[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DAQKDTA7WES0E3PA564VBH":3,"skill-same:01M6DAQKDTA7WES0E3PA564VBH":97},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":77,"commitSha":78,"skillDirectory":79,"forksCount":14,"repositoryHtmlUrl":80,"repositoryHomepageUrl":6,"ownerHtmlUrl":81,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":82,"install":83,"resources":96},"01M6DAQKDTA7WES0E3PA564VBH","tooluniverse-epigenomics-chromatin",null,"Epigenomics and chromatin accessibility research -- histone modification ChIP-seq data from ENCODE, CTCF binding and chromatin architecture, eQTL analysis connecting variants to gene regulation, gene expression correlation with chromatin marks, regulatory element identification via SCREEN\u002FUCSC cCREs, transcription factor binding motifs via JASPAR\u002FReMap, and variant regulatory scoring via RegulomeDB. Use when users ask about histone marks, chromatin states, CTCF binding, eQTLs, cis-regulatory elements, enhancer\u002Fpromoter annotation, or chromatin accessibility in specific cell types.","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-epigenomics-chromatin\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"tooluniverse epigenomics chromatin","regulatory","factor","atac","elements","cell","gene expression","scoring","transcription","cis","research","use","ask","analysis","correlation","data","state","architecture","accessibility","2026-11-10 10:12:30",false,"---\nname: tooluniverse-epigenomics-chromatin\ndescription: >\n  Epigenomics and chromatin accessibility research -- histone modification ChIP-seq data from ENCODE,\n  CTCF binding and chromatin architecture, eQTL analysis connecting variants to gene regulation,\n  gene expression correlation with chromatin marks, regulatory element identification via SCREEN\u002FUCSC cCREs,\n  transcription factor binding motifs via JASPAR\u002FReMap, and variant regulatory scoring via RegulomeDB.\n  Use when users ask about histone marks, chromatin states, CTCF binding, eQTLs, cis-regulatory elements,\n  enhancer\u002Fpromoter annotation, or chromatin accessibility in specific cell types.\ntriggers:\n  - keywords: [histone, ChIP-seq, chromatin, ENCODE, eQTL, CTCF, enhancer, promoter, cCRE, H3K27ac, H3K4me3, H3K4me1, chromatin state, ATAC-seq, regulatory element, RegulomeDB]\n  - patterns: [\"histone modification\", \"chromatin accessibility\", \"chromatin architecture\", \"gene regulation\", \"regulatory variant\", \"expression QTL\", \"cis-regulatory\"]\n---\n\n# Epigenomics and Chromatin Accessibility Research\n\n## NOT for (use other skills instead)\n\n- Methylation array data processing (CpG beta values, differential methylation) -> Use `tooluniverse-epigenomics`\n- RNA-seq differential expression -> Use `tooluniverse-rnaseq-deseq2`\n- GWAS variant interpretation -> Use `tooluniverse-gwas-snp-interpretation`\n- Variant functional annotation from VCF -> Use `tooluniverse-variant-analysis`\n\n---\n\n## Reasoning: Classify the Question First\n\nBefore calling any tool, identify which question type you're answering. Each maps to a different tool set.\n\n**(a) Which regulatory elements exist at a locus?**\nUse UCSC_get_encode_cCREs (region-based) or SCREEN_get_regulatory_elements (gene-based). Then check ENCODE_get_chromatin_state for ChromHMM annotation and ENCODE_search_chromatin_accessibility for ATAC-seq evidence.\n\n**(b) Which TFs bind there?**\nUse ReMap_get_transcription_factor_binding for ChIP-seq experiments. Use jaspar_search_matrices to retrieve binding motifs and check whether the sequence disrupts a known motif.\n\n**(c) How does a variant affect regulation?**\nUse RegulomeDB_query_variant for a scored summary. Then build multi-layer evidence: UCSC_get_encode_cCREs (is the variant in a cCRE?), GTEx_get_single_tissue_eqtls (is it an eQTL?), jaspar_search_matrices (does it disrupt a TF motif?). No single layer is sufficient — see the variant reasoning section below.\n\n**(d) What genes are regulated by an element?**\nUse GTEx_get_single_tissue_eqtls or GTEx_query_eqtl to find genes whose expression is associated with variants in the element. Use SCREEN_get_regulatory_elements with element_type=\"PLS\"\u002F\"pELS\"\u002F\"dELS\" to classify element-to-promoter relationships.