Disease Research with ENCODE Functional Genomics

When to Use

• User wants to connect GWAS variants to ENCODE regulatory elements for disease mechanism research
• User asks about "disease", "pathology", "therapeutic targets", "GWAS interpretation", or "clinical variants"
• User needs to annotate disease-associated loci with functional genomics data from ENCODE
• User wants to identify drug targets from epigenomic evidence using Open Targets integration
• Example queries: "find enhancers disrupted by diabetes GWAS hits", "identify drug targets from ChIP-seq data", "connect my disease variants to regulatory elements"

Leverage ENCODE's 926,535 cCREs and multi-layer functional data to understand disease mechanisms, interpret disease-associated variants, identify therapeutic targets, and connect genomic findings to clinical applications.

Scientific Rationale

The question: "How can ENCODE functional genomics help me understand a disease's molecular mechanisms and identify actionable targets?"

Over 90% of disease-associated variants from GWAS fall in non-coding regions (Maurano et al. 2012). They disrupt regulatory elements controlling gene expression, not protein sequences. ENCODE provides the most comprehensive catalog of these elements across hundreds of cell types and tissues. This skill connects (1) genetic association data, (2) ENCODE functional annotations, and (3) clinical/pharmacological databases for druggable targets.

Literature Foundation

Step 1: Map Disease to Relevant Tissues

ENCODE regulatory elements are highly tissue-specific. Correct tissue mapping is the single most important decision.

Disease-Tissue Mapping Table

Check ENCODE availability:

encode_get_facets(organ="pancreas")
encode_get_facets(organ="brain")

If tissue has limited data: Use Tier 1 cell lines (K562, GM12878, H1-hESC) or Roadmap Epigenomics as proxies. Document the mismatch explicitly.

Step 2: Find Disease-Relevant ENCODE Data

For GWAS / Genetic Diseases

Open chromatin and active enhancers in disease tissue (Maurano et al. 2012):

encode_search_experiments(assay_title="ATAC-seq", organ="...", biosample_type="tissue")
encode_search_experiments(assay_title="Histone ChIP-seq", target="H3K27ac", organ="...")
encode_search_experiments(assay_title="DNase-seq", organ="...", biosample_type="tissue")

Then use the variant-annotation skill to overlap variants with functional elements.

For Cancer Research

ENCODE cancer cell lines (NOT tumors — see Cancer Epigenomics section below):

encode_search_experiments(biosample_term_name="K562")    # CML
encode_search_experiments(biosample_term_name="HepG2")   # Hepatocellular carcinoma
encode_search_experiments(biosample_term_name="MCF-7")   # ER+ breast cancer
encode_search_experiments(biosample_term_name="A549")     # Lung adenocarcinoma

For Perturbation Studies

encode_search_experiments(perturbed=True, organ="...")
encode_search_experiments(assay_title="CRISPR screen", organ="...")

For Rare Diseases

1. Search for closest available tissue; 2. Check Roadmap Epigenomics; 3. Use Tier 1 cell lines as baseline; 4. Document tissue proxy limitations.

Step 3: Cross-Reference with Disease Databases

PubMed — Disease Literature

search_articles(query="[DISEASE] AND (ENCODE OR regulatory element OR enhancer)")

Track experiments and link papers:

encode_track_experiment(accession="ENCSR...", notes="Disease research - [disease]")
encode_get_citations(accession="ENCSR...")
encode_link_reference(experiment_accession="ENCSR...", reference_type="pmid", reference_id="12345678")

ClinicalTrials.gov — Active Trials

search_trials(condition="[DISEASE]", intervention="[TARGET_GENE or DRUG]", status=["RECRUITING"])

Link trials: encode_link_reference(experiment_accession="ENCSR...", reference_type="nct_id", reference_id="NCT...")

Open Targets — Target-Disease Associations

search_entities(query_strings=["[GENE_NAME]"])
query_open_targets_graphql(
    query_string="query target($ensemblId: String!) { target(ensemblId: $ensemblId) { approvedSymbol knownDrugs { rows { drug { name } phase mechanismOfAction } } } }",
    variables={"ensemblId": "ENSG..."}
)

bioRxiv — Recent Preprints

search_preprints(category="genetics", recent_days=90)
encode_link_reference(experiment_accession="ENCSR...", reference_type="preprint_doi", reference_id="10.1101/...")