bio-genome-intervals-overlap-significance

name	bio-genome-intervals-overlap-significance
description	Tests whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-value.
origin	openai4s
category	bioskills/genome-intervals
metadata	{"tool_type":"mixed","primary_tool":"regioneR","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: bedtools 2.31+, pybedtools 0.10+, regioneR 1.36+ (Bioconductor 3.18+), GAT 1.3+, LOLA 1.30+, rGREAT 2.4+.

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
• R: packageVersion('regioneR') then ?permTest to verify parameters

Bioconductor packages (regioneR, LOLA, rGREAT) are version-pinned to the Bioconductor release, not just the package version - record the Bioconductor release with results. rGREAT 2.x runs GREAT locally with general background handling; rGREAT 1.x only proxied the (whole-genome-default) web server. If code throws an error, introspect the installed package and adapt the example to match the actual API rather than retrying.

Overlap Significance

"My peaks overlap enhancers a lot - is that more than chance?" -> Compare the observed overlap to a null distribution from a structured-genome model, not to a uniform-random expectation, and report a permutation p-value/z-score, not a raw count.

• CLI: bedtools fisher -a A -b B -g genome.txt (fast screen); gat-run.py --segments=A --annotations=B --workspace=accessible.bed --isochores=gc.bed --num-samples=10000
• Python: BedTool(A).shuffle(g='genome.txt', excl='blacklist.bed').jaccard(B) looped for a null (pybedtools)
• R: permTest(A=peaks, B=enhancers, randomize.function=circularRandomizeRegions, evaluate.function=numOverlaps, genome='hg38', mask=blacklist, ntimes=1000) (regioneR)

The Single Most Important Modern Insight -- A Raw Overlap Count Is an Observation in Search of a Null, and the Universe Choice Dominates

"847 of 1,000 peaks overlap enhancers" means nothing until the analysis can say what that number would have been by chance - and "by chance" is almost never "place the regions uniformly at random on the genome." The genome is structured: genes cluster, GC varies in megabase isochores, mappability is uneven, half the genome is repeat/gap, and query regions are drawn from a biased universe (open chromatin, callable space, exons). Two tracks that share nothing but a gene-rich, high-GC, high-mappability habitat overlap far more than uniform-random expectation, and a naive test returns p < 1e-300. The co-localization is real; the interpretation ("functional association") is false. Three load-bearing moves:

1. The universe/background is the lever that moves the whole answer - bigger than the test choice. Across LOLA (userUniverse), GREAT (background regions), GAT (--workspace), and regioneR (the mask/resampleRegions universe), the most consequential choice is the set of regions the query could have come from. ATAC/ChIP peaks can only be called in accessible chromatin; their honest universe is "all accessible regions," not the genome. Testing against the whole genome merely rediscovers that open chromatin is gene-rich - every gene-associated annotation lights up, none of it specific. The difference between LOLA/GAT/regioneR on the same correct universe is second-order; the difference between a correct universe and a whole-genome universe on the same tool is often p1 vs p1e-200. Care belongs on the background, not on tool selection.

2. A correct null preserves three things, or it manufactures significance: (a) the regions' size distribution (a 50 kb domain hits anything; a 200 bp peak rarely does - relocate intervals of the observed sizes, do not sprinkle points); (b) an accessible workspace excluding assembly gaps, centromeres, the ENCODE blacklist (Amemiya 2019), and unmappable bins; (c) local structure - GC/isochore, gene density, and clustering (GAT --isochores; regioneR circularRandomizeRegions for autocorrelated regions). The "right" answer is usually less significant than the naive test - that deflation is the methodology working.

3. One ten-minute sanity move catches most false claims: shuffle the query within the same workspace and re-run the same overlap pipeline. If shuffled regions also overlap the annotation a lot, the "enrichment" is workspace geography, not biology.