\n\n---\n\n## Reasoning: Histone Marks\n\nUse histone mark identity to guide tool queries and interpret results before fetching data.\n\n- **H3K4me3** = active promoter. If present without H3K27ac, promoter may be active but not hyperacetylated.\n- **H3K27ac** = active enhancer or promoter. Strong signal = regulatory element is on.\n- **H3K4me1** = poised or active enhancer. Needs H3K27ac to confirm activity; H3K4me1 alone = poised.\n- **H3K27me3** = Polycomb repression. Gene is silenced by PRC2.\n- **H3K9me3** = constitutive heterochromatin. Region is structurally silenced.\n- **H3K36me3** = transcribed gene body. Confirms active elongation.\n\n**Bivalent promoter logic**: If you observe H3K4me3 + H3K27me3 together at the same locus, the promoter is bivalent — poised but not active. This is common in stem cells and developmentally regulated genes. Do not report such genes as \"actively transcribed.\" Use GTEx_get_expression_summary to check if the gene is actually expressed in the tissue of interest.\n\n**Inference rule**: If a user asks about a mark you haven't queried yet, ask: does the mark you *have* found already answer the question? H3K4me3 in a region predicts active transcription; you may not need to also query H3K36me3 unless confirming elongation specifically.\n\n---\n\n## Reasoning: eQTL Interpretation\n\nAn eQTL means variant X is statistically associated with expression of gene Y in tissue T. Before reporting eQTL results, apply this chain of reasoning:\n\n1. **Association ≠ causation.** The variant may be in LD with the causal variant. Report effect size (NES) and p-value, not causality.\n2. **Check tissue specificity.** Use GTEx_get_multi_tissue_eqtls to see whether the effect is shared across tissues (m-value near 1.0 in many tissues) or tissue-specific (m-value near 1.0 in only one tissue). Tissue-specific eQTLs are stronger candidates for cell-type-specific regulation.\n3. **Cross-reference with chromatin.** Is the eQTL variant inside a cCRE? Use UCSC_get_encode_cCREs on the variant's coordinates. If yes, the variant likely acts through a regulatory element.\n4. **Check TF motif disruption.** Use jaspar_search_matrices to find motifs overlapping the eQTL locus. If the variant alleles differ in motif score, it is a candidate causal variant.\n5. **Effect direction matters.** Positive NES = reference allele increases expression. Negative NES = alternative allele decreases expression.\n\n---\n\n## Reasoning: Variant Regulatory Impact\n\nTo assess a non-coding variant's regulatory impact, build evidence from multiple independent layers. No single layer is sufficient.\n\n**Layer 1 — RegulomeDB score**: High probability (score 1a–2b) means convergent evidence from eQTL + TF binding + DNase. Score 4–7 means weak support. Use as a triage filter.\n\n**Layer 2 — Regulatory element overlap**: Query UCSC_get_encode_cCREs at the variant's coordinates. If the variant falls in a cCRE (especially PLS or pELS), it is in a functional context.\n\n**Layer 3 — eQTL evidence**: Query GTEx_get_single_tissue_eqtls for nearby genes. If the variant is a significant eQTL, the association supports regulatory function.\n\n**Layer 4 — TFBS disruption**: Query jaspar_search_matrices for TFs with motifs at the locus. If the variant changes a high-information-content position in a motif, it is a strong functional candidate.\n\n**Synthesis rule**: Report each layer separately. Convergence across 3+ layers = high-confidence regulatory variant. A single layer (e.g., eQTL alone) warrants caution.\n\n---\n\n## Phase 0: Disambiguation\n\n**MyGene_query_genes**: `query` (string). Converts gene symbols to Ensembl IDs and coordinates. Filter results by `symbol == '\u003CGENE>'` — first hit may not match.\n\n**ensembl_lookup_gene**: `gene_id` (Ensembl ID), `species` (REQUIRED, \"homo_sapiens\"). Returns chr\u002Fstart\u002Fend.\n\nKey format notes:\n- GTEx requires versioned GENCODE IDs: `ENSG00000012048.20`\n- RegulomeDB takes rsIDs: `rs4994`\n- GTEx variant IDs: `chr17_43705621_T_C_b38`\n- UCSC cCRE regions: `chrom=\"chr17\", start=7668421, end=7687490`\n\n---\n\n## Phase 1: Histone Modification & ChIP-seq\n\n**ENCODE_search_histone_experiments**: `target` (histone mark), `cell_type` (or `tissue` alias), `biosample_term_name` (most explicit ENCODE ontology name), `limit`.