Histone-modification ChIP-seq, ATAC-seq accessibility, chromatin state, and TF binding analysis from ENCODE, Roadmap Epigenomics, ChIP-Atlas. Use for chromatin-state-by-tissue queries, TF-binding-by-region, regulatory landscape mapping, and ENCODE-cCRE annotations. For DNA methylation use tooluniverse-epigenomics; for RNA-seq use tooluniverse-rnaseq-deseq2.
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Histone-modification ChIP-seq, ATAC-seq accessibility, chromatin state, and TF binding analysis from ENCODE, Roadmap Epigenomics, ChIP-Atlas. Use for chromatin-state-by-tissue queries, TF-binding-by-region, regulatory landscape mapping, and ENCODE-cCRE annotations. For DNA methylation use tooluniverse-epigenomics; for RNA-seq use tooluniverse-rnaseq-deseq2.
Methylation array data processing (CpG beta values, differential methylation) -> Use tooluniverse-epigenomics
RNA-seq differential expression -> Use tooluniverse-rnaseq-deseq2
GWAS variant interpretation -> Use tooluniverse-gwas-snp-interpretation
Variant functional annotation from VCF -> Use tooluniverse-variant-analysis
Reasoning: Classify the Question First
Before calling any tool, identify which question type you're answering. Each maps to a different tool set.
Use 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.
(a) Which regulatory elements exist at a locus?
(b) Which TFs bind there?
Use 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.
(c) How does a variant affect regulation?
Use 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.
(d) What genes are regulated by an element?
Use 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"/"pELS"/"dELS" to classify element-to-promoter relationships.
Reasoning: Histone Marks
Use histone mark identity to guide tool queries and interpret results before fetching data.
H3K4me3 = active promoter. If present without H3K27ac, promoter may be active but not hyperacetylated.
H3K27ac = active enhancer or promoter. Strong signal = regulatory element is on.
H3K4me1 = poised or active enhancer. Needs H3K27ac to confirm activity; H3K4me1 alone = poised.
H3K27me3 = Polycomb repression. Gene is silenced by PRC2.
H3K9me3 = constitutive heterochromatin. Region is structurally silenced.
H3K36me3 = transcribed gene body. Confirms active elongation.
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.
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.
Reasoning: eQTL Interpretation
An eQTL means variant X is statistically associated with expression of gene Y in tissue T. Before reporting eQTL results, apply this chain of reasoning:
Association ≠ causation. The variant may be in LD with the causal variant. Report effect size (NES) and p-value, not causality.
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.
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.
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.
Effect direction matters. Positive NES = reference allele increases expression. Negative NES = alternative allele decreases expression.
Reasoning: Variant Regulatory Impact
To assess a non-coding variant's regulatory impact, build evidence from multiple independent layers. No single layer is sufficient.
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.
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.
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.
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.
Synthesis rule: Report each layer separately. Convergence across 3+ layers = high-confidence regulatory variant. A single layer (e.g., eQTL alone) warrants caution.
Phase 0: Disambiguation
MyGene_query_genes: query (string). Converts gene symbols to Ensembl IDs and coordinates. Filter results by symbol == '<GENE>' — first hit may not match.
ensembl_lookup_gene: gene_id (Ensembl ID), species (REQUIRED, "homo_sapiens"). Returns chr/start/end.
GTEx_get_single_tissue_eqtls: gene_symbol. Returns all significant eQTLs across tissues with snpId, pValue, tissueSiteDetailId, nes (normalized effect size).
result = tu.tools.GTEx_get_single_tissue_eqtls(gene_symbol="BRCA1")
from collections import Counter
tissue_counts = Counter(e["tissueSiteDetailId"] for e in result["data"])
GTEx_query_eqtl: gene_symbol, tissue (tissueSiteDetailId), page (1-indexed), size. Use for a specific tissue.
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.
result = tu.tools.GTEx_get_multi_tissue_eqtls(
operation="get_multi_tissue_eqtls",
gencode_id="ENSG00000012048.20"
)
GTEx_calculate_eqtl: operation="calculate_eqtl", gencode_id, variant_id (chr_pos_ref_alt_b38), tissue_site_detail_id. Works for non-significant pairs.
eQTL_list_datasets / eQTL_get_associations: EBI eQTL Catalogue. Use dataset_id (from list call), gene_id (Ensembl), variant. Complementary to GTEx.
Phase 5: Gene Expression Context
GTEx_get_expression_summary: gene_symbol. Recommended — auto-resolves GENCODE versions. Returns median TPM per tissue.
result = tu.tools.GTEx_get_expression_summary(gene_symbol="BRCA1")
top_tissues = sorted(result["data"], key=lambda x: x["median"], reverse=True)[:5]
GTEx_get_median_gene_expression: Requires operation="get_median_gene_expression" + exact versioned gencode_id. Use only when version precision is needed.
GTEx_get_tissue_sites: No params. Returns all tissueSiteDetailId values.
Phase 6: Transcription Factor Binding
jaspar_search_matrices: name (TF name), collection ("CORE"), tax_group ("vertebrates"), species ("9606"), page_size.
result = tu.tools.jaspar_search_matrices(name="CTCF", collection="CORE", page_size=5)
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.
ReMap_get_transcription_factor_binding: gene_name (TF), cell_type, limit. Same tool used for CTCF in Phase 2 — applies to any TF.
STRING_get_functional_annotations: identifiers (gene name), species (9606), category ("Process"/"Function"/"KEGG"). Returns GO/KEGG/Reactome annotations for regulatory context.
Rankings 1a–1f all have eQTL evidence. Rankings 2a–3b have TF binding without eQTL. Rankings 4–7 have decreasing evidence. Use ranking <= 2b as a threshold for "strong regulatory support."
T3 (Moderate): eQTL p < 0.05, JASPAR motif match, multi-tissue m-value > 0.5
T4 (Annotation-based): STRING GO terms, literature references
Convergence 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.