Method Taxonomy

Tool	Citation	Null model	When
bedtools fisher	Quinlan 2010 Bioinformatics	analytic 2x2; estimates the unobserved "in-neither" cell from mean interval size + genome size; ignores genome structure	fast triage screen only - never the reported result
bedtools shuffle + jaccard/numOverlaps	Quinlan 2010 Bioinformatics	DIY size-preserving permutation; -incl/-excl make it matched	entry-level permutation in a shell/Python pipeline; full control, more code
GAT	Heger 2013 Bioinformatics	per-isochore size-preserving simulation in a workspace; GC/composition conditioned; built-in FDR across annotations	composition-aware enrichment vs many tracks with multiple-testing control
regioneR	Gel 2016 Bioinformatics	flexible permutation; randomizeRegions (mask) vs circularRandomizeRegions (preserves clustering) vs resampleRegions (real universe); any evaluator; localZScore	publication-grade, R-native, autocorrelated regions, where-in-the-region probing
LOLA	Sheffield & Bock 2016 Bioinformatics	NOT a shuffle - Fisher's exact of query vs a region database, relative to a userUniverse	ranking enrichment of a peak set against ENCODE/Roadmap region collections
GREAT / rGREAT	McLean 2010 Nat Biotechnol; Gu & Hubschmann 2023 Bioinformatics	gene regulatory-domain (basal 5 kb up/1 kb down, extend to 1 Mb); binomial-over-regions AND hypergeometric-over-genes	GO/ontology enrichment from regions (cis-regulatory), not from a gene list

Decision Tree by Scenario

Scenario	Recommended	Why
Quick "is this even worth permuting?"	bedtools fisher	one command, analytic; p~1 means stop; low means permute
Publication-grade colocalization of two region sets	regioneR permTest with a mask	flexible, reports p + z-score; circular randomization for clustered regions
Need GC/isochore/composition control + FDR over many tracks	GAT with --workspace + --isochores	per-isochore sampling removes the GC confounder; built-in qvalue
Enrich a peak set against a region database (TFBS/chromatin)	LOLA runLOLA with a correct userUniverse	universe-relative Fisher; ranked odds ratios + q-values
GO/ontology terms FROM regions (cis-regulatory)	rGREAT with a real background	regulatory-domain model; require binomial AND hypergeometric
GWAS/eQTL statistical colocalization (shared causal variant)	-> causal-genomics/colocalization-analysis	DISTINCT problem: coloc/SuSiE on summary stats, not interval overlap
Gene-list (not region) ontology enrichment	-> pathway-analysis/go-enrichment	start from genes; GREAT is the region-based analog
Query regions are autocorrelated/clustered	regioneR circularRandomizeRegions	rotating the set preserves inter-region spacing; uniform randomization inflates significance
CNV/SV concordance between call sets	bedtools intersect -f 0.5 -r (50% reciprocal)	"same event" convention; one-sided fractions let a giant call swallow a tiny one
Peaks not yet called	-> chip-seq/peak-calling, atac-seq/atac-peak-calling	this skill operates on existing interval sets

bedtools fisher - The Analytic Screen (weakest null)

bedtools fisher -a peaks.bed -b enhancers.bed -g genome.txt   # both inputs sorted; genome file required

fisher builds a 2x2 table (in-A/not x in-B/not) and runs Fisher's exact test. The trap: it cannot observe the "in-neither" cell (there is no negative class of intervals that do not exist), so it estimates the table totals from a heuristic on mean interval size and genome size, assuming intervals are independent points uniformly placeable across the genome - exactly the assumption a structured genome violates. The bedtools docs warn it is prone to inflation and advise validating any low p-value by simulation. Treat it as triage: p1 -> stop; p1e-50 -> run GAT or regioneR, because the structure-corrected p could be anywhere from 1e-30 to 0.3.

Permutation Null with bedtools shuffle (entry-level)

Goal: Decide whether two interval sets overlap more than chance, controlling for feature size and the accessible workspace.

