bio-atac-seq-enhancer-gene-linking
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.
npx skills add BioTender-max/awesome-bio-agent-skills --skill enhancer-gene-linking --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## 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 loop
- Version Compatibility
- Algorithmic Taxonomy
- ABC Mathematics
- ENCODE-rE2G Differences from ABC
- Per-Tool Failure Modes
- ABC -- Wrong cell-type-matched Hi-C
- ABC -- H3K27ac normalization
- ENCODE-rE2G -- Cell type not in pre-trained set
- Cicero -- No Hi-C concordance benchmark
- HiChIP -- Loop calling threshold
- EpiMap / GeneHancer -- Cell-type-agnostic limitation
- Decision Tree by Available Data
- ABC Standard Pipeline
- ENCODE-rE2G
bamCoverage --bam atac.bam --outFileName atac.bw --binSize 50 --normalizeUsing RPGC \ Filter to non-promoter regions bedtools intersect -v -a atac_peaks.narrowPeak -b promoter_regions.bed > candidate_enhancers.bed Script path: legacy ABC = src/run.neighborhoods.py; Snakemake-based modern = workflow/scripts/run.neighborhoods.py python /path/ABC-Enhancer-Gene-Prediction/workflow/scripts/run.neighborhoods.py \ python /path/ABC-Enhancer-Gene-Prediction/workflow/scripts/predict.py \ git clone https://github.com/EngreitzLab/ENCODE_rE2G cd ENCODE_rE2G Edit config.yaml with cell type, ATAC, H3K27ac paths Run with appropriate model (cell-type-matched logistic regression weights)
What does the bio-atac-seq-enhancer-gene-linking skill do?
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.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill enhancer-gene-linking --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.