\n\nENCODE anatomy term notes: \"breast\" → try \"breast epithelium\" or \"mammary epithelial cell\"; \"brain\" → \"brain\" works; if 0 results, append \"tissue\", \"epithelium\", or \"cell\".\n\n```python\nresult = tu.tools.ENCODE_search_histone_experiments(target=\"H3K27ac\", cell_type=\"GM12878\", limit=5)\n# result[\"data\"][\"experiments\"][0][\"accession\"] -> \"ENCSR000AKC\"\n```\n\n**GEO_search_chipseq_datasets**: Fallback for older or non-ENCODE ChIP-seq datasets.\n\n---\n\n## Phase 2: Chromatin Accessibility & Architecture\n\n**ENCODE_search_chromatin_accessibility**: `cell_type`, `limit`. Returns ATAC-seq experiments.\n\n**ENCODE_get_chromatin_state**: `cell_type`, `limit`. Returns ChromHMM 15-state annotations (TssA, Enh, TssBiv, ReprPC, etc.). Use to confirm bivalent promoter state or enhancer classification.\n\n**ENCODE_search_rnaseq_experiments**: `assay_type` (default `\"total RNA-seq\"`), `biosample`, `limit`. If 0 results, retry with `assay_type=\"polyA plus RNA-seq\"`.\n\n**GEO_search_rnaseq_datasets** \u002F **GEO_search_atacseq_datasets**: `query`, `organism`, `limit` (also `max_results`). GEO adds \"ATAC-seq\" automatically for the ATAC tool.\n\n**ReMap_get_transcription_factor_binding** (CTCF): `gene_name=\"CTCF\"`, `cell_type`, `limit`. Returns ENCODE TF ChIP-seq experiments.\n\n---\n\n## Phase 3: Regulatory Element Identification\n\n**SCREEN_get_regulatory_elements**: `gene_name`, `element_type` (PLS\u002FpELS\u002FdELS\u002FCTCF-only\u002FDNase-H3K4me3), `limit`.\n\n**UCSC_get_encode_cCREs**: `chrom` (REQUIRED), `start` (REQUIRED), `end` (REQUIRED), `genome` (default \"hg38\"). Returns cCREs with Z-scores for DNase, H3K4me3, H3K27ac, CTCF signals.\n\n```python\n# cCREs near TP53\nresult = tu.tools.UCSC_get_encode_cCREs(chrom=\"chr17\", start=7668421, end=7687490, genome=\"hg38\")\n```\n\n**ENCODE_search_annotations**: `annotation_type` (\"candidate Cis-Regulatory Elements\" or \"chromatin state\"), `biosample_term_name`, `organism`, `assembly`, `limit`.\n\n---\n\n## Phase 4: eQTL Analysis\n\n**GTEx_get_single_tissue_eqtls**: `gene_symbol`. Returns all significant eQTLs across tissues with snpId, pValue, tissueSiteDetailId, nes (normalized effect size).\n\n```python\nresult = tu.tools.GTEx_get_single_tissue_eqtls(gene_symbol=\"BRCA1\")\nfrom collections import Counter\ntissue_counts = Counter(e[\"tissueSiteDetailId\"] for e in result[\"data\"])\n```\n\n**GTEx_query_eqtl**: `gene_symbol`, `tissue` (tissueSiteDetailId), `page` (1-indexed), `size`. Use for a specific tissue.\n\n**GTEx_get_multi_tissue_eqtls**: `operation=\"get_multi_tissue_eqtls\"`, `gencode_id` (versioned, REQUIRED). Returns per-variant m-values showing tissue-sharing. m-value near 1.0 = effect present; near 0.0 = absent.\n\n```python\nresult = tu.tools.GTEx_get_multi_tissue_eqtls(\n    operation=\"get_multi_tissue_eqtls\",\n    gencode_id=\"ENSG00000012048.20\"\n)\n```\n\n**GTEx_calculate_eqtl**: `operation=\"calculate_eqtl\"`, `gencode_id`, `variant_id` (chr_pos_ref_alt_b38), `tissue_site_detail_id`. Works for non-significant pairs.\n\n**eQTL_list_datasets** \u002F **eQTL_get_associations**: EBI eQTL Catalogue. Use `dataset_id` (from list call), `gene_id` (Ensembl), `variant`. Complementary to GTEx.\n\n---\n\n## Phase 5: Gene Expression Context\n\n**GTEx_get_expression_summary**: `gene_symbol`. Recommended — auto-resolves GENCODE versions. Returns median TPM per tissue.\n\n```python\nresult = tu.tools.GTEx_get_expression_summary(gene_symbol=\"BRCA1\")\ntop_tissues = sorted(result[\"data\"], key=lambda x: x[\"median\"], reverse=True)[:5]\n```\n\n**GTEx_get_median_gene_expression**: Requires `operation=\"get_median_gene_expression\"` + exact versioned `gencode_id`. Use only when version precision is needed.\n\n**GTEx_get_tissue_sites**: No params. Returns all tissueSiteDetailId values.\n\n---\n\n## Phase 6: Transcription Factor Binding\n\n**jaspar_search_matrices**: `name` (TF name), `collection` (\"CORE\"), `tax_group` (\"vertebrates\"), `species` (\"9606\"), `page_size`.