depth-of-coverage metrics from BAM/CRAM files aligned to hg38.
It produces a self-contained interactive HTML report with automated PASS/FAIL classification
— designed for clinical and research sequencing QC workflows.

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Demo

covsnap_demo-2026-04-17_10.07.00.mp4

Screenshots

Interactive HTML Report

Graphical Interface

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Key Features

	Feature	Description
GUI	Graphical interface	Run covsnap with no arguments to launch a Tkinter GUI. Works on Linux, macOS, and Windows.
Genes	Gene-aware analysis	Look up genes by symbol (BRCA1) or analyze multiple genes at once (BRCA1,TP53,ETFDH). Built-in dictionary of ~60 genes + optional full GENCODE v44 index (62,700+ genes).
Exons	Exon-level resolution	Per-exon depth metrics via --exons using MANE Select transcripts from GENCODE v44.
Exon-only	Intronic exclusion	--exon-only computes gene-level metrics from exonic regions only — ideal for targeted/exome panels where introns have no coverage by design.
Region	Region & BED modes	Accepts genomic coordinates (chr17:43044295-43125482) or a BED file. Region mode auto-discovers overlapping genes and exons.
Report	Interactive HTML report	Self-contained HTML with summary cards, exon bar charts, accordion details, glossary, and PASS/FAIL classifications.
Engine	Dual engine support	Prefers mosdepth when available; falls back to samtools depth.
Perf	Streaming architecture	O(1) memory per target using Welford's algorithm and histogram-based exact median. Parallel execution.
Smart	Auto-detection	Contig style auto-detection (chr/no-chr), gene alias resolution (HER2 -> ERBB2), fuzzy suggestions for typos.
Safety	BED guardrails	Configurable limits on target count, total bases, and file size to prevent accidental WES/WGS runs.

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Installation

From Bioconda (recommended)

conda install -c bioconda covsnap

From PyPI

pip install covsnap

Docker (BioContainers)

docker pull quay.io/biocontainers/covsnap:0.3.0--pyhdfd78af_0
docker run --rm -v $(pwd):/data quay.io/biocontainers/covsnap:0.3.0--pyhdfd78af_0 \
    covsnap /data/sample.bam BRCA1 -o /data/report.html

From source

git clone https://github.com/enes-ak/covsnap.git
cd covsnap
pip install .

Runtime requirements

Dependency	Version	Required?
Python	>= 3.9	Yes
pysam	>= 0.22	Yes
numpy	>= 1.24	Yes
samtools	any recent	Yes (engine)
mosdepth	>= 0.3	Optional (preferred engine)

At least one of samtools or mosdepth must be on your $PATH. When --engine auto (the default), covsnap prefers mosdepth and falls back to samtools.

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Quick Start

Graphical interface

covsnap

Run with no arguments to launch the GUI — select your BAM file, choose analysis mode, configure options, and run.

Gene mode

covsnap sample.bam BRCA1

Produces covsnap.report.html with coverage metrics and PASS/FAIL classification.

Multiple genes with exon breakdown

covsnap sample.bam BRCA1,TP53,ETFDH --exons

Exon-only mode (exclude introns)

For targeted/exome panels where intronic regions have no coverage by design:

covsnap sample.bam BRCA1 --exon-only           # gene metrics from exons only
covsnap sample.bam BRCA1 --exon-only --exons   # same + show exon details in report

--exon-only and --exons are independent flags:

--exons	--exon-only	Gene metrics based on	Exon details in report
		full gene (introns + exons)	no
x		full gene (introns + exons)	yes
	x	exonic regions only	no
x	x	exonic regions only	yes

Region mode

covsnap sample.bam chr17:43044295-43125482

Overlapping genes and exons are auto-discovered.

BED mode

covsnap sample.bam --bed targets.bed

CRAM files

covsnap sample.cram BRCA1 --reference hg38.fa

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HTML Report

covsnap produces a single self-contained HTML file (no external dependencies) containing:

• Summary cards — key metrics at a glance (mean depth, coverage breadth, classification)
• Exon bar chart — per-exon coverage with smooth HSL color gradient (red -> amber -> teal)
• Accordion details — expandable per-target and per-exon metrics
• Low-coverage blocks — contiguous regions below threshold (when --emit-lowcov is used)
• Classification heuristics reference — applied rules and thresholds
• Glossary — definitions of all metrics and classification terms

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Classification Heuristics

Each target is classified using ordered heuristics (first match wins):

Status	Condition
DROP_OUT	pct_zero > 5% OR any zero-coverage block >= 500 bp
UNEVEN	mean_depth > 20 AND coefficient of variation > 1.0
LOW_EXON	Any exon with pct_ge_20 < 90% or pct_zero > 5% (exon mode only)
LOW_COVERAGE	pct_ge_20 < 95%
PASS	pct_ge_20 >= 95% AND pct_zero <= 1%

