genome-intervals-coverage-analysis

name	genome-intervals-coverage-analysis
description	Computes and interprets sequencing read depth and coverage over a genome, windows, or target regions with mosdepth (windowed depth, cumulative distribution, --quantize callable BEDs), bedtools genomecov/coverage (bedGraph tracks, per-target stats), samtools depth/coverage (per-base depth, per-contig depth+breadth). Covers the breadth-vs-mean distinction, the cumulative-coverage curve, evenness (CV/Fano/fold-80/Gini), what each tool silently counts (duplicates, secondary/supplementary, MAPQ, read span vs fragment, mate-overlap), the samtools-depth 8000-cap version trap, and the bedtools coverage -a/-b orientation flip. Use when assessing sequencing adequacy, building coverage tracks, computing breadth at a depth threshold, defining callable regions, or QCing target-capture uniformity.
tool_type	mixed
primary_tool	bedtools

Version Compatibility

Reference examples tested with: bedtools 2.31+, mosdepth 0.3+, samtools 1.19+, pybedtools 0.10+, numpy 1.26+.

Before using code patterns, verify installed versions match. If versions differ:

• CLI: <tool> --version then <tool> --help to confirm flags
• Python: pip show <package> then help(module.function) to check signatures

samtools depth behaviour changed across versions: pre-1.13 capped depth at 8000 and truncated silently (-d 0 = unlimited); 1.13+ rewrote the subcommand with NO cap and -d/-m deprecated/ignored. Always check samtools --version before trusting a max-depth number. If code throws an error, introspect the installed tool and adapt rather than retrying.

Coverage Analysis

"Is my sequencing deep enough to answer the question?" -> Measure depth as a distribution over positions, then report median, breadth at a depth threshold, and an evenness number -- never the mean alone.

• CLI: mosdepth --by 500 prefix in.bam (windowed depth + cumulative dist), samtools coverage in.bam (per-contig depth+breadth), bedtools genomecov -ibam in.bam -bga (bedGraph track)
• Python: pybedtools.BedTool('in.bam').genome_coverage(bga=True) (pybedtools); parse prefix.mosdepth.global.dist.txt for the breadth curve

The Single Most Important Modern Insight -- Mean Depth Is a Budget, Not a Result; Report Breadth Off a Cumulative Curve

"30x WGS" describes what was paid for, not what was achieved. Coverage is a distribution over positions, and the mean is its worst summary: it is dragged up by a fat right tail (repeats, rDNA, mitochondria, PCR pileups, segmental dups) while staying blind to a hard left wall of zeros and near-zeros (GC-extreme exons, poorly-mappable regions, capture dropout). Two libraries with identical mean 30x can differ completely -- one even and callable everywhere, one spiky with 20% of the target uncallable. The mean hides both failures. Four load-bearing moves:

1. Report MEDIAN, not mean. The median is robust to the right tail. When mean/median exceeds ~1.1-1.2 the distribution is skewed and the mean is overstating typical depth -- that gap is a free evenness diagnostic.
2. Report a BREADTH / cumulative-coverage curve. "% of target >= 1x, >= 10x, >= 20x, >= 30x" is the honest summary, because adequacy is a breadth statement: a base that was not covered deeply enough is uncallable no matter how deep the rest of the genome is. mosdepth's *.mosdepth.global.dist.txt IS this curve. The killer question for any "mean = 30x" claim is "breadth at 20x?".
3. Quantify EVENNESS (CV, Fano factor, Picard fold-80, or Gini) -- an even 30x and a spiky 30x are different experiments, and a spiky library cannot be rescued by sequencing deeper (extra reads follow the same biased distribution; the holes stay holes). Fix the library (PCR-free, better capture, UMIs), not the lane count.
4. Say WHAT WAS COUNTED. A depth number is meaningless until the recipe is stated: duplicates dropped (only if MARKED first)? secondary/supplementary included? MAPQ filter? read span or fragment? mate-overlap corrected? per-base or per-region? The tools disagree on every one of these by default.