Approach: Compute the observed overlap (jaccard or count), then shuffle one set N times within an include-list / outside a blacklist, recompute each time, and locate the observed value in the resulting null distribution.

import pybedtools

N_PERMUTATIONS = 1000   # >=1000 gives a stable empirical p down to ~0.001; fewer cannot resolve small p
a = pybedtools.BedTool('peaks.bed')
b = pybedtools.BedTool('enhancers.bed').sort()
observed = a.sort().jaccard(b)['jaccard']
null = [a.shuffle(g='genome.txt', incl='accessible.bed', excl='blacklist.bed', chrom=True).sort().jaccard(b)['jaccard'] for _ in range(N_PERMUTATIONS)]
p = (sum(x >= observed for x in null) + 1) / (N_PERMUTATIONS + 1)   # +1 avoids a p of exactly 0 (Phipson & Smyth 2010)

-incl restricts placement to the accessible workspace (the universe); -excl avoids gaps/blacklist; -chrom keeps each region on its own chromosome (preserves per-chromosome density). Without -incl/-excl this collapses to a uniform-random null - the wrong one.

GAT - Isochore/GC-Conditioned Simulation

Goal: Test enrichment against one or many annotation tracks while conditioning on GC/composition and controlling FDR.

Approach: Provide the query segments, the annotations, an accessible workspace, and an isochore segmentation; GAT samples size-matched segments per isochore and compares observed to sampled overlap, reporting fold, empirical p, and FDR-adjusted q across annotations.

gat-run.py \
  --segments=peaks.bed \
  --annotations=features.bed \
  --workspace=accessible.bed \
  --isochores=gc_bins.bed \
  --num-samples=10000 \
  --counter=nucleotide-overlap \
  --log=gat.log > gat_results.tsv

--isochores subdivides the workspace (GC bins, chromatin state, or mappability) so sampling happens per isochore, preserving GC/composition confounding rather than averaging it away - GAT's signature over a plain shuffle. Set --num-samples (>=10000 for stable small q) and --counter explicitly; do not rely on defaults.

regioneR - Flexible Permutation in R

Goal: Get a publication-grade colocalization p-value and z-score, with a null that preserves the structure the query trivially has.

Approach: Mask the genome to the workspace, choose a randomizer that concedes the right structure (uniform vs clustering-preserving vs real-universe), permute N times scoring overlaps, then probe where the association lives with localZScore.

# Reference: regioneR 1.36+ (Bioconductor 3.18+) | Verify API if version differs
library(regioneR)

N_TIMES <- 1000   # permutation count; >=1000 for a stable empirical p (Gel 2016)
peaks <- toGRanges('peaks.bed')
enhancers <- toGRanges('enhancers.bed')
gam <- getGenomeAndMask(genome = 'hg38', mask = toGRanges('blacklist.bed'))

pt <- permTest(A = peaks, B = enhancers,
               randomize.function = circularRandomizeRegions,   # preserves clustering; use randomizeRegions for non-autocorrelated query
               evaluate.function = numOverlaps,
               genome = gam$genome, mask = gam$mask,
               ntimes = N_TIMES, count.once = TRUE)
pt$numOverlaps$pval; pt$numOverlaps$zscore

lz <- localZScoreA  peaks B  enhancers pt  pt window   step     

circularRandomizeRegions rotates the whole set around the genome, preserving inter-region spacing/clustering - the honest null when the query is autocorrelated (CpG islands, TAD-restricted peaks); plain randomizeRegions breaks clustering and inflates significance. resampleRegions draws from a supplied real universe. The mask is the workspace control.

