consensus-peakset

Name: consensus-peakset
Author: GPTomics

1.1kstars185forks

How to install consensus-peakset?

gptomics/bioskills/consensus-peakset

$npx -y skills add GPTomics/bioSkills --skill consensus-peakset

Installs into the current project.

Summary

Build a differential-ready consensus peakset from per-replicate ATAC-seq peaks using iterative overlap removal, fixed-width re-centering, and majority-rule overlap. Use when generating a stable peak coordinate system for downstream differential accessibility, ML feature engineeri

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: bedtools 2.31+, samtools 1.19+, BEDOPS 2.4.41+, GenomicRanges 1.54+, DiffBind 3.12+, Subread 2.0.2+ (featureCounts; `--countReadPairs` requires >= 2.0.2), pybedtools 0.10+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7	- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
8	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
9
10	If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
11
12	# Consensus Peakset Construction
13
14	"Build a single peakset to count reads against for all my samples" -> Combine per-replicate or per-condition peak calls into a non-redundant, fixed-width set of regions. The strategy chosen drives FDR calibration, peak-width fairness, and reproducibility downstream.
15
16	- CLI: `bedtools merge` (simple union) and `bedtools multiinter` (per-sample membership columns emitted by default)
17	- CLI: Corces 2018 iterative overlap removal (custom shell)
18	- R: `DiffBind::dba.count(summits=250)` (built-in fixed-width)
19	- Python: `pybedtools` for programmatic merging
20
21	The peakset choice is rarely default-correct. Wrong width or wrong overlap rule propagates to every downstream analysis (differential, motif, footprint, ML).
22
23	## Why a Consensus Peakset Matters
24
25	ATAC peaks vary in width across replicates: same regulatory element might be called 200 bp in rep1 and 800 bp in rep2 because of stochastic Tn5 cuts at edges. Counting reads in different-width intervals confounds peak width with biological signal. A fixed-width consensus avoids this.
26
27	For ENCODE-style differential analysis: ALL samples must be counted against the SAME peak coordinates; otherwise the count matrix is non-rectangular and statistical models are misspecified.
28
29	## Strategy Taxonomy
30
31	\| Strategy \| Implementation \| Width \| When to use \| Fails when \|
32	\|----------\|---------------\|-------\|-------------\|------------\|
33	\| Naive union \| `bedtools merge` of all peaks \| Variable, tends wide \| Quick exploratory; never for differential \| Width inflation drives spurious differential \|
34	\| Naive intersection \| `bedtools multiinter` requiring all samples \| Variable \| High-stringency reproducibility \| Loses real condition-specific peaks \|
35	\| Majority-rule overlap \| `multiinter` requiring >= n/2 samples \| Variable \| Balance; DiffBind default with `minOverlap` \| Width still varies; counts are width-biased \|
36	\| Iterative overlap removal (Corces 2018) \| Sort by significance, greedily keep non-overlapping at fixed width \| 501 bp fixed \| ML features; cross-study comparison; modern ATAC standard \| Loses sub-501bp resolution; overweights high-significance peaks \|
37	\| Summit-centered fixed width (DiffBind) \| `dba.count(summits=250)` re-centers all peaks on summit +/- 250 bp \| 501 bp fixed \| Matches the Corces 501 bp convention (note: `dba.count` default is `summits=200` -> 401 bp); integrates with replicate counts \| Requires summit info (MACS narrowPeak); broad peaks lose width info \|
38	\| IDR-filtered union \| Union of IDR-passed peaks across rep pairs \| Variable \| ENCODE pipeline-compliant; reproducibility-aware \| Requires running IDR per pair; computationally heavier \|
39	\| Per-condition union, then global union \| Each group consensus separately, then merge \| Variable \| Different cell types / strong condition shift \| Same width issues as naive union \|
40	\| Width-controlled extension \| Extend each peak to median width centered on midpoint \| User-set \| Quick fixed-width without summit info \| Midpoint != summit; can shift biology \|
41
42	Methodology evolves; verify against current ENCODE 4 ATAC standards (encodeproject.org/atac-seq) and the Corces 2018 iterative overlap algorithm before locking pipelines.
43
44	## Iterative Overlap Removal (Corces 2018)
45
46	This is the modern standard for fixed-width consensus peaksets used in cross-study comparison and machine-learning feature matrices.
47
48	Algorithm:
49	1. Pool all peaks from all samples; re-center each on its summit; extend +/- 250 bp -> 501 bp fixed-width peaks.
50	2. Sort by significance (narrowPeak column 7, signalValue, descending).
51	3. Walk down the sorted list. Keep each peak if it does not overlap any previously kept peak. Drop if overlap.
52	4. Output the kept peaks as the consensus.
53
54	Goal: Produce a non-overlapping, fixed-width peakset weighted toward strongest evidence.