Tool Taxonomy

Tool	Counts what (defaults)	Per-base or region	When
mosdepth	corrects mate-overlap by default (off under --fast-mode/-x); -Q MAPQ filter; emits cumulative dist + summary	windowed (--by), per-region, or callable bins (--quantize)	the modern fast default for WGS/WES/targeted; gives the breadth curve directly
samtools coverage	per-reference summary (added 1.10); coverage column = breadth %, meandepth = depth	per-contig	quick "is this contig actually covered?" -- spots high-mean/low-breadth pileups
samtools depth	drops UNMAP/SECONDARY/QCFAIL/DUP by default; -Q/-q filters; -s de-double-counts overlap; CRAM needs --reference	per-base	exact per-base depth over small regions; watch the 8000-cap version trap
bedtools genomecov	counts READ coverage by default (double-counts mate overlap); -pc = fragment; -split for spliced	per-base / bedGraph / histogram	bedGraph tracks, genome-wide depth histogram
bedtools coverage	per-A-interval stats from B reads; -a/-b flipped at v2.24.0	per-region (or -d per-base)	per-target counts/breadth/mean over a BED
Picard CollectHsMetrics	capture-kit QC	per-target panel	exome/panel uniformity: on-target %, fold-80, PCT_TARGET_BASES_20X

Decision Tree by Scenario

Scenario	Recommended	Why
WGS / WES breadth + adequacy	mosdepth --by then parse *.global.dist.txt	emits the cumulative curve + median directly; fast
Quick per-contig depth & breadth glance	samtools coverage	one line/contig; coverage col = breadth, meandepth = depth
Exact per-base depth, small region	samtools depth -a -r chr:from-to	per-base; add -s for short-insert; check version for 8000 cap
bedGraph coverage TRACK for a browser	bedtools genomecov -ibam -bga (or -bg)	-bga marks zero-coverage gaps; convert to bigWig -> bigwig-tracks
Per-target counts/breadth/mean over a BED	bedtools coverage -a targets.bed -b in.bam	A = targets, B = reads (post-v2.24.0); -mean for mean depth
Callable-region BED (NO/LOW/CALLABLE/HIGH)	mosdepth --quantize 0:1:4:150:	lightweight CallableLoci replacement at scale
Target-capture uniformity QC	-> Picard CollectHsMetrics (fold-80, on-target %)	the capture-QC standard; off-target loss + bait unevenness
Spliced/RNA-seq depth	add -split (genomecov/coverage)	without it an intron (N CIGAR) is counted as covered
Short-insert VAF (amplicon/cfDNA)	correct mate-overlap: samtools depth -s / genomecov -pc / mosdepth default	naive per-base double-counts the overlap, corrupting VAFs
Normalized cross-sample track	-> chip-seq/chipseq-visualization (deepTools bamCoverage)	library-size correction (RPGC/CPM/BPM) for comparison
Pileup/variant evidence from BAM	-> alignment-files/pileup-generation	depth is upstream of per-call DP/AD

mosdepth -- The Modern Default

Goal: Get the median depth and the full breadth curve for a BAM in one fast pass.

Approach: Run mosdepth windowed (or whole-genome), then read the cumulative distribution file -- it already holds breadth at every depth threshold; no histogram integration needed.

mosdepth --by 500 -Q 20 sample in.bam     # --by 500 = 500 bp windows; -Q 20 = drop MAPQ<20 (repeat coverage collapses, intentionally)
# Outputs: sample.mosdepth.summary.txt (mean/min/max per chrom + total)
#          sample.mosdepth.global.dist.txt (cumulative: chrom, depth, proportion >= depth)
#          sample.regions.bed.gz (per-window mean depth)

The *.global.dist.txt rows are chrom depth proportion_of_bases_at_least_this_depth -- the breadth curve directly. Read median as the depth where proportion crosses 0.5. --fast-mode/-x is ~2x faster but SILENTLY disables mate-overlap correction -- fine for a rough WGS glance, wrong for VAF-sensitive short-insert data.

Goal: Emit a callable-region BED (NO_COVERAGE / LOW / CALLABLE / HIGH) without GATK3.

Approach: Use --quantize to bin depth and merge adjacent equal-bin runs into a compact BED.

mosdepth --quantize 0:1:4:150: callable in.bam   # bins: [0,1)=NO_COVERAGE, [1,4)=LOW, [4,150)=CALLABLE, [150,inf)=HIGH
# 4 = min callable depth (tune to caller); 150 = excessive-depth ceiling (flags rDNA/artifact pileups)
zcat callable.quantized.bed.gz | head

bedtools genomecov -- Tracks and the Histogram Default

bedtools genomecov -ibam in.bam -bga > cov.bedGraph   # -bga = bedGraph INCLUDING zero-coverage runs; -bg omits zeros
bedtools genomecov -ibam in.bam -pc -bg > frag.bedGraph # -pc = FRAGMENT coverage (mate overlap counted once); default counts reads (double-counts overlap)
bedtools genomecov -ibam in.bam -split -bg > rna.bedGraph # -split = skip N-CIGAR gaps (introns); MANDATORY for spliced RNA-seq
bedtools genomecov -ibam in.bam > hist.txt            # NO output flag = a 5-col HISTOGRAM, not a track

