tooluniverse-regulatory-genomics - Claude MCP Skill

# 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` |
| `EnsemblReg_get_motif_features` | TF binding motif instances in a region (Ensembl Regulatory Build) | `region`, `species` | motif instances with `binding_matrix_stable_id`, `transcription_factor_complex`, `score` |
| `EnsemblReg_get_constrained_elements` | Evolutionarily constrained (purifying-selection) elements in a region — the conservation evidence type called out below | `region`, `species` | `constrained_elements[]` with `score`, `start`/`end` |
| `EnsemblReg_get_binding_matrix` | Full PWM/PFM for an Ensembl binding-matrix stable ID | `binding_matrix_id` (e.g. `ENSPFM0320`) | nucleotide-frequency matrix, `associated_tfs[]`, `threshold` |
| `MEME_fimo_scan` / `MEME_discover_motifs` / `MEME_tomtom_compare` / `MEME_list_databases` | Scan sequences for known motifs / discover novel motifs de novo / compare a motif against JASPAR-HOCOMOCO-CIS-BP / list available comparison databases | `sequences`, `motif_text` (MEME format) | Submits a job to meme-suite.org; 10-120s depending on op |
| `HOCOMOCO_search_motifs` / `HOCOMOCO_get_motif` | Curated ChIP-seq-derived TF motif by gene name / full motif detail + optional PWM | `query` (gene name) / `motif_id` | Human + mouse only; complements JASPAR, not a replacement |
| `Dfam_search_families` / `Dfam_get_family` / `Dfam_get_annotations` | Find transposable-element/repeat families / get one family's detail / get repeat annotations for a genomic region | `name_prefix`/`clade`/`repeat_type` / `accession` / `chrom`,`start`,`end` | Repeats can carry their own regulatory elements (e.g. SVA/Alu insertions) |

### 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 1c: Ensembl Regulatory Build (motifs + the missing conservation evidence type)

The Domain Reasoning above lists four converging evidence types for a
high-confidence regulatory element — conservation, accessibility, TF
binding, eQTL — but conservation had no tool until now.
`EnsemblReg_get_constrained_elements` fills that gap directly: it returns
evolutionarily constrained (purifying-selection) elements in a region,
independent of JASPAR/UniBind/ENCODE's binding-evidence tools. Combine it
with `EnsemblReg_get_motif_features` (a second, database-scale source of TF
motif instances, complementary to JASPAR/UniBind) to build the
two-or-more-evidence-types case this skill's reasoning already calls for.

```
# TF binding motif instances in a region (species defaults to homo_sapiens)
tu.run_tool("EnsemblReg_get_motif_features", {"region": "7:140424943-140524564"})
#   -> motif_count, motif_features[] each with stable_id,
#      transcription_factor_complex, binding_matrix_stable_id, score,
#      start/end/strand

# Evolutionary conservation evidence for the same region
tu.run_tool("EnsemblReg_get_constrained_elements", {"region": "17:7661779-7687538"})
#   -> element_count, constrained_elements[] each with score (higher =
#      stronger conservation), start/end

# Full PWM for a binding matrix found above (e.g. from motif_features)
tu.run_tool("EnsemblReg_get_binding_matrix", {"binding_matrix_id": "ENSPFM0320"})
#   -> associated_tfs[], nucleotide-frequency matrix, threshold, consensus
```

**Operational notes (live-verified, not assumed)**: these three endpoints
are slow — `get_motif_features` typically takes 10-25s, `get_binding_matrix`
50-60s, `get_constrained_elements` up to 25s for a small region. Use a
generous timeout (60-90s) rather than treating a slow response as a hang.
`get_constrained_elements` is also unreliable on larger regions (e.g. the
~100kb BRAF locus reproducibly failed or timed out on repeated attempts
while the smaller TP53 region succeeded) — prefer regions well under 50kb
for this specific endpoint, and retry once before concluding the call
failed.

### Phase 1d: TF Motif Discovery and Comparison (MEME Suite) + Curated Reference Motifs (HOCOMOCO)

JASPAR gives a curated motif *library*; the MEME Suite tools instead let you
*discover* a motif de novo from a set of related sequences and *compare* any
motif (discovered or known) against reference databases. HOCOMOCO is a
second curated reference library (ChIP-seq-derived, human/mouse only) —
complementary to JASPAR, not a replacement, since the two are built from
different underlying peak sets and sometimes disagree on model quality for
the same TF.

```
# 1. Scan sequences for known motif occurrences (submits a job to
#    meme-suite.org, polls ~10-30s)
tu.run_tool("MEME_fimo_scan", {
    "sequences": ">promoter\nGGATCCGCGCGCTATAAAAGGATCC...",
    "motif_text": "MEME version 5\n\nALPHABET= ACGT\n...",  # full MEME-format motif
    "pvalue_threshold": 0.0001
})
#   -> hits[] with start/stop/strand/score/pvalue/qvalue/matched_sequence

# 2. De novo motif discovery across >=2 related sequences (~30-120s)
tu.run_tool("MEME_discover_motifs", {
    "sequences": ">seq1\n...\n>seq2\n...",  # co-regulated gene promoters, etc.
    "nmotifs": 3
})
#   -> motifs[] with consensus, width, sites, evalue, probability_matrix

# 3. Identify which known TF a discovered (or any) motif resembles
#    (~5-20s)
tu.run_tool("MEME_tomtom_compare", {
    "query_motif": "MEME version 5\n...",
    "target_db": "JASPAR2026_vertebrates"  # or "HOCOMOCO_v12"; see list_databases
})
#   -> matches[] ranked by evalue, with target_id (e.g. JASPAR MA0106.3)

# 4. See available target databases for tomtom_compare (no remote call,
#    cached metadata)
tu.run_tool("MEME_list_databases", {"category_filter": "JASPAR"})
```

