bio-gene-regulatory-networks-multiomics-grn
PKU-YuanGroup/OpenAI4SBuild enhancer-driven gene regulatory networks (eGRNs) by integrating single-cell RNA-seq and ATAC-seq using SCENIC+, CellOracle base GRNs, Pando, FigR,…
Scores out of 100 · grade A
2026-08-22Works
40% of the score100/100
- Loads cleanly: valid frontmatter, required fields present, no dangling references.
Maintained
25% of the score100/100
- no commits in the last 12 weeks
Adopted
20% of the score35/100
- 335 stars on the source repo.
Documented
15% of the score100/100
- 1,925 words with worked examples.
- Ships 3 bundled files.
Install
npx skills add PKU-YuanGroup/OpenAI4S/bio-gene-regulatory-networks-multiomics-grnWhat it says it does
Build enhancer-driven gene regulatory networks (eGRNs) by integrating single-cell RNA-seq and ATAC-seq using SCENIC+, CellOracle base GRNs, Pando, FigR, DIRECT-NET, TRIPOD, and scMEGA. Covers the accessibility-defines-enhancers principle, peak-to-gene linking and its cell-composition confound, the paired-vs-unpaired decision, and TF-region-gene eRegulon triplets. Use when analyzing 10x multiome or paired/unpaired scRNA+scATAC to infer cis-regulatory GRNs. For RNA-only regulons see scenic-regulons; for in silico TF perturbation see perturbation-simulation.
Also in PKU-YuanGroup/OpenAI4S
| Artifact | Score | What the check found | Type | Reach | Last commit |
|---|---|---|---|---|---|
| bio-atac-seq-allele-specific-accessibilityPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-atac-seq-atac-peak-callingPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-atac-seq-consensus-peaksetPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-atac-seq-deep-learning-atacPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-atac-seq-footprintingPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-causal-genomics-effector-gene-prioritizationPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today |
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