\n\n```python\nresult = tu.tools.jaspar_search_matrices(name=\"CTCF\", collection=\"CORE\", page_size=5)\n```\n\n**jaspar_get_matrix**: Returns position frequency matrix for a JASPAR matrix ID. Use to check if a variant allele disrupts a high-information-content position.\n\n**ReMap_get_transcription_factor_binding**: `gene_name` (TF), `cell_type`, `limit`. Same tool used for CTCF in Phase 2 — applies to any TF.\n\n**STRING_get_functional_annotations**: `identifiers` (gene name), `species` (9606), `category` (\"Process\"\u002F\"Function\"\u002F\"KEGG\"). Returns GO\u002FKEGG\u002FReactome annotations for regulatory context.\n\n---\n\n## Phase 7: Variant Regulatory Scoring\n\n**RegulomeDB_query_variant**: `rsid` (e.g., \"rs4994\"). Returns probability, ranking (1a = strongest, 7 = weakest), and tissue-specific scores.\n\n```python\nresult = tu.tools.RegulomeDB_query_variant(rsid=\"rs4994\")\nscore = result[\"data\"][\"regulome_score\"]\n# score[\"ranking\"]: \"1a\" (eQTL + TF + motif + DNase) ... \"7\" (no evidence)\n# score[\"probability\"]: 0.0–1.0\ntop_tissues = sorted(score[\"tissue_specific_scores\"].items(), key=lambda x: float(x[1]), reverse=True)[:5]\n```\n\nRankings 1a–1f all have eQTL evidence. Rankings 2a–3b have TF binding without eQTL. Rankings 4–7 have decreasing evidence. Use ranking \u003C= 2b as a threshold for \"strong regulatory support.\"\n\n---\n\n## Phase 8: Integration\n\nCombine evidence tiers before reporting:\n\n- **T1 (Direct experimental)**: ENCODE ChIP-seq experiments, GTEx eQTL p \u003C 5e-8\n- **T2 (Strong computational)**: RegulomeDB score \u003C= 2, SCREEN cCRE classification, ChromHMM state\n- **T3 (Moderate)**: eQTL p \u003C 0.05, JASPAR motif match, multi-tissue m-value > 0.5\n- **T4 (Annotation-based)**: STRING GO terms, literature references\n\nConvergence of T1+T2 evidence from independent sources (e.g., ENCODE ChIP-seq overlapping a RegulomeDB 1a variant with GTEx eQTL) constitutes strong evidence for regulatory function. Contradictions between layers (e.g., high RegulomeDB score but no eQTL) should be explicitly noted.\n\n---\n\n## Fallback Strategies\n\n| Phase | Primary Tool | Fallback |\n|-------|-------------|----------|\n| Histone ChIP-seq | ENCODE_search_histone_experiments | GEO_search_chipseq_datasets |\n| RNA-seq | ENCODE_search_rnaseq_experiments (total RNA-seq) | retry with polyA plus RNA-seq |\n| ATAC-seq | ENCODE_search_chromatin_accessibility | GEO_search_atacseq_datasets |\n| cCREs | UCSC_get_encode_cCREs | SCREEN_get_regulatory_elements |\n| eQTLs | GTEx_get_single_tissue_eqtls | eQTL_get_associations (EBI) |\n| Expression | GTEx_get_expression_summary | GTEx_get_median_gene_expression |\n| TF motifs | jaspar_search_matrices | ReMap_get_transcription_factor_binding |\n| Variant scoring | RegulomeDB_query_variant | combine eQTL + TF binding manually |\n","# Epigenomics and Chromatin Accessibility Research\n\n## NOT for (use other skills instead)\n\n- Methylation array data processing (CpG beta values, differential methylation) -> Use `tooluniverse-epigenomics`\n- RNA-seq differential expression -> Use `tooluniverse-rnaseq-deseq2`\n- GWAS variant interpretation -> Use `tooluniverse-gwas-snp-interpretation`\n- Variant functional annotation from VCF -> Use `tooluniverse-variant-analysis`\n\n---\n\n## Reasoning: Classify the Question First\n\nBefore calling any tool, identify which question type you're answering. Each maps to a different tool set.\n\n**(a) Which regulatory elements exist at a locus?**\nUse UCSC_get_encode_cCREs (region-based) or SCREEN_get_regulatory_elements (gene-based). Then check ENCODE_get_chromatin_state for ChromHMM annotation and ENCODE_search_chromatin_accessibility for ATAC-seq evidence.\n\n**(b) Which TFs bind there?**\nUse ReMap_get_transcription_factor_binding for ChIP-seq experiments. Use jaspar_search_matrices to retrieve binding motifs and check whether the sequence disrupts a known motif.\n\n**(c) How does a variant affect regulation?**\nUse RegulomeDB_query_variant for a scored summary. Then build multi-layer evidence: UCSC_get_encode_cCREs (is the variant in a cCRE?), GTEx_get_single_tissue_eqtls (is it an eQTL?), jaspar_search_matrices (does it disrupt a TF motif?). No single layer is sufficient — see the variant reasoning section below.