All thresholds are tunable via CLI flags:

covsnap sample.bam BRCA1 \
    --pass-pct-ge-20 98.0 \
    --pass-max-pct-zero 0.5 \
    --dropout-pct-zero 3.0 \
    --uneven-cv 0.8

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BED Guardrails

When using --bed, covsnap enforces limits to prevent accidental whole-exome/whole-genome processing:

Parameter	Default	Flag
Max target intervals	2,000	--max-targets
Max total base pairs	50 Mb	--max-total-bp
Max BED file size	50 MB	--max-bed-bytes

When limits are exceeded, the behavior is controlled by --on-large-bed:

Mode	Behavior
error	Exit with code 4
warn_and_clip (default)	Keep the first N targets that fit within limits
warn_and_sample	Reservoir sample N targets (deterministic with --large-bed-seed)

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Building the Full Gene Index

The package ships with a built-in dictionary of ~60 clinically relevant genes. For access to the full GENCODE v44 catalog (62,700+ genes, 201,000+ MANE Select exons), build the tabix index:

# Download GENCODE v44 GTF (~1.5 GB)
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_44/gencode.v44.annotation.gtf.gz

# Build the index
python scripts/build_gene_index.py gencode.v44.annotation.gtf.gz

# Reinstall to include index files
pip install .

This creates hg38_genes.tsv.gz, hg38_exons.bed.gz, and hg38_gene_aliases.json.gz in src/covsnap/data/.

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Full CLI Reference

covsnap [-h] [--version] [--bed BED] [--exons] [--exon-only]
        [--reference FASTA] [--no-index]
        [--engine {auto,mosdepth,samtools}] [--threads N]
        [-o FILE] [--emit-lowcov] [--lowcov-threshold N] [--lowcov-min-len N]
        [--max-targets N] [--max-total-bp N] [--max-bed-bytes BYTES]
        [--on-large-bed {error,warn_and_clip,warn_and_sample}]
        [--large-bed-seed N] [--pct-thresholds LIST]
        [--pass-pct-ge-20 F] [--pass-max-pct-zero F]
        [--dropout-pct-zero F] [--uneven-cv F]
        [--exon-pct-ge-20 F] [--exon-max-pct-zero F]
        [-v] [--quiet]
        alignment [target]

Positional arguments

Argument	Description
alignment	Path to BAM or CRAM file
target	Gene symbol, comma-separated gene list, or genomic region. Mutually exclusive with --bed

Commonly used options

Flag	Description	Default
--bed BED	BED file of target intervals	--
--exons	Show exon-level details in the report (gene mode only)	off
--exon-only	Compute gene metrics from exonic regions only, excluding introns	off
--reference FASTA	Reference FASTA for CRAM decoding	--
--engine	Depth engine: auto, mosdepth, samtools	auto
--threads N	Parallel workers for samtools / threads for mosdepth	4
-o FILE / --output FILE	HTML report output path	covsnap.report.html
--emit-lowcov	Include low-coverage blocks in the report	off
-v / --verbose	Increase verbosity (repeatable)	--
--quiet	Suppress non-error output	off

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Exit Codes

Code	Meaning
0	Success
1	Invalid arguments or input validation failure
2	Engine error (samtools/mosdepth failure)
3	Unknown gene name (with fuzzy suggestions printed to stderr)
4	BED guardrail limits exceeded (when --on-large-bed error)
5	CRAM reference not provided (missing --reference and no REF_PATH/REF_CACHE)

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Running Tests

pip install ".[test]"
pytest

The test suite uses synthetic BAM files generated on the fly (no real sequencing data needed). Tests requiring the full GENCODE index or mosdepth are automatically skipped if unavailable.

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Project Structure

covsnap/
├── src/covsnap/
│   ├── __init__.py          # Version, build, annotation constants
│   ├── cli.py               # CLI entry point and orchestration
│   ├── annotation.py        # Gene lookup, contig detection, region parsing
│   ├── bed.py               # Streaming BED parser with guardrails
│   ├── metrics.py           # TargetAccumulator (Welford + histogram)
│   ├── engines.py           # samtools / mosdepth depth computation
│   ├── gui.py               # Tkinter graphical interface
│   ├── html_report.py       # Self-contained interactive HTML report
│   ├── report.py            # Classification heuristics
│   └── data/                # Gene/exon tabix indexes + logo (GENCODE v44)
├── tests/                   # Comprehensive test suite
├── scripts/
│   ├── build_gene_index.py  # GENCODE GTF -> tabix index builder
│   └── covsnap.desktop      # Linux desktop entry
├── recipes/conda/           # Bioconda-compatible recipe
└── pyproject.toml

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Coordinate Convention

All output coordinates use 0-based half-open intervals, consistent with BED format. User-facing region input accepts 1-based inclusive coordinates (e.g. chr17:1000-1099), which are internally converted.

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License

MIT License. See LICENSE for details.