Bio Genome Intervals Overlap Significance

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

Decision Tree by Scenario

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 <- localZScore(A = peaks, B = enhancers, pt = pt, window = 10000, step = 500)   # sharp vs diffuse positional association

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

Common Errors

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