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consensus-peakset

1.1kstars185forks

View on GitHub

How to install consensus-peakset?

gptomics/bioskills/consensus-peakset

$npx -y skills add GPTomics/bioSkills --skill consensus-peakset

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: bedtools 2.31+, samtools 1.19+, BEDOPS 2.4.41+, GenomicRanges 1.54+, DiffBind 3.12+, Subread 2.0.2+ (featureCounts; `--countReadPairs` requires >= 2.0.2), pybedtools 0.10+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7	- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
8	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
9
10	If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
11
12	# Consensus Peakset Construction
13
14	"Build a single peakset to count reads against for all my samples" -> Combine per-replicate or per-condition peak calls into a non-redundant, fixed-width set of regions. The strategy chosen drives FDR calibration, peak-width fairness, and reproducibility downstream.
15
16	- CLI: `bedtools merge` (simple union) and `bedtools multiinter` (per-sample membership columns emitted by default)
17	- CLI: Corces 2018 iterative overlap removal (custom shell)
18	- R: `DiffBind::dba.count(summits=250)` (built-in fixed-width)
19	- Python: `pybedtools` for programmatic merging
20
21	The peakset choice is rarely default-correct. Wrong width or wrong overlap rule propagates to every downstream analysis (differential, motif, footprint, ML).
22
23	## Why a Consensus Peakset Matters
24
25	ATAC peaks vary in width across replicates: same regulatory element might be called 200 bp in rep1 and 800 bp in rep2 because of stochastic Tn5 cuts at edges. Counting reads in different-width intervals confounds peak width with biological signal. A fixed-width consensus avoids this.
26
27	For ENCODE-style differential analysis: ALL samples must be counted against the SAME peak coordinates; otherwise the count matrix is non-rectangular and statistical models are misspecified.
28
29	## Strategy Taxonomy
30
31	\| Strategy \| Implementation \| Width \| When to use \| Fails when \|
32	\|----------\|---------------\|-------\|-------------\|------------\|
33	\| Naive union \| `bedtools merge` of all peaks \| Variable, tends wide \| Quick exploratory; never for differential \| Width inflation drives spurious differential \|
34	\| Naive intersection \| `bedtools multiinter` requiring all samples \| Variable \| High-stringency reproducibility \| Loses real condition-specific peaks \|
35	\| Majority-rule overlap \| `multiinter` requiring >= n/2 samples \| Variable \| Balance; DiffBind default with `minOverlap` \| Width still varies; counts are width-biased \|
36	\| Iterative overlap removal (Corces 2018) \| Sort by significance, greedily keep non-overlapping at fixed width \| 501 bp fixed \| ML features; cross-study comparison; modern ATAC standard \| Loses sub-501bp resolution; overweights high-significance peaks \|
37	\| Summit-centered fixed width (DiffBind) \| `dba.count(summits=250)` re-centers all peaks on summit +/- 250 bp \| 501 bp fixed \| Matches the Corces 501 bp convention (note: `dba.count` default is `summits=200` -> 401 bp); integrates with replicate counts \| Requires summit info (MACS narrowPeak); broad peaks lose width info \|
38	\| IDR-filtered union \| Union of IDR-passed peaks across rep pairs \| Variable \| ENCODE pipeline-compliant; reproducibility-aware \| Requires running IDR per pair; computationally heavier \|
39	\| Per-condition union, then global union \| Each group consensus separately, then merge \| Variable \| Different cell types / strong condition shift \| Same width issues as naive union \|
40	\| Width-controlled extension \| Extend each peak to median width centered on midpoint \| User-set \| Quick fixed-width without summit info \| Midpoint != summit; can shift biology \|
41
42	Methodology evolves; verify against current ENCODE 4 ATAC standards (encodeproject.org/atac-seq) and the Corces 2018 iterative overlap algorithm before locking pipelines.
43
44	## Iterative Overlap Removal (Corces 2018)
45
46	This is the modern standard for fixed-width consensus peaksets used in cross-study comparison and machine-learning feature matrices.
47
48	Algorithm:
49	1. Pool all peaks from all samples; re-center each on its summit; extend +/- 250 bp -> 501 bp fixed-width peaks.
50	2. Sort by significance (narrowPeak column 7, signalValue, descending).
51	3. Walk down the sorted list. Keep each peak if it does not overlap any previously kept peak. Drop if overlap.
52	4. Output the kept peaks as the consensus.
53
54	Goal: Produce a non-overlapping, fixed-width peakset weighted toward strongest evidence.

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