enhancer-gene-linking

Name: enhancer-gene-linking
Author: GPTomics

1.1kstars185forks

How to install enhancer-gene-linking?

gptomics/bioskills/enhancer-gene-linking

$npx -y skills add GPTomics/bioSkills --skill enhancer-gene-linking

Installs into the current project.

Summary

Predict enhancer-gene regulatory connections from ATAC-seq using ABC, ENCODE-rE2G, HiChIP, or Cicero. Use when linking distal enhancers to target genes, choosing between contact-aware (ABC, ENCODE-rE2G), accessibility-only (Cicero), and orthogonal (HiChIP H3K27ac, EpiMap) approac

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: ABC-Enhancer-Gene-Prediction 0.2.2+ (Engreitz lab), ENCODE-rE2G v1.0+ (EngreitzLab), Cicero 1.20+, GenomicInteractions 1.36+, FitHiChIP 9.1+, HiC-Pro 3.1+, FAN-C 0.9+, MACS3 3.0+, samtools 1.19+, bedtools 2.31+.
4
5	Verify before use:
6	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
7	- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
8	- Python: `pip show <package>` then `help(module.function)` to check signatures
9
10	If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
11
12	# Enhancer-Gene Linking
13
14	"Which gene does this distal accessible region regulate?" -> Predict the enhancer's target gene using a model that combines accessibility activity, 3D contact frequency, and (optionally) sequence-based chromatin predictions. Output is a per-(enhancer, gene) score that can be thresholded for high-confidence calls.
15
16	- CLI: ABC pipeline (`run.neighborhoods.py`, `predict.py` from Engreitz lab)
17	- CLI: ENCODE-rE2G (Snakemake-based; ENCODE 4 enhancer-gene standard)
18	- R: Cicero (ATAC-only; covered in atac-seq/co-accessibility)
19	- CLI: FitHiChIP / hichipper for HiChIP H3K27ac loops
20	- Database: EpiMap (Boix 2021), GeneHancer, FANTOM5 (pre-computed reference)
21
22	ABC and ENCODE-rE2G are the canonical predictors when Hi-C/Micro-C data is available. Cicero is the ATAC-only fallback. CRISPRi-FlowFISH (Fulco 2019) is the gold-standard experimental validation.
23
24	## Algorithmic Taxonomy
25
26	\| Method \| Inputs \| Mathematics \| Strength \| Fails when \|
27	\|--------\|--------\|-------------\|----------\|------------\|
28	\| ABC (Fulco 2019, Nasser 2021) \| ATAC + H3K27ac + Hi-C/Micro-C \| ABC = (Activity_E x Contact_E,G) / sum_e(Activity_e x Contact_e,G); threshold typically >= 0.02 \| Mechanistically grounded; published gold-standard for human cell lines \| Requires matched Hi-C / Micro-C; cell-type-specific; default contact uses average across 10 ENCODE cell types if Hi-C not available \|
29	\| ENCODE-rE2G (Gschwind 2023) \| ATAC + H3K27ac + (Hi-C optional) \| Logistic regression trained on CRISPRi-FlowFISH ground truth; uses ABC features + sequence features + distance \| ENCODE 4 standard; pre-trained models for many cell types \| Pre-trained models only available for ENCODE cell types; retraining requires CRISPRi data \|
30	\| Cicero (Pliner 2018) \| scATAC peak-cell matrix \| Graphical lasso on metacell co-accessibility \| ATAC-only; works without Hi-C \| Less concordant with Hi-C than ABC; cis-distance-limited; alpha-sensitive \|
31	\| HiChIP H3K27ac + FitHiChIP \| H3K27ac HiChIP \| Statistically significant loops at FDR < 0.05 \| Direct experimental loop measurement; cell-type-specific; orthogonal to ATAC \| Requires HiChIP wet-lab; only captures loops within HiChIP resolution (~10 kb) \|
32	\| Hi-C + HiCCUPS \| Bulk Hi-C \| Fold-enrichment loop calling \| Most-validated 3D contact method \| Resolution typically 5-25 kb; misses sub-loop fine structure \|
33	\| Capture Hi-C / PCHi-C (CHiCAGO) \| Promoter Capture Hi-C \| Asymptotic CHiCAGO score \| High-resolution promoter-anchored \| Wet-lab cost; promoter capture only \|
34	\| EpiMap (Boix 2021) reference \| None (pre-computed lookup) \| Bulk-derived enhancer-gene predictions in 833 epigenomes \| Fast, comprehensive \| Cell-type-agnostic for tissues outside the reference set \|
35	\| GeneHancer / FANTOM5 (legacy) \| None (pre-computed lookup) \| Pre-computed; varied methods per database \| Comprehensive lookup; widely cited \| Older; less reliable than ABC for cell-type-specific \|
36
37	Methodology evolves; verify against current Engreitz lab releases (ABC), ENCODE 4 publications (ENCODE-rE2G), and Mumbach 2017 (HiChIP) before locking pipelines.
38
39	## ABC Mathematics
40
41	For each candidate (enhancer E, gene G) pair within the cis window (default 5 Mb):
42
43	```
44	ABC(E -> G) = Activity_E * Contact_E,G / sum_{all e in window}(Activity_e * Contact_e,G)
45	```
46
47	- Activity_E = ATAC reads at E * H3K27ac reads at E (geometric mean of normalized signals; reflects "enhancer strength")
48	- Contact_E,G = Hi-C/Micro-C contact frequency from E to G's TSS (after distance-correction)
49	- Window = +/- 5 Mb cis (default; ENCODE-rE2G uses 1 Mb)
50
51	Threshold typical: ABC >= 0.02 for high-confidence; >= 0.01 for exploratory.
52
53	When Hi-C is unavailable, ABC uses an "average contact" averaged across 10 ENCODE Hi-C cell types as proxy (Nasser 2021); it performs comparably to cell-type-matched Hi-C. The alternat