\n\n**(d) What genes are regulated by an element?**\nUse GTEx_get_single_tissue_eqtls or GTEx_query_eqtl to find genes whose expression is associated with variants in the element. Use SCREEN_get_regulatory_elements with element_type=\"PLS\"\u002F\"pELS\"\u002F\"dELS\" to classify element-to-promoter relationships.\n\n---\n\n## Reasoning: Histone Marks\n\nUse histone mark identity to guide tool queries and interpret results before fetching data.\n\n- **H3K4me3** = active promoter. If present without H3K27ac, promoter may be active but not hyperacetylated.\n- **H3K27ac** = active enhancer or promoter. Strong signal = regulatory element is on.\n- **H3K4me1** = poised or active enhancer. Needs H3K27ac to confirm activity; H3K4me1 alone = poised.\n- **H3K27me3** = Polycomb repression. Gene is silenced by PRC2.\n- **H3K9me3** = constitutive heterochromatin. Region is structurally silenced.\n- **H3K36me3** = transcribed gene body. Confirms active elongation.\n\n**Bivalent promoter logic**: If you observe H3K4me3 + H3K27me3 together at the same locus, the promoter is bivalent — poised but not active. This is common in stem cells and developmentally regulated genes. Do not report such genes as \"actively transcribed.\" Use GTEx_get_expression_summary to check if the gene is actually expressed in the tissue of interest.\n\n**Inference rule**: If a user asks about a mark you haven't queried yet, ask: does the mark you *have* found already answer the question? H3K4me3 in a region predicts active transcription; you may not need to also query H3K36me3 unless confirming elongation specifically.\n\n---\n\n## Reasoning: eQTL Interpretation\n\nAn eQTL means variant X is statistically associated with expression of gene Y in tissue T. Before reporting eQTL results, apply this chain of reasoning:\n\n1. **Association ≠ causation.** The variant may be in LD with the causal variant. Report effect size (NES) and p-value, not causality.\n2. **Check tissue specificity.** Use GTEx_get_multi_tissue_eqtls to see whether the effect is shared across tissues (m-value near 1.0 in many tissues) or tissue-specific (m-value near 1.0 in only one tissue). Tissue-specific eQTLs are stronger candidates for cell-type-specific regulation.\n3. **Cross-reference with chromatin.** Is the eQTL variant inside a cCRE? Use UCSC_get_encode_cCREs on the variant's coordinates. If yes, the variant likely acts through a regulatory element.\n4. **Check TF motif disruption.** Use jaspar_search_matrices to find motifs overlapping the eQTL locus. If the variant alleles differ in motif score, it is a candidate causal variant.\n5. **Effect direction matters.** Positive NES = reference allele increases expression. Negative NES = alternative allele decreases expression.\n\n---\n\n## Reasoning: Variant Regulatory Impact\n\nTo assess a non-coding variant's regulatory impact, build evidence from multiple independent layers. No single layer is sufficient.\n\n**Layer 1 — RegulomeDB score**: High probability (score 1a–2b) means convergent evidence from eQTL + TF binding + DNase. Score 4–7 means weak support. Use as a triage filter.\n\n**Layer 2 — Regulatory element overlap**: Query UCSC_get_encode_cCREs at the variant's coordinates. If the variant falls in a cCRE (especially PLS or pELS), it is in a functional context.\n\n**Layer 3 — eQTL evidence**: Query GTEx_get_single_tissue_eqtls for nearby genes. If the variant is a significant eQTL, the association supports regulatory function.\n\n**Layer 4 — TFBS disruption**: Query jaspar_search_matrices for TFs with motifs at the locus. If the variant changes a high-information-content position in a motif, it is a strong functional candidate.\n\n**Synthesis rule**: Report each layer separately. Convergence across 3+ layers = high-confidence regulatory variant. A single layer (e.g., eQTL alone) warrants caution.\n\n---\n\n## Phase 0: Disambiguation\n\n**MyGene_query_genes**: `query` (string). Converts gene symbols to Ensembl IDs and coordinates. Filter results by `symbol == '\u003CGENE>'` — first hit may not match.\n\n**ensembl_lookup_gene**: `gene_id` (Ensembl ID), `species` (REQUIRED, \"homo_sapiens\"). Returns chr\u002Fstart\u002Fend.