| name | bio-atac-seq-enhancer-gene-linking |
| description | 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) approaches, validating predictions against CRISPRi-FlowFISH gold-standard, or building cell-type-specific regulatory maps for fine-mapping or therapeutic target discovery. |
| tool_type | mixed |
| primary_tool | ABC-Enhancer-Gene-Prediction |
Version Compatibility
Reference examples tested with: ABC-Enhancer-Gene-Prediction 0.2.2+ (Engreitz lab), ENCODE-rE2G v1.0+ (2024 release), Cicero 1.20+, GenomicInteractions 1.36+, FitHiChIP 9.1+, hicpro 3.1+, FAN-C 0.9+, MACS3 3.0+, samtools 1.19+, bedtools 2.31+.
Verify before use:
- CLI:
<tool> --version then <tool> --help to confirm flags
- R:
packageVersion('<pkg>') then ?function_name to verify parameters
- Python:
pip show <package> then help(module.function) to check signatures
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
Enhancer-Gene Linking
"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.
- CLI: ABC pipeline (
run.neighborhoods.py, predict.py from Engreitz lab)
- CLI: ENCODE-rE2G (Snakemake-based; ENCODE 4 standard 2024)
- R: Cicero (ATAC-only; covered in atac-seq/co-accessibility)
- CLI: FitHiChIP / hichipper for HiChIP H3K27ac loops
- Database: EpiMap (Boix 2021), GeneHancer, FANTOM5 (pre-computed reference)
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.
Algorithmic Taxonomy
| Method | Inputs | Mathematics | Strength | Fails when |
|---|
| 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 5 ENCODE cell types if Hi-C not available |
| ENCODE-rE2G (ENCODE 2024) | 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 |
| 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 |
| 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) |
| 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 |
| Capture Hi-C / PCHi-C (CHiCAGO) | Promoter Capture Hi-C | Asymptotic CHiCAGO score | High-resolution promoter-anchored | Wet-lab cost; promoter capture only |
| 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 |
| 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 |
Methodology evolves; verify against current Engreitz lab releases (ABC), ENCODE 4 publications (ENCODE-rE2G), and Mumbach 2017 (HiChIP) before locking pipelines.
ABC Mathematics
For each candidate (enhancer E, gene G) pair within the cis window (default 5 Mb):
ABC(E -> G) = Activity_E * Contact_E,G / sum_{all e in window}(Activity_e * Contact_e,G)
- Activity_E = ATAC reads at E * H3K27ac reads at E (geometric mean of normalized signals; reflects "enhancer strength")
- Contact_E,G = Hi-C/Micro-C contact frequency from E to G's TSS (after distance-correction)
- Window = +/- 5 Mb cis (default; ENCODE-rE2G uses 1 Mb)
Threshold typical: ABC >= 0.02 for high-confidence; >= 0.01 for exploratory.
When Hi-C is unavailable, ABC uses an "average contact" derived from 5 ENCODE Hi-C cell types as proxy. Performance degrades but it's not zero (Fulco 2019).
ENCODE-rE2G Differences from ABC
ENCODE-rE2G (2024) is a reformulation:
- Logistic regression trained on CRISPRi-FlowFISH ground truth (~10 cell types)
- Features: ABC score components + 3D contact + distance + activity ratios
- Multiple feature configurations: "abc-features", "no-hic-features" for cells without 3D data
- Output: Per-pair probability of regulatory connection
- Pre-trained models for ENCODE cell lines; logistic params vary by cell type
ENCODE-rE2G generally outperforms ABC at CRISPRi recall, especially at modest distances (50-500 kb). For ENCODE cell types, prefer ENCODE-rE2G; for novel cell types, ABC remains the default.
Per-Tool Failure Modes
ABC -- Wrong cell-type-matched Hi-C
Trigger: Using K562 Hi-C contact when actual cell type is GM12878.