People who liked this also viewed

6 picks

Type

enhancer-gene-linking

1.1kstars185forks

View on GitHub

How to install enhancer-gene-linking?

gptomics/bioskills/enhancer-gene-linking

$npx -y skills add GPTomics/bioSkills --skill enhancer-gene-linking

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: ABC-Enhancer-Gene-Prediction 0.2.2+ (Engreitz lab), ENCODE-rE2G v1.0+ (EngreitzLab), Cicero 1.20+, GenomicInteractions 1.36+, FitHiChIP 9.1+, HiC-Pro 3.1+, FAN-C 0.9+, MACS3 3.0+, samtools 1.19+, bedtools 2.31+.
4
5	Verify before use:
6	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
7	- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
8	- Python: `pip show <package>` then `help(module.function)` to check signatures
9
10	If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
11
12	# Enhancer-Gene Linking
13
14	"Which gene does this distal accessible region regulate?" -> Predict the enhancer's target gene using a model that combines accessibility activity, 3D contact frequency, and (optionally) sequence-based chromatin predictions. Output is a per-(enhancer, gene) score that can be thresholded for high-confidence calls.
15
16	- CLI: ABC pipeline (`run.neighborhoods.py`, `predict.py` from Engreitz lab)
17	- CLI: ENCODE-rE2G (Snakemake-based; ENCODE 4 enhancer-gene standard)
18	- R: Cicero (ATAC-only; covered in atac-seq/co-accessibility)
19	- CLI: FitHiChIP / hichipper for HiChIP H3K27ac loops
20	- Database: EpiMap (Boix 2021), GeneHancer, FANTOM5 (pre-computed reference)
21
22	ABC and ENCODE-rE2G are the canonical predictors when Hi-C/Micro-C data is available. Cicero is the ATAC-only fallback. CRISPRi-FlowFISH (Fulco 2019) is the gold-standard experimental validation.
23
24	## Algorithmic Taxonomy
25
26	\| Method \| Inputs \| Mathematics \| Strength \| Fails when \|
27	\|--------\|--------\|-------------\|----------\|------------\|
28	\| ABC (Fulco 2019, Nasser 2021) \| ATAC + H3K27ac + Hi-C/Micro-C \| ABC = (Activity_E x Contact_E,G) / sum_e(Activity_e x Contact_e,G); threshold typically >= 0.02 \| Mechanistically grounded; published gold-standard for human cell lines \| Requires matched Hi-C / Micro-C; cell-type-specific; default contact uses average across 10 ENCODE cell types if Hi-C not available \|
29	\| ENCODE-rE2G (Gschwind 2023) \| ATAC + H3K27ac + (Hi-C optional) \| Logistic regression trained on CRISPRi-FlowFISH ground truth; uses ABC features + sequence features + distance \| ENCODE 4 standard; pre-trained models for many cell types \| Pre-trained models only available for ENCODE cell types; retraining requires CRISPRi data \|
30	\| Cicero (Pliner 2018) \| scATAC peak-cell matrix \| Graphical lasso on metacell co-accessibility \| ATAC-only; works without Hi-C \| Less concordant with Hi-C than ABC; cis-distance-limited; alpha-sensitive \|
31	\| HiChIP H3K27ac + FitHiChIP \| H3K27ac HiChIP \| Statistically significant loops at FDR < 0.05 \| Direct experimental loop measurement; cell-type-specific; orthogonal to ATAC \| Requires HiChIP wet-lab; only captures loops within HiChIP resolution (~10 kb) \|
32	\| Hi-C + HiCCUPS \| Bulk Hi-C \| Fold-enrichment loop calling \| Most-validated 3D contact method \| Resolution typically 5-25 kb; misses sub-loop fine structure \|
33	\| Capture Hi-C / PCHi-C (CHiCAGO) \| Promoter Capture Hi-C \| Asymptotic CHiCAGO score \| High-resolution promoter-anchored \| Wet-lab cost; promoter capture only \|
34	\| EpiMap (Boix 2021) reference \| None (pre-computed lookup) \| Bulk-derived enhancer-gene predictions in 833 epigenomes \| Fast, comprehensive \| Cell-type-agnostic for tissues outside the reference set \|
35	\| GeneHancer / FANTOM5 (legacy) \| None (pre-computed lookup) \| Pre-computed; varied methods per database \| Comprehensive lookup; widely cited \| Older; less reliable than ABC for cell-type-specific \|
36
37	Methodology evolves; verify against current Engreitz lab releases (ABC), ENCODE 4 publications (ENCODE-rE2G), and Mumbach 2017 (HiChIP) before locking pipelines.
38
39	## ABC Mathematics
40
41	For each candidate (enhancer E, gene G) pair within the cis window (default 5 Mb):
42
43	```
44	ABC(E -> G) = Activity_E * Contact_E,G / sum_{all e in window}(Activity_e * Contact_e,G)
45	```
46
47	- Activity_E = ATAC reads at E * H3K27ac reads at E (geometric mean of normalized signals; reflects "enhancer strength")
48	- Contact_E,G = Hi-C/Micro-C contact frequency from E to G's TSS (after distance-correction)
49	- Window = +/- 5 Mb cis (default; ENCODE-rE2G uses 1 Mb)
50
51	Threshold typical: ABC >= 0.02 for high-confidence; >= 0.01 for exploratory.
52
53	When Hi-C is unavailable, ABC uses an "average contact" averaged across 10 ENCODE Hi-C cell types as proxy (Nasser 2021); it performs comparably to cell-type-matched Hi-C. The alternat

People who liked this also viewed

6 picks

Type