\n\nKey format notes:\n- GTEx requires versioned GENCODE IDs: `ENSG00000012048.20`\n- RegulomeDB takes rsIDs: `rs4994`\n- GTEx variant IDs: `chr17_43705621_T_C_b38`\n- UCSC cCRE regions: `chrom=\"chr17\", start=7668421, end=7687490`\n\n---\n\n## Phase 1: Histone Modification & ChIP-seq\n\n**ENCODE_search_histone_experiments**: `target` (histone mark), `cell_type` (or `tissue` alias), `biosample_term_name` (most explicit ENCODE ontology name), `limit`.\n\nENCODE anatomy term notes: \"breast\" → try \"breast epithelium\" or \"mammary epithelial cell\"; \"brain\" → \"brain\" works; if 0 results, append \"tissue\", \"epithelium\", or \"cell\".\n\n```python\nresult = tu.tools.ENCODE_search_histone_experiments(target=\"H3K27ac\", cell_type=\"GM12878\", limit=5)\n# result[\"data\"][\"experiments\"][0][\"accession\"] -> \"ENCSR000AKC\"\n```\n\n**GEO_search_chipseq_datasets**: Fallback for older or non-ENCODE ChIP-seq datasets.\n\n---\n\n## Phase 2: Chromatin Accessibility & Architecture\n\n**ENCODE_search_chromatin_accessibility**: `cell_type`, `limit`. Returns ATAC-seq experiments.\n\n**ENCODE_get_chromatin_state**: `cell_type`, `limit`. Returns ChromHMM 15-state annotations (TssA, Enh, TssBiv, ReprPC, etc.). Use to confirm bivalent promoter state or enhancer classification.\n\n**ENCODE_search_rnaseq_experiments**: `assay_type` (default `\"total RNA-seq\"`), `biosample`, `limit`. If 0 results, retry with `assay_type=\"polyA plus RNA-seq\"`.\n\n**GEO_search_rnaseq_datasets** \u002F **GEO_search_atacseq_datasets**: `query`, `organism`, `limit` (also `max_results`). GEO adds \"ATAC-seq\" automatically for the ATAC tool.\n\n**ReMap_get_transcription_factor_binding** (CTCF): `gene_name=\"CTCF\"`, `cell_type`, `limit`. Returns ENCODE TF ChIP-seq experiments.\n\n---\n\n## Phase 3: Regulatory Element Identification\n\n**SCREEN_get_regulatory_elements**: `gene_name`, `element_type` (PLS\u002FpELS\u002FdELS\u002FCTCF-only\u002FDNase-H3K4me3), `limit`.\n\n**UCSC_get_encode_cCREs**: `chrom` (REQUIRED), `start` (REQUIRED), `end` (REQUIRED), `genome` (default \"hg38\"). Returns cCREs with Z-scores for DNase, H3K4me3, H3K27ac, CTCF signals.\n\n```python\n# cCREs near TP53\nresult = tu.tools.UCSC_get_encode_cCREs(chrom=\"chr17\", start=7668421, end=7687490, genome=\"hg38\")\n```\n\n**ENCODE_search_annotations**: `annotation_type` (\"candidate Cis-Regulatory Elements\" or \"chromatin state\"), `biosample_term_name`, `organism`, `assembly`, `limit`.\n\n---\n\n## Phase 4: eQTL Analysis\n\n**GTEx_get_single_tissue_eqtls**: `gene_symbol`. Returns all significant eQTLs across tissues with snpId, pValue, tissueSiteDetailId, nes (normalized effect size).\n\n```python\nresult = tu.tools.GTEx_get_single_tissue_eqtls(gene_symbol=\"BRCA1\")\nfrom collections import Counter\ntissue_counts = Counter(e[\"tissueSiteDetailId\"] for e in result[\"data\"])\n```\n\n**GTEx_query_eqtl**: `gene_symbol`, `tissue` (tissueSiteDetailId), `page` (1-indexed), `size`. Use for a specific tissue.\n\n**GTEx_get_multi_tissue_eqtls**: `operation=\"get_multi_tissue_eqtls\"`, `gencode_id` (versioned, REQUIRED). Returns per-variant m-values showing tissue-sharing. m-value near 1.0 = effect present; near 0.0 = absent.\n\n```python\nresult = tu.tools.GTEx_get_multi_tissue_eqtls(\n    operation=\"get_multi_tissue_eqtls\",\n    gencode_id=\"ENSG00000012048.20\"\n)\n```\n\n**GTEx_calculate_eqtl**: `operation=\"calculate_eqtl\"`, `gencode_id`, `variant_id` (chr_pos_ref_alt_b38), `tissue_site_detail_id`. Works for non-significant pairs.\n\n**eQTL_list_datasets** \u002F **eQTL_get_associations**: EBI eQTL Catalogue. Use `dataset_id` (from list call), `gene_id` (Ensembl), `variant`. Complementary to GTEx.\n\n---\n\n## Phase 5: Gene Expression Context\n\n**GTEx_get_expression_summary**: `gene_symbol`. Recommended — auto-resolves GENCODE versions. Returns median TPM per tissue.\n\n```python\nresult = tu.tools.GTEx_get_expression_summary(gene_symbol=\"BRCA1\")\ntop_tissues = sorted(result[\"data\"], key=lambda x: x[\"median\"], reverse=True)[:5]\n```\n\n**GTEx_get_median_gene_expression**: Requires `operation=\"get_median_gene_expression\"` + exact versioned `gencode_id`. Use only when version precision is needed.\n\n**GTEx_get_tissue_sites**: No params. Returns all tissueSiteDetailId values.