Regulatory Genomics Research Skill

Systematic investigation of gene regulation through transcription factor binding, chromatin state, and regulatory element annotation. Integrates JASPAR (TF motifs), ENCODE (functional genomics experiments), RegulomeDB (regulatory variant scoring), and UCSC cCREs.

Domain Reasoning

Regulatory element identification requires converging lines of evidence: sequence conservation alone is insufficient (many conserved sequences are not regulatory), chromatin accessibility is necessary but not sufficient (open chromatin can be structural), TF binding peaks require motif validation, and eQTL evidence ties the element to a transcriptional outcome. No single data type is sufficient. A high-confidence regulatory element requires at least two independent evidence types, and ideally all four.

LOOK UP DON'T GUESS

• TF binding motifs: retrieve from jaspar_search_matrices and jaspar_get_matrix; do not describe motifs from memory.
• Experimental ChIP-seq data: search ENCODE_search_experiments; do not assume a TF has been profiled in a given cell type.
• cCRE annotations for a genomic region: call UCSC_get_encode_cCREs with exact coordinates; do not guess element types.
• Regulatory impact of a variant: query RegulomeDB_query_variant; never estimate regulatory importance from position alone.

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"
• "Predict the effect of this non-coding variant on expression / chromatin accessibility"
• "Predict regulatory activity (expression, accessibility, TF binding) directly from a DNA sequence"
• "Which of these enhancer variants is predicted to be most disruptive?"

Key Tools

Sequence-based deep-learning models (predict, don't just annotate)

The tools above tell you what is known to be at a locus (databases). These models instead predict regulatory activity directly from the DNA sequence, and — by scoring a reference vs. alternate window — predict what a non-coding variant does. RegulomeDB ranks a variant by overlap with existing annotations; these give a quantitative, tissue-aware effect size even for novel variants with no annotation. Reach for them when annotation is silent or when the question is "how much does this allele change regulation".

Picking one: for a known single-nucleotide variant, check AlphaGenome_atlas_lookup_variant (or atlas_scan_interval for a whole region) first — it's a precomputed database read covering all ~9B possible human SNVs, far cheaper than a live call, and its AVI_SCORE already fuses AlphaGenome's regulatory signal with AlphaMissense's coding-impact model into one number. Fall back to the live AlphaGenome_* operations for indels, custom/synthetic sequences, or full per-track detail (predict_variant/predict_sequence/score_interval are also available for raw-track and no-variant use cases); run_enformer_* / run_borzoi_* are the published, self-hostable equivalents (Enformer for general regulation, Borzoi when expression/splicing is the question); run_chrombpnet_* when the question is specifically chromatin accessibility; Evo2_score_variant as a sequence-only check that also covers coding variants. Outputs are Δ (alt − ref) effect sizes, not calibrated probabilities (except AVI_SCORE, which is a unified impact score) — rank/calibrate against known variants. If no key/server is provisioned, fall back to the annotation tools above and say so.

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 1b: Direct TF-DNA Binding Sites (UniBind)

JASPAR gives the motif model; UniBind gives curated, experimentally-derived direct binding sites (motif-anchored, base-pair resolution, per ChIP-seq experiment via the DAMO/ChIP-eat pipeline). Use it to find which experiments support binding for a TF and to get downloadable BED/FASTA of the sites — a middle layer between JASPAR motifs and raw ChIP-seq peaks.

# Find curated binding-site datasets for a TF (filters compose)
tu.run_tool("UniBind_search_datasets",
            {"tf_name": "CTCF", "species": "Homo sapiens",
             "collection": "Robust", "page_size": 5})
#   -> list of {tf_name, total_peaks, dataset_id, dataset_url}

# Full binding-site detail for one dataset
tu.run_tool("UniBind_get_dataset",
            {"dataset_id": "EXP030726.neural_stem_cells.SMAD3"})
#   -> tf_name, cell_line, jaspar_id[], tfbs_models[] each with
#      jaspar_id, total_tfbs, score_threshold, adj_centrimo_pvalue,
#      bed_url, fasta_url, summary_plot_url

# Discover valid tf_name values (search is client-side substring)
tu.run_tool("UniBind_list_tfs", {"search": "SMAD"})   # -> [SMAD2, SMAD3, SMAD4]

Notes: species is the scientific name ('Homo sapiens', not a taxid); collection is 'Robust' (high-confidence) or 'Permissive'; public, no API key.

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