Transcription factor binding, cis-regulatory elements (cCREs), chromatin accessibility, and regulatory annotation using JASPAR (motifs), ENCODE (cCREs, ChIP-seq), RegulomeDB (regulatory variant scoring), UCSC — plus sequence-based deep-learning prediction of regulatory activity and non-coding variant effects (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2). Use for regulatory element annotation, TF-binding-site prediction, regulatory-region functional impact assessment, and predicting how a non-coding variant or a raw DNA sequence affects expression/chromatin/accessibility. Use this whenever a user asks what regulates a gene, whether a SNP hits a regulatory element, or to predict a non-coding variant's functional effect from sequence.
Transcription factor binding, cis-regulatory elements (cCREs), chromatin accessibility, and regulatory annotation using JASPAR (motifs), ENCODE (cCREs, ChIP-seq), RegulomeDB (regulatory variant scoring), UCSC — plus sequence-based deep-learning prediction of regulatory activity and non-coding variant effects (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2). Use for regulatory element annotation, TF-binding-site prediction, regulatory-region functional impact assessment, and predicting how a non-coding variant or a raw DNA sequence affects expression/chromatin/accessibility. Use this whenever a user asks what regulates a gene, whether a SNP hits a regulatory element, or to predict a non-coding variant's functional effect from sequence.
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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 ; do not assume a TF has been profiled in a given cell type.
ENCODE_search_experiments
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:
English-first queries - Use English gene/TF names in all tool calls; respond in user's language
Coordinate precision - Genome coordinates must specify assembly (GRCh38 preferred)
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
Tool
Purpose
Key Params
jaspar_search_matrices
Find TF binding motifs by TF name or organism
name, species, collection, tax_id
jaspar_get_matrix
Get full PWM/PFM for a specific JASPAR matrix
matrix_id (e.g., "MA0139.1")
JASPAR_get_transcription_factors
List all TF matrices (paginated)
page, page_size
UniBind_search_datasets
Find curated, experimentally-derived direct TF-DNA binding-site datasets
Get binding-site detail for one UniBind dataset (JASPAR motifs, score/CentriMo thresholds, BED/FASTA URLs)
dataset_id
UniBind_list_tfs
List/filter TFs profiled in UniBind
search (substring), limit
ENCODE_search_experiments
Search ENCODE ChIP-seq/ATAC-seq/WGBS experiments
assay_title, target, biosample_term_name, limit
ENCODE_search_histone_experiments
Search histone mark ChIP-seq specifically
histone_mark, biosample_term_name, limit
ENCODE_search_chromatin_accessibility
Search ATAC-seq/DNase-seq experiments
biosample_term_name, limit
ENCODE_get_experiment
Get full metadata for a specific ENCODE experiment
accession (e.g., "ENCSR000EGM")
ENCODE_search_annotations
Search ENCODE cCRE and chromatin state annotations
annotation_type, biosample_term_name, limit
ENCODE_get_chromatin_state
Search ChromHMM segmentation data
biosample_term_name, limit
UCSC_get_encode_cCREs
Get cCREs overlapping a genomic region
chrom, start, end
RegulomeDB_query_variant
Score regulatory impact of a variant
rsid (e.g., "rs4994")
ENCODE_search_biosamples
Find available cell lines/tissues in ENCODE
term_name, biosample_type, limit
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".
genome-foundation-model delta log-likelihood; coding and non-coding
up to 1 Mb
hosted NIM — NVIDIA_API_KEY
Picking one:AlphaGenome_* is the broadest readout + longest context when its key is set; 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 — 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
)
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
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
Primary Tool
Fallback
When
ENCODE_search_experiments with specific biosample
Remove biosample_term_name filter
No results for specific tissue
jaspar_search_matrices(name=TF)
jaspar_search_matrices(name=TF_family)
TF not found by exact name
UCSC_get_encode_cCREs
ENCODE_search_annotations without coordinates
If coordinates unknown
RegulomeDB_query_variant(rsid)
Use ENCODE_search_experiments + JASPAR to manually assess overlap