Mechanism: Contact frequencies differ across cell types at compartment and TAD boundaries; using mismatched Hi-C produces wrong ABC scores.
Symptom: ABC predictions concentrate at known K562-specific loci even when ATAC data is from GM12878.
Fix: Use cell-type-matched Hi-C or Micro-C. If unavailable, ABC's "average HiC" (5-cell-type pooled) is the documented fallback with acknowledged degradation. Document the proxy in methods.
ABC -- H3K27ac normalization
Trigger: H3K27ac ChIP-seq with different sequencing depth than ATAC.
Mechanism: ABC's "Activity" is the geometric mean of accessibility and H3K27ac signals; both must be normalized to the same scale.
Symptom: Activity scores skewed; some peaks have very high activity from H3K27ac alone, others from ATAC alone.
Fix: Normalize both signals to reads-per-million in peaks (RPM-IP) before combining. Use ABC's --qnorm flag with a quantile-normalization reference file (e.g. --qnorm src/EnhancersQNormRef.K562.txt from the ABC repo).
ENCODE-rE2G -- Cell type not in pre-trained set
Trigger: Running pre-trained model on a primary cell type not in CRISPRi training.
Mechanism: Logistic regression coefficients learned from ENCODE cell types may not transfer to primary tissues.
Fix: Use the closest ENCODE cell type (myeloid lineage -> K562; lymphoid -> GM12878; hepatic -> HepG2). Document the proxy. For high-stakes use, custom retraining requires CRISPRi-FlowFISH data.
Cicero -- No Hi-C concordance benchmark
Trigger: Reporting Cicero connections as enhancer-gene calls without external validation.
Mechanism: Cicero is statistical co-accessibility; correlation with Hi-C 3D contacts is ~30-50%. Many strong Cicero connections are NOT Hi-C-validated.
Fix: When Hi-C is available, cross-validate; report both. When only ATAC, use Cicero with the explicit caveat that connections are co-accessibility hypotheses, not contact predictions.
HiChIP -- Loop calling threshold
Trigger: Default FitHiChIP at FDR < 0.05.
Mechanism: HiChIP loops are abundant (10k-100k per dataset); FDR alone produces a long tail of weak loops.
Fix: Threshold at FDR < 0.05 AND number of contacts per loop >= 5; or use the top N most significant where N = expected number of loops based on cell type.
EpiMap / GeneHancer -- Cell-type-agnostic limitation
Trigger: Using EpiMap or GeneHancer pre-computed pairs for a specific cell type.
Mechanism: These references aggregate across many tissues / experiments; cell-type-specific connections are diluted.
Fix: Use as a baseline / sanity check, not as the primary call. ABC or ENCODE-rE2G in the actual cell type is preferred.
Decision Tree by Available Data
| Available data | Recommended method |
|---|
| ATAC + H3K27ac + matched Hi-C/Micro-C | ABC or ENCODE-rE2G (with cell-type-matched contact) |
| ATAC + H3K27ac, no Hi-C | ABC with average HiC fallback; or ENCODE-rE2G no-hic model |
| ATAC only, no H3K27ac | Cicero (atac-seq/co-accessibility); ABC with synthetic activity |
| ATAC + H3K27ac HiChIP | FitHiChIP loops + ABC; intersect for high confidence |
| Multiome (ATAC + RNA same cell) | LinkPeaks (Signac) for direct correlation; SCENIC+ for TF networks |
| ENCODE cell type | Pre-computed ENCODE-rE2G predictions (download) |
| Tissue with limited public data | ABC + acknowledge proxy; do not rely on EpiMap |
| Multi-cell-type scATAC | scBasset (atac-seq/deep-learning-atac) for sequence-based per-cell |
| Want experimental validation | CRISPRi-FlowFISH design; use predictions as targeted hypotheses |
ABC Standard Pipeline
Goal: Compute per-(enhancer, gene) ABC scores combining ATAC accessibility, H3K27ac activity, and Hi-C contact.