The bare default is a histogram, not a bedGraph -- 5 columns: chrom depth bases_at_that_depth chrom_size fraction_of_chrom, with a final genome block for the whole genome. Breadth/mean must be integrated from it yourself (sum fraction over depth >= threshold) -- which is exactly why mosdepth's ready-made dist file is preferred.

bedtools coverage -- Per-Target Stats (mind the orientation)

bedtools coverage -a targets.bed -b in.bam > per_target.bed   # stats reported FOR each A interval
bedtools coverage -a targets.bed -b in.bam -mean > mean.bed   # -mean = mean depth per A interval

As of bedtools v2.24.0 coverage is computed for the -a file (it was -b before) -- A = the regions stats are wanted for (targets), B = the reads. The default appends 4 columns to each A interval: (1) count of B features overlapping, (2) bases in A covered >=1x, (3) length of A, (4) fraction of A covered (col2/col3 = per-interval breadth). -d = per-base depth within each interval; -hist = depth histogram per interval plus an all summary; -counts = just the overlap count (faster).

samtools depth / coverage

samtools coverage in.bam                          # per-contig: rname..numreads covbases coverage(=breadth%) meandepth meanbaseq meanmapq
samtools depth -a -Q 20 -r chr1:1-100000 in.bam   # -a = report zero-depth positions; -Q = min MAPQ; -r = region
samtools depth -s in.bam                          # -s = count overlapping mate pair only ONCE (short-insert de-double-count)
samtools depth -a --reference ref.fa in.cram      # CRAM REQUIRES --reference

In samtools coverage the column literally named coverage is breadth (% bases >=1x), and meandepth is depth -- a contig with coverage=9.7 and meandepth=3.5 is 3.5x over only 9.7% of the contig (a localized pileup), NOT "9.7x coverage". samtools depth drops UNMAP/SECONDARY/QCFAIL/DUP by default (so duplicates are excluded -- but only if they were MARKED first). Without -a/-aa, zero-depth positions are omitted, so a naive sum/lines mean over-counts by dropping the zeros.

Per-Method Failure Modes

Reporting mean depth as the result

Trigger: citing "mean = 30x" as adequacy. Mechanism: mean is inflated by the repeat/rDNA tail and blind to GC/mappability holes. Symptom: a genome with large uncallable gaps looks fine. Fix: report median + breadth at the caller's threshold (mosdepth dist).

samtools depth silent 8000 cap

Trigger: pre-1.13 samtools on high-depth loci (rDNA, mito, amplicon, ctDNA). Mechanism: old default -d/-m capped depth at 8000 and truncated with no warning. Symptom: depth plateaus near 8000. Fix: samtools --version; on old builds add -d 0; 1.13+ has no cap (flag ignored). Note mpileup has its own separate 8000 default.

Mate-overlap double-counting

Trigger: naive per-base depth on short-insert libraries (amplicon, cfDNA, FFPE). Mechanism: the two mates of a short fragment both cover the overlap, counted twice but not independent. Symptom: locally doubled depth, corrupted/inflated VAFs. Fix: samtools depth -s, genomecov -pc (fragment), or mosdepth default -- and do NOT use mosdepth --fast-mode/-x, which turns the correction off.

Coverage off an un-deduped BAM

Trigger: depth on a BAM whose duplicates were never marked. Mechanism: dedup-aware tools drop the DUP flag, but nothing was flagged. Symptom: inflated depth at amplified (often GC-extreme) loci, fatter right tail. Fix: Picard MarkDuplicates / samtools markdup FIRST, then measure.

