curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gds&db=sra&id=GDS_UID&tool=encode_mcp&email=YOUR_EMAIL"

Step 1: Track the experiment to extract publications
  encode_track_experiment(accession="ENCSR133RZO", fetch_publications=True)
  -> Extracts PMIDs from experiment metadata
 
Step 2: Get the PMIDs
  encode_get_references(experiment_accession="ENCSR133RZO", reference_type="pmid")
  -> Returns: {"references": [{"id": 3, "experiment_accession": "ENCSR133RZO",
       "reference_type": "pmid", "reference_id": "32728249",
       "description": "Auto-extracted from ENCODE dbxrefs", "linked_at