Tooluniverse Regulatory Genomics

KEY PRINCIPLES:

1. English-first queries - Use English gene/TF names in all tool calls; respond in user's language
2. Evidence layering - Combine motif (JASPAR) + experimental (ENCODE ChIP-seq) + variant (RegulomeDB) evidence
3. Coordinate precision - Genome coordinates must specify assembly (GRCh38 preferred)
4. Negative results documented - Report when a TF has no ChIP-seq data in ENCODE

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

When to Use

• "What transcription factors bind near gene X?"
• "Does this SNP affect a regulatory element?"
• "Find CTCF binding sites in liver tissue"
• "What are the enhancers active in this cell type?"
• "Show me ChIP-seq experiments for H3K27ac in T cells"
• "Is rs1234567 in a regulatory region?"
• "What TF motifs overlap this genomic region?"
• "Find ENCODE experiments for ATAC-seq in cancer cell lines"

Key Tools

Workflow

Phase 1: TF Motif Discovery (JASPAR)

When asked about TF binding motifs or what TFs might regulate a gene:

1. jaspar_search_matrices(name="TF_NAME", species="Homo sapiens")
   -> Returns list of matrices with matrix_id, collection, version

2. jaspar_get_matrix(matrix_id="MA0139.1")
   -> Returns full PFM/PWM matrix, sequence logo URL, binding sites URL

3. For broad TF family search:
   jaspar_search_matrices(species="Homo sapiens", collection="CORE")
   -> Filter by TF family name in results

JASPAR Collections:

• CORE: High-quality, non-redundant matrices (best for most use cases)
• CNE: Conserved non-coding elements
• POLII: RNA Pol II binding sites

Key Response Fields:

• matrix_id: Versioned ID (e.g., "MA0139.1") — use for jaspar_get_matrix
• name: TF gene symbol
• sequence_logo: URL to binding site logo PNG/SVG
• collection: Which JASPAR collection

Phase 2: ENCODE Experiment Search

When looking for ChIP-seq, ATAC-seq, or other functional genomics data:

For TF ChIP-seq:

ENCODE_search_experiments(
    assay_title="TF ChIP-seq",
    target="CTCF",              # TF gene name
    biosample_term_name="HepG2", # Cell line or tissue
    limit=10
)

For histone marks:

ENCODE_search_histone_experiments(
    histone_mark="H3K27ac",         # or H3K4me3, H3K27me3, H3K36me3
    biosample_term_name="liver",
    limit=10
)

For chromatin accessibility:

ENCODE_search_chromatin_accessibility(
    biosample_term_name="T cell",
    limit=10
)

For regulatory annotations (cCREs, ChromHMM):

ENCODE_search_annotations(
    annotation_type="candidate Cis-Regulatory Elements",
    biosample_term_name="K562",
    limit=10
)

Common assay_title values:

• "TF ChIP-seq" - Transcription factor binding
• "Histone ChIP-seq" - Histone modification
• "ATAC-seq" - Chromatin accessibility
• "DNase-seq" - Open chromatin (older method)
• "WGBS" - DNA methylation

Note: ENCODE_search_experiments returns experiment metadata only (accession, biosample, status). Use ENCODE_get_experiment(accession) to get file download links and detailed metadata.

Phase 3: cCRE Annotation (UCSC + ENCODE)

When annotating a specific genomic region:

UCSC_get_encode_cCREs(
    chrom="chr8",       # Chromosome (GRCh38)
    start=37966000,     # Start coordinate
    end=37967000        # End coordinate
)
# Returns cCREs with type: pELS (proximal enhancer), dELS (distal enhancer),
# PLS (promoter-like), CTCF-only, DNase-H3K4me3

cCRE Types:

• PLS (Promoter-like): High DNase + H3K4me3 + H3K27ac signal near TSS
• pELS (Proximal Enhancer): High DNase + H3K27ac, within 2kb of TSS
• dELS (Distal Enhancer): High DNase + H3K27ac, >2kb from TSS
• CTCF-only: CTCF binding without enhancer marks
• DNase-H3K4me3: Unclassified accessible region

Phase 4: Regulatory Variant Scoring (RegulomeDB)

When assessing regulatory impact of a variant:

RegulomeDB_query_variant(rsid="rs4994")
# Returns:
#   regulome_score.ranking: "1a"-"7" (1a = highest regulatory evidence)
#   regulome_score.probability: 0-1 continuous score
#   tissue_specific_scores: dict of tissue -> score
#   overlapping features: eQTLs, TF binding, DNase peaks, motifs

RegulomeDB Score Interpretation:

Variants with rank 1a-2b are most likely to affect gene regulation.

Tool Parameter Reference

Common Patterns

Pattern 1: TF Binding Site Investigation

Goal: Find where TF X binds and what motif it recognizes
Flow:
  1. jaspar_search_matrices(name="CTCF") -> get matrix_id
  2. jaspar_get_matrix(matrix_id) -> get full PWM, logo URL
  3. ENCODE_search_experiments(assay_title="TF ChIP-seq", target="CTCF") -> experimental binding data
  4. For specific tissue: add biosample_term_name="HepG2"
Output: Motif logo + experimental binding evidence

Pattern 2: Regulatory Variant Interpretation

Goal: Assess if variant rs1234567 affects gene regulation
Flow:
  1. RegulomeDB_query_variant(rsid="rs1234567") -> score + overlapping features
  2. If score <= 2b: ENCODE_search_experiments(target=overlapping_TF) -> experimental evidence
  3. UCSC_get_encode_cCREs(chrom, start, end) -> check if variant in known cCRE
Output: Regulatory score + supporting evidence + cCRE context

Pattern 3: Cell-Type Regulatory Landscape

Goal: Characterize active enhancers in a cell type
Flow:
  1. ENCODE_search_histone_experiments(histone_mark="H3K27ac", biosample_term_name="K562") -> active enhancers
  2. ENCODE_search_chromatin_accessibility(biosample_term_name="K562") -> open chromatin
  3. ENCODE_search_annotations(annotation_type="candidate Cis-Regulatory Elements", biosample_term_name="K562")
  4. ENCODE_get_chromatin_state(biosample_term_name="K562") -> ChromHMM states
Output: Active regulatory elements specific to the cell type

Pattern 4: Gene Regulatory Region Mapping

Goal: Find all regulatory elements near a gene
Flow:
  1. Get gene coordinates from MyGene_query_genes or ensembl_lookup_gene
  2. UCSC_get_encode_cCREs(chrom, start-50000, end+50000) -> nearby cCREs
  3. ENCODE_search_experiments(target=TF_OF_INTEREST) -> TF binding data
  4. jaspar_search_matrices(name=TF_NAME) -> motif for TF
Output: Map of regulatory elements around gene with evidence types

Fallback Strategies

Limitations

• ENCODE TF ChIP-seq: assay_title="TF ChIP-seq" uses ENCODE's exact controlled vocabulary — avoid "ChIP-seq" (too general)
• UCSC cCREs: Coordinates must be in GRCh38 (hg38); liftOver required for hg19 variants
• RegulomeDB: Only scores variants with known rsIDs; novel variants not supported
• JASPAR: Provides motif databases only — not genomic binding locations; combine with ENCODE for experimental evidence
• ENCODE experiment results: The @graph field may be empty if query filters are too restrictive; relax filters and retry