LOLA - Universe-Relative Region-Set Enrichment

# Reference: LOLA 1.30+ | Verify API if version differs
library(LOLA)
regionDB <- loadRegionDB('LOLACore/hg38')
userSets <- readBed('peaks.bed')
userUniverse <- readBed('all_called_regions.bed')   # the candidate pool the query was drawn from - NOT the whole genome
res <- runLOLA(userSets, userUniverse, regionDB, cores = 4)   # odds ratio + p + q per reference set, rankable

LOLA is not a shuffle: it tests the query against many reference region sets (ENCODE TFBS, Roadmap chromatin) with a Fisher's exact test relative to userUniverse. The universe is everything - the LOLA vignette recommends either the union of all query sets across the experiment ("regions that were in play") or the assay's full candidate pool (e.g. all tested DHS/called peaks); pick the one that honestly bounds where the query could have come from. A whole-genome universe inflates every enrichment.

rGREAT - Ontology Enrichment From Regions

# Reference: rGREAT 2.4+ | Verify API if version differs
library(rGREAT)
res <- great(toGRanges('peaks.bed'), gene_sets = 'GO:BP', tss_source = 'txdb:hg38',
             background = toGRanges('accessible.bed'))   # supply a real background, not the whole-genome default
tb <- getEnrichmentTable(res)   # has Binom + Hyper p/adjp columns

GREAT assigns each gene a regulatory domain (basal 5 kb upstream / 1 kb downstream, extended up to 1 Mb to the next gene's basal domain), maps regions to those domains, then runs a binomial-over-regions test AND a hypergeometric-over-genes test - by design, with opposite biases. Trust a term only if both fire: a single gene with a huge regulatory domain attracts regions by target size and lights up the binomial; the hypergeometric (counting that gene once) calls the bluff. Supply a real background - the binomial assumes regions are independent and uniformly placeable, which clustered ChIP/ATAC peaks violate (Fulcher 2021 demonstrates orders-of-magnitude false-positive inflation from spatial autocorrelation in genomic enrichment analysis - a transferable critique of region-to-gene-category tests).

Per-Method Failure Modes

Whole-genome universe for a biased query

Trigger: testing ATAC/ChIP peaks (or DMRs, or capture-panel regions) against the whole genome. Mechanism: the query could only have come from accessible/assayed space, which is gene-rich; the genome universe credits that geography as enrichment. Symptom: every gene-associated annotation is "significant," none specific, p absurdly small. Fix: set the universe to the callable/candidate pool (LOLA userUniverse, GAT --workspace, regioneR mask/resampleRegions).

bedtools fisher reported as the result

Trigger: putting a bedtools fisher p-value in a figure or reviewer reply. Mechanism: its analytic 2x2 estimates the unobserved cell from a uniform-placement heuristic, ignoring genome structure; prone to inflation. Symptom: spuriously tiny p that evaporates under a competent permutation null. Fix: use fisher only to triage; validate any low p with GAT/regioneR.

Uniform null on clustered query regions

Trigger: randomizeRegions / plain shuffle on autocorrelated regions (CpG islands, tandem families, TAD-restricted peaks). Mechanism: uniform placement destroys the clustering the observed data has, so permuted overlap is too low. Symptom: inflated significance vs a structure-preserving null. Fix: circularRandomizeRegions (regioneR) or per-chromosome/per-class shuffling.

No blacklist / no workspace exclusion

Trigger: shuffling across the full genome including gaps, centromeres, and the ENCODE blacklist. Mechanism: the null places regions where reads/peaks could never occur, lowering expected overlap. Symptom: enrichment that is really mappability artifact. Fix: exclude the ENCODE blacklist (Amemiya 2019) and assembly gaps via -excl/mask before any test.

Ignoring multiple testing across tracks/terms

Trigger: testing a query against many annotation tracks or many GO terms and reading raw p-values. Mechanism: dozens-to-thousands of tests inflate the family-wise false-positive rate. Symptom: a long list of "significant" hits dominated by chance. Fix: use GAT's built-in FDR, FDR-adjust LOLA ranks, and read GREAT's binomial+hypergeometric q-values; require both GREAT tests.

GREAT term significant by only one test

Trigger: reporting a GO term significant by binomial OR hypergeometric alone. Mechanism: the two tests have opposite biases (domain size vs gene count). Symptom: a term driven by one large-domain gene, or by gene-counting alone. Fix: require significance by both; distrust single-test hits.