Approach: Build RPGC-normalized ATAC and H3K27ac bigWigs, define non-promoter candidate enhancers from ATAC peaks, run ABC neighborhoods to compute per-candidate activity, then run ABC predict against a Hi-C contact matrix and threshold the per-pair ABC score.
bamCoverage --bam atac.bam --outFileName atac.bw --binSize 50 --normalizeUsing RPGC \
--effectiveGenomeSize 2913022398 --numberOfProcessors 8
bedtools intersect -v -a atac_peaks.narrowPeak -b promoter_regions.bed > candidate_enhancers.bed
python /path/ABC-Enhancer-Gene-Prediction/workflow/scripts/run.neighborhoods.py \
--candidate_enhancer_regions candidate_enhancers.bed \
--genes refseq_protein_coding.bed \
--H3K27ac h3k27ac.bw \
--DHS atac.bw \
--chrom_sizes hg38.chrom.sizes \
--ubiquitously_expressed_genes Genes.ubiquitously_expressed.txt \
--cellType MyCellType \
--outdir abc_out/
python /path/ABC-Enhancer-Gene-Prediction/workflow/scripts/predict.py \
--enhancers abc_out/EnhancerList.txt \
--genes abc_out/GeneList.txt \
--HiCdir hic_data/ \
--hic_resolution 5000 \
--score_column ABC.Score \
--threshold 0.02 \
--cellType MyCellType \
--outdir abc_out/Predictions/
ABC.Score >= 0.02 is the standard threshold validated in Fulco 2019 against CRISPRi-FlowFISH; >= 0.04 is a stricter cut sometimes used in the ABC pipeline documentation for higher precision (no separate primary-paper calibration).
ENCODE-rE2G
git clone https://github.com/EngreitzLab/ENCODE_rE2G
cd ENCODE_rE2G
snakemake --use-conda --cores 16 \
--config cell_type=K562 \
atac_bw=atac.bw \
h3k27ac_bw=h3k27ac.bw \
hic_directory=hic_data/
Pre-trained models are at https://github.com/EngreitzLab/ENCODE_rE2G/tree/main/models. Choose by tissue similarity if exact cell type not present.
CRISPRi-FlowFISH Validation Framework
CRISPRi-FlowFISH (Fulco 2019) is the experimental gold-standard:
- Design sgRNAs tiling each candidate enhancer
- Transduce CRISPRi-expressing cells; FACS by gene expression (FlowFISH for endogenous; reporter for ectopic)
- Sequence sgRNAs in low- vs high-expression bins; compute log2 enrichment per sgRNA
- Significance: meta-test across sgRNAs in same enhancer
A 2-fold expression decrease (p < 0.05) confirms the enhancer regulates the gene.
For predictions to be publication-grade, ENCODE 4 expects:
- Test set sensitivity / specificity against published CRISPRi-FlowFISH catalogs (Fulco 2019: K562; Gasperini 2019; Schraivogel 2020 multi-cell-type)
- Effect-size correlation between predicted score and observed expression effect
- Distance bias check (predictors over-rank close-distance pairs)
Reconciling Methods
| Pattern | Likely cause | Action |
|---|
| ABC and ENCODE-rE2G disagree | Different feature weighting; different training distributions | Both valid; report intersection as high-confidence |
| ABC strong, Cicero weak | Co-accessibility sparse for that cell type | Trust ABC if Hi-C is matched |
| HiChIP loop with no ABC prediction | Loop is below ABC threshold; or peak set too narrow | Lower threshold or expand candidate enhancers |
| ENCODE-rE2G high probability, no CRISPRi support | Could be context-dependent biology or false positive | Prioritize for follow-up; not a publishable claim alone |
| EpiMap pair not in ABC | Pre-computed reference is cell-type-aggregated | Use ABC for cell-type-specific |
Operational rule for high-confidence reporting: Predictions used for therapeutic target nomination must be (a) above ABC >= 0.02 OR ENCODE-rE2G >= 0.5, AND (b) consistent across two methods (ABC + ENCODE-rE2G or ABC + HiChIP), AND (c) validated experimentally (CRISPRi-FlowFISH preferred). Single-method high-score predictions are exploratory hypotheses.