**HOCOMOCO as a curated cross-check** — given a TF name, get its curated
consensus/PWM/quality grade directly, no discovery step needed:

```
tu.run_tool("HOCOMOCO_search_motifs", {"query": "CTCF"})
#   -> [{motif_id: "CTCF.H14CORE.0.P.B", gene_name_human, quality, consensus, ...}]
tu.run_tool("HOCOMOCO_get_motif", {"motif_id": "CTCF.H14CORE.0.P.B", "include_pwm": true})
#   -> real verified response: consensus "hbRCCRShAGRKGGCGShvn", quality "B",
#      tfclass (superclass/class/family/subfamily), uniprot_ac_human "P49711",
#      pwm (if include_pwm=true)
```

Quality grades run A (best) to D (lowest); data_sources "P" = ChIP-Seq
peaks, "S" = HT-SELEX. Use HOCOMOCO's quality grade to sanity-check a
JASPAR/MEME-discovered motif for the same TF — a low grade or missing entry
is itself informative (that TF's binding may be less well characterized).

### Repeat Elements (Dfam)

Transposable elements and other repeats can create, destroy, or shift
regulatory elements (SVA/Alu insertions carrying their own TF binding
sites is a well-known mechanism) — a fifth context worth checking alongside
the four evidence types in Domain Reasoning above when a regulatory element
sits inside or near a repeat-masked region.

```
tu.run_tool("Dfam_search_families", {"name_prefix": "AluS"})
#   -> [{accession: "DF000000003", name: "AluSc", repeat_type: "SINE", ...}]
tu.run_tool("Dfam_get_family", {"accession": "DF000000003"})
#   -> consensus_sequence, classification, clades[], citations[]

# Real example: TP53 locus (chr17:7,661,779-7,687,538, GRCh38) — the same
# region used in the Ensembl Regulatory Build section above — carries two
# real SVA retroposon insertions (SVA_E, SVA_A) and an AluSc SINE:
tu.run_tool("Dfam_get_annotations", {
    "assembly": "hg38", "chrom": "chr17", "start": 7661779, "end": 7687538
})
#   -> [{accession: "DF000001071", query: "SVA_E", type: "Retroposon",
#        bit_score: 180, e_value: "1.3e-50", seq_start: 7661463, ...}, ...]
```

`nrph=true` (default) returns non-redundant profile hits only; set `false`
for every raw hit including overlapping/lower-scoring alignments.

### 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" |
| `JASPAR_get_transcription_factors` | (none) | `page`, `page_size` | Paginated; default page_size=10 |
| `jaspar_get_matrix_versions` | `base_id` | — | base_id is unversioned (e.g., "MA0139") |
| `ENCODE_search_experiments` | (none — returns all if empty) | `assay_title`, `target`, `biosample_term_name`, `limit` | assay_title must match ENCODE vocabulary exactly |
| `ENCODE_search_histone_experiments` | (none) | `histone_mark`, `biosample_term_name`, `limit` | histone_mark: "H3K27ac", "H3K4me3", etc. |
| `ENCODE_search_chromatin_accessibility` | (none) | `biosample_term_name`, `limit` | Returns ATAC-seq and DNase-seq |
| `ENCODE_get_experiment` | `accession` | — | accession: "ENCSR..." format |
| `ENCODE_search_annotations` | (none) | `annotation_type`, `biosample_term_name`, `limit` | annotation_type: "candidate Cis-Regulatory Elements" |
| `ENCODE_get_chromatin_state` | (none) | `biosample_term_name`, `limit` | Returns ChromHMM segmentation |
| `ENCODE_search_biosamples` | (none) | `term_name`, `biosample_type`, `limit` | biosample_type: "cell line", "tissue", "primary cell" |
| `UCSC_get_encode_cCREs` | `chrom`, `start`, `end` | — | Coordinates in GRCh38; chrom format: "chr1" |
| `RegulomeDB_query_variant` | `rsid` | — | rsid format: "rs4994" (with rs prefix) |

---

## 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

| 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 | rsid not in RegulomeDB |

---

## 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