\n\n---\n\n## Phase 6: Transcription Factor Binding\n\n**jaspar_search_matrices**: `name` (TF name), `collection` (\"CORE\"), `tax_group` (\"vertebrates\"), `species` (\"9606\"), `page_size`.\n\n```python\nresult = tu.tools.jaspar_search_matrices(name=\"CTCF\", collection=\"CORE\", page_size=5)\n```\n\n**jaspar_get_matrix**: Returns position frequency matrix for a JASPAR matrix ID. Use to check if a variant allele disrupts a high-information-content position.\n\n**ReMap_get_transcription_factor_binding**: `gene_name` (TF), `cell_type`, `limit`. Same tool used for CTCF in Phase 2 — applies to any TF.\n\n**STRING_get_functional_annotations**: `identifiers` (gene name), `species` (9606), `category` (\"Process\"\u002F\"Function\"\u002F\"KEGG\"). Returns GO\u002FKEGG\u002FReactome annotations for regulatory context.\n\n---\n\n## Phase 7: Variant Regulatory Scoring\n\n**RegulomeDB_query_variant**: `rsid` (e.g., \"rs4994\"). Returns probability, ranking (1a = strongest, 7 = weakest), and tissue-specific scores.\n\n```python\nresult = tu.tools.RegulomeDB_query_variant(rsid=\"rs4994\")\nscore = result[\"data\"][\"regulome_score\"]\n# score[\"ranking\"]: \"1a\" (eQTL + TF + motif + DNase) ... \"7\" (no evidence)\n# score[\"probability\"]: 0.0–1.0\ntop_tissues = sorted(score[\"tissue_specific_scores\"].items(), key=lambda x: float(x[1]), reverse=True)[:5]\n```\n\nRankings 1a–1f all have eQTL evidence. Rankings 2a–3b have TF binding without eQTL. Rankings 4–7 have decreasing evidence. Use ranking \u003C= 2b as a threshold for \"strong regulatory support.\"\n\n---\n\n## Phase 8: Integration\n\nCombine evidence tiers before reporting:\n\n- **T1 (Direct experimental)**: ENCODE ChIP-seq experiments, GTEx eQTL p \u003C 5e-8\n- **T2 (Strong computational)**: RegulomeDB score \u003C= 2, SCREEN cCRE classification, ChromHMM state\n- **T3 (Moderate)**: eQTL p \u003C 0.05, JASPAR motif match, multi-tissue m-value > 0.5\n- **T4 (Annotation-based)**: STRING GO terms, literature references\n\nConvergence of T1+T2 evidence from independent sources (e.g., ENCODE ChIP-seq overlapping a RegulomeDB 1a variant with GTEx eQTL) constitutes strong evidence for regulatory function. Contradictions between layers (e.g., high RegulomeDB score but no eQTL) should be explicitly noted.\n\n---\n\n## Fallback Strategies\n\n| Phase | Primary Tool | Fallback |\n|-------|-------------|----------|\n| Histone ChIP-seq | ENCODE_search_histone_experiments | GEO_search_chipseq_datasets |\n| RNA-seq | ENCODE_search_rnaseq_experiments (total RNA-seq) | retry with polyA plus RNA-seq |\n| ATAC-seq | ENCODE_search_chromatin_accessibility | GEO_search_atacseq_datasets |\n| cCREs | UCSC_get_encode_cCREs | SCREEN_get_regulatory_elements |\n| eQTLs | GTEx_get_single_tissue_eqtls | eQTL_get_associations (EBI) |\n| Expression | GTEx_get_expression_summary | GTEx_get_median_gene_expression |\n| TF motifs | jaspar_search_matrices | ReMap_get_transcription_factor_binding |\n| Variant scoring | RegulomeDB_query_variant | combine eQTL + TF binding manually |\n",{"name":5,"triggers":48,"description":76},[49,67],{"keywords":50},[51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66],"histone","ChIP-seq","chromatin","ENCODE","eQTL","CTCF","enhancer","promoter","cCRE","H3K27ac","H3K4me3","H3K4me1","chromatin state","ATAC-seq","regulatory element","RegulomeDB",{"patterns":68},[69,70,71,72,73,74,75],"histone modification","chromatin accessibility","chromatin architecture","gene regulation","regulatory variant","expression QTL","cis-regulatory","Epigenomics and chromatin accessibility research -- histone modification ChIP-seq data from ENCODE, CTCF binding and chromatin architecture, eQTL analysis connecting variants to gene regulation, gene expression correlation with chromatin marks, regulatory element identification via SCREEN\u002FUCSC cCREs, transcription factor binding motifs via JASPAR\u002FReMap, and variant regulatory scoring via RegulomeDB. Use when users ask about histone marks, chromatin states, CTCF binding, eQTLs, cis-regulatory elements, enhancer\u002Fpromoter annotation, or chromatin accessibility in specific cell types.