MAPQ filtering and repeat coverage

Trigger: choosing a MAPQ threshold without considering repeats. Mechanism: repeats give low MAPQ; -Q 20+ makes repeat coverage vanish, MAPQ 0 lets multimappers pile up or smear. Symptom: repeats read as either empty or noisy -- no neutral choice. Fix: mask repeats (ENCODE blacklist / mappability) and report breadth over the MAPPABLE genome, not the whole genome.

bedtools coverage -a/-b backwards

Trigger: pre-v2.24.0 muscle memory / old tutorials. Mechanism: semantics flipped to report stats for -a at v2.24.0. Symptom: well-formed output describing per-read instead of per-target stats. Fix: A = targets, B = reads; sanity-check the row count equals the target count.

genomecov default misread / no -split on RNA-seq

Trigger: expecting a bedGraph from bare genomecov, or omitting -split on spliced reads. Mechanism: bare default is a histogram; without -split an N-CIGAR intron is counted as covered. Symptom: misparsed histogram, or every spliced gene appears fully covered across introns. Fix: add -bg/-bga for a track; always -split for spliced data.

Quantitative Thresholds

Threshold	Source	Rationale
WGS germline ~30x mean -> ~95% of genome >= 20x	field convention (approx)	het-SNP sensitivity plateaus ~30x; frame as breadth, not mean
WES germline ~100x on-target -> ~90-95% target >= 10-20x	field convention (approx; ACMG-style, lab-dependent)	capture unevenness + off-target loss eat the raw mean
Somatic bulk tumor ~60-100x+	field convention (approx)	low-VAF subclones need depth ~ 1/VAF; impure tumors need more
ctDNA/UMI panels 1000s-50000x raw	field convention (approx)	raw depth != usable depth after UMI collapse; report effective depth
Long-read WGS ~20-30x (HiFi ~30x, ONT SV ~20x+)	moving convention (approx)	flatter GC bias + better repeat mappability reach more genome per x
mean/median > ~1.1-1.2 = skewed	distribution diagnostic	the tail is inflating the mean; investigate dups/repeats/rDNA
Fano factor = 1 (Poisson ideal); real >> 1	Lander & Waterman 1988	overdispersion = evenness problem; deeper sequencing won't fill holes
Picard fold-80 ~1.3-2 good, >3 poor	practitioner heuristic (Picard defines only the metric)	fold extra sequencing to lift 80% of targets to the mean

Common Errors

Error / symptom	Cause	Solution
Depth plateaus at ~8000	pre-1.13 samtools default cap	samtools --version; add -d 0; upgrade to 1.13+
Inflated VAFs in amplicon/cfDNA	mate-overlap double-counting	samtools depth -s / genomecov -pc / mosdepth (not --fast-mode)
"30x" but variants missing in some genes	GC-shallow / uncallable holes hidden by mean	report breadth at threshold; mask blacklist; check fold-80
samtools coverage "coverage" looks tiny	it is breadth %, not depth	read meandepth for depth; coverage = % bases >=1x
genomecov gives a histogram not a track	no -bg/-bga flag	add -bga (with zeros) or -bg
Every spliced gene fully covered	missing -split on RNA-seq	add -split to genomecov/coverage
bedtools coverage stats look per-read	-a/-b backwards (pre-2.24 habit)	A = targets, B = reads
CRAM depth errors / empty	missing reference	samtools depth --reference ref.fa

References

• Quinlan AR, Hall IM. 2010. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26:841-842.
• Pedersen BS, Quinlan AR. 2018. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics 34:867-868.
• Danecek P, Bonfield JK, Liddle J, et al. 2021. Twelve years of SAMtools and BCFtools. GigaScience 10:giab008.
• Aird D, Ross MG, Chen WS, et al. 2011. Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biol 12:R18.
• Benjamini Y, Speed TP. 2012. Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Res 40:e72.
• Amemiya HM, Kundaje A, Boyle AP. 2019. The ENCODE blacklist: identification of problematic regions of the genome. Sci Rep 9:9354.
• Lander ES, Waterman MS. 1988. Genomic mapping by fingerprinting random clones: a mathematical analysis. Genomics 2:231-239.

Related Skills

• bedgraph-handling - bedGraph tracks this skill emits, and their normalization
• bigwig-tracks - Convert the coverage bedGraph to an indexed bigWig for browsers
• interval-arithmetic - Intersect coverage/callable BEDs with target regions
• alignment-files/pileup-generation - Per-base pileup upstream of depth and per-call DP
• alignment-files/bam-statistics - flagstat/idxstats and dup rate that explain coverage confounders
• chip-seq/chipseq-visualization - deepTools normalized coverage tracks for cross-sample comparison
• data-visualization/genome-tracks - Render the coverage tracks built here