Quantitative Thresholds

Threshold	Source	Rationale
Permutation N >= 1000	empirical p resolution (Gel 2016; Phipson & Smyth 2010)	stable empirical p down to ~0.001; the (hits+1)/(N+1) estimator avoids p=0
GAT --num-samples >= 10000	GAT practice	needed for stable small q across many annotations
50% reciprocal overlap (-f 0.5 -r) for CNV/SV concordance	field convention	"same event"; one-sided fractions let a giant call swallow a tiny one
Universe = candidate/callable pool, not the genome	LOLA/GAT/regioneR design	the dominant lever; whole-genome universe inflates every enrichment
GREAT basal 5 kb up / 1 kb down, extend to 1 Mb	McLean 2010 default	regulatory-domain model; user-configurable, report the values used
ENCODE blacklist excluded before any test	Amemiya 2019	high-signal artifact regions otherwise manufacture overlap
FDR control across tracks/terms	multiple-testing	many-track / many-term tests inflate false positives

Common Errors

Error / symptom	Cause	Solution
Every annotation "significant," p absurdly small	whole-genome universe for a biased query	set the universe to the callable/accessible pool
bedtools fisher error	inputs not sorted, or missing genome file	sort -k1,1 -k2,2n; pass -g genome.txt
Empty/zero overlap in shuffle null	chrom naming mismatch (chr1 vs 1) across query/genome/blacklist	harmonize chromosome naming across all files
permTest much more significant than expected	uniform randomizer on clustered regions	use circularRandomizeRegions
GAT reports no GC effect	no --isochores supplied	pass an isochore/GC segmentation of the workspace
GREAT term looks real but is fragile	significant by only one of the two tests	require binomial AND hypergeometric
regioneR toGRanges/getGenomeAndMask error	genome name not recognized / mask chrom mismatch	use a supported genome id or supply explicit GRanges; match chrom naming

References

• Quinlan AR, Hall IM. 2010. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26:841-842.
• Heger A, Webber C, Goodson M, Ponting CP, Lunter G. 2013. GAT: a simulation framework for testing the association of genomic intervals. Bioinformatics 29:2046-2048.
• Gel B, Diez-Villanueva A, Serra E, Buschbeck M, Peinado MA, Malinverni R. 2016. regioneR: an R/Bioconductor package for the association analysis of genomic regions based on permutation tests. Bioinformatics 32:289-291.
• Sheffield NC, Bock C. 2016. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics 32:587-589.
• McLean CY, Bristor D, Hiller M, Clarke SL, Schaar BT, Lowe CB, Wenger AM, Bejerano G. 2010. GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol 28:495-501.
• Gu Z, Hubschmann D. 2023. rGREAT: an R/Bioconductor package for functional enrichment on genomic regions. Bioinformatics 39:btac745.
• Amemiya HM, Kundaje A, Boyle AP. 2019. The ENCODE blacklist: identification of problematic regions of the genome. Sci Rep 9:9354.
• Fulcher BD, Arnatkeviciute A, Fornito A. 2021. Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data. Nat Commun 12:2669.
• Phipson B, Smyth GK. 2010. Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Stat Appl Genet Mol Biol 9:Article 39.

Related Skills

• interval-arithmetic - The intersect/shuffle/jaccard/fisher mechanics this skill turns into a test
• bed-file-basics - BED format, coordinate systems, and sorting the inputs every test requires
• proximity-operations - Nearest-feature assignment when the question is distance, not overlap enrichment
• chip-seq/peak-calling - Source of the peak query sets tested for enrichment
• chip-seq/peak-annotation - Assign enriched peaks to genes/features
• atac-seq/atac-peak-calling - Source of ATAC peak sets and the accessible-region universe
• pathway-analysis/go-enrichment - Gene-list ontology enrichment; GREAT is the region-based analog
• causal-genomics/colocalization-analysis - GWAS/eQTL statistical colocalization (shared causal variant) - a distinct problem from interval overlap
• data-visualization/genome-tracks - Render the query and annotation tracks behind an enrichment claim