tooluniverse-regulatory-genomics

# 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 | 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 | `tf_name`, `species`, `cell_line`, `collection` (Robust/Permissive) | | `UniBind_get_dataset` | 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". | Tool | Op | Predicts | Context | Access | |------|----|----------|---------|--------| | `AlphaGenome_atlas_lookup_variant` / `AlphaGenome_atlas_scan_interval` | precomputed lookup: one SNV / every SNV in a region | unified AVI_SCORE (AlphaGenome + AlphaMissense) + per-track scores, no live model run | up to 10 kb per scan | hosted API — `ALPHA_GENOME_API_KEY` (much higher query rate than the live ops below) | | `AlphaGenome_predict_interval` / `AlphaGenome_score_variant` / `AlphaGenome_score_ism_variants` | profile region / score variant / saturation-mutagenesis scan | RNA-seq, ATAC, CAGE, splice tracks (frontier accuracy, single-base); ISM ranks every substitution in a ≤500 bp window by effect — good for "which base in this element matters" | up to 1 Mb (ISM window ≤500 bp) | hosted API — `ALPHA_GENOME_API_KEY` | | `run_enformer_predict` / `run_enformer_variant_effect` | profile / score | 5,313 human (+1,643 mouse) tracks: expression, chromatin, TF binding | 196 kb | remote MCP server | | `run_borzoi_predict` / `run_borzoi_variant_effect` | profile / score | RNA-seq coverage (expression / polyA / splicing emphasis), 7,611 tracks | 524 kb | remote MCP server | | `run_chrombpnet_predict` / `run_chrombpnet_variant_effect` | profile / score | chromatin accessibility (ATAC / DNase), base-resolution profile + counts | ~2 kb | remote MCP server | | `Evo2_score_variant` | score | genome-foundation-model delta log-likelihood; coding **and** non-coding | up to 1 Mb | hosted NIM — `NVIDIA_API_KEY` | | `gi_*` (opt-in, `GENOMIC_INTELLIGENCE_MCP_URL`) | profile (6 tasks) + composite find-genes-then-predict | promoter, splice sites, enhancer activity, chromatin state, expression, gene annotation | — | public demo server, shared rate-limited quota, no key | **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**: | Rank | Meaning | |------|---------| | 1a | eQTL + TF binding + matched TF motif + DNase peak | | 1b | eQTL + TF binding + DNase peak | | 1c | eQTL + TF binding or DNase peak | | 1d | eQTL + motif or protein binding | | 1e | eQTL + motif hit | | 1f | eQTL only | | 2a | TF binding + motif match + DNase | | 2b | TF binding + matched motif | | 2c | TF binding with/without motif | | 3a | DNase peak + motif | | 3b | DNase peak only | | 4 | Motif hit only | | 5 | Proximity to Footprint | | 6 | Proximity to Footprint + TF | | 7 | No evidence | Variants with rank 1a-2b are most likely to affect gene regulation. --- ## Tool Parameter Reference | Tool | Required Params | Optional Params | Notes | |------|----------------|-----------------|-------| | `jaspar_search_matrices` | (none — returns all if empty) | `name`, `species`, `collection`, `tax_id`, `page`, `page_size` | Use `name` for TF name search | | `jaspar_get_matrix` | `matrix_id` | — | Full version required: "MA0139.1" not "MA0139" |