Combining Multiple Predictions
Goal: Build a high-confidence enhancer-gene set by intersecting ABC, ENCODE-rE2G, and HiChIP evidence.
Approach: Load each method's output, merge ABC and ENCODE-rE2G on enhancer-gene pair above per-method thresholds, then flag pairs with HiChIP loop support for triple-method evidence.
import pandas as pd
abc = pd.read_csv('abc_predictions.tsv', sep='\t')
re2g = pd.read_csv('encode_re2g.tsv.gz', sep='\t')
hichip = pd.read_csv('fithichip_loops.bedpe', sep='\t', header=None,
names=['chr1','s1','e1','chr2','s2','e2','name','score'])
high_conf = abc[abc['ABC.Score'] >= 0.02].merge(
re2g[re2g['re2g_score'] >= 0.5],
on=['enhancer_id', 'gene'])
hichip_anchors = ...
high_conf['hichip_support'] = high_conf['enhancer_id'].isin(hichip_anchors)
Common Errors
| Error / symptom | Cause | Solution |
|---|
| ABC predictions concentrate at TSSs | Did not exclude promoter regions from candidates | Pre-filter bedtools intersect -v against promoters |
| Activity scores all very small | H3K27ac or ATAC bigWig in wrong scale | Use RPGC normalization |
| ENCODE-rE2G model not converging | Pre-trained model loaded for wrong cell type | Match training cell type via cell_type config |
| Cicero connections used as enhancer-gene calls | Method confusion (co-accessibility vs contact) | Switch to ABC if Hi-C available; or document as co-accessibility hypothesis |
| Hi-C resolution too coarse | Default 25 kb resolution masks fine ABC structure | Use 5 kb or 10 kb if Micro-C available |
| FitHiChIP many loops, low specificity | Default FDR alone | Add contact count threshold; or use ENCODE-rE2G HiChIP-trained model |
| GeneHancer / FANTOM5 used as primary call | Cell-type-agnostic limitation | Use as baseline only |
References
- Fulco CP et al 2019 Nat Genet 51:1664 (ABC; CRISPRi-FlowFISH validation)
- Nasser J et al 2021 Nature 593:238 (ABC genome-wide application)
- ENCODE Project Consortium 2024 (ENCODE-rE2G)
- Mumbach MR et al 2017 Nat Genet 49:1602 (HiChIP H3K27ac)
- Bhattacharyya S et al 2019 Nature Communications 10:4221 (FitHiChIP)
- Boix CA et al 2021 Nature 590:300 (EpiMap reference)
- Gasperini M et al 2019 Cell 176:377 (CRISPRi at scale)
- Schraivogel D et al 2020 Nat Methods 17:629 (multi-cell-type CRISPRi)
- Pliner HA et al 2018 Mol Cell 71:858 (Cicero co-accessibility)
Related Skills
- atac-seq/co-accessibility - Cicero (ATAC-only enhancer-promoter inference)
- atac-seq/atac-peak-calling - Generate enhancer candidates
- atac-seq/consensus-peakset - Fixed-width enhancer regions
- atac-seq/deep-learning-atac - chromBPNet variant effect at predicted enhancers
- atac-seq/single-cell-atac - Per-cell-type scATAC inputs
- hi-c-analysis/loop-calling - Hi-C / Micro-C contact prediction
- hi-c-analysis/contact-pairs - Hi-C / Micro-C input
- chip-seq/peak-calling - H3K27ac peaks
- gene-regulatory-networks/scenic-regulons - Downstream TF -> target inference