\n","main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Flibrary\u002Fskills\u002Ftooluniverse-epigenomics-chromatin","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":84,"prompt":94,"zipAvailable":95},[85,88,91],{"tool":86,"command":87},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-epigenomics-chromatin",{"tool":89,"command":90},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-epigenomics-chromatin",{"tool":92,"command":93},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-epigenomics-chromatin","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-epigenomics-chromatin\u002FSKILL.md",true,[],[98,119,148,175,188,204],{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":43},"01M6DF7D7478R55P46REYH4QAC","clarify-requirements","Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. NOT for mid-task use (injection risk); NOT when the goal is already unambiguous; use to disambiguate vague prompts before any file edits.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fworkspace-hooks\u002Fskills\u002Fclarify-requirements\u002FSKILL.md",[],[105,106,107,108,109,110,111,112,113,35,114,115,116,117,118],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":128,"publishedAt":43},"01M6DF79CA4BHJS66J8EZDBPWJ","openmm-md","Run FREE, open-source, Windows-native GPU molecular dynamics via the openmm-md MCP (OpenMM + CUDA on the local NVIDIA GPU). Use for protein \u002F protein-ligand MD when you want a free stack or are on Windows without a Schrodinger license. Covers the full workflow (prepare, solvate, minimize, equilibrate, production, analyze, mmgbsa) AND an honest decision guide vs Schrodinger Desmond, including how to run Desmond legitimately on Linux or a cloud GPU with a real license. This is a deliberately LESSER free alternative and does NOT replace the schrodinger MCP tools. Do NOT use it to pick a free tool when a validated FEP+, WaterMap, IFD-MD, or Prime MM-GBSA result is actually required.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fmcp\u002Fopenmm-md-mcp\u002Fskill\u002Fopenmm-md\u002FSKILL.md",[125],{"categoryNo":126,"categoryName":127,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[129,130,131,132,133,134,135,136,137,138,139,140,141,142,35,143,144,145,146,147],"openmm md","how","decision","pick","linux","analyze","tool","stack","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local","do","MCP",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":160,"publishedAt":43},"01M6DF76C1MG85VEWP8VZCBNMN","visual-review","Take screenshots of web pages and UI using Playwright for visual review and iteration","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fvisual-review\u002FSKILL.md",[154,157],{"categoryNo":155,"categoryName":156,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":158,"categoryName":159,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[161,162,163,164,165,166,167,168,169,170,130,171,172,173,174],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":181,"publishedAt":43},"01M6DF73AQVCBZKEN0YXEX9FXP","status","Diagnostic status report — shows metaswarm installation state, project setup, and potential issues","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstatus\u002FSKILL.md",[],[177,182,183,184,185,186,187,40],"setup","report","project","installation","project setup","issues",{"skillNo":189,"name":190,"nameZh":6,"description":191,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":192,"categories":193,"keywords":195,"publishedAt":43},"01M6DF6X6C2XB0P6GX76P7TTYV","start","Use when starting work on any task, when the user mentions metaswarm, or when the user wants to begin tracked development work","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstart\u002FSKILL.md",[194],{"categoryNo":126,"categoryName":127,"taxonomyType":21,"taxonomyTypeDesc":22},[190,196,110,197,198,199,35,200,201,202,203],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":205,"name":182,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":207,"categories":208,"keywords":209,"publishedAt":43},"01M6DF6QMJ9AJKFKHHKRT005PQ","Interactive project setup — detects your project, configures metaswarm, writes project-local files","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fsetup\u002FSKILL.md",[],[182,184,145,210,186,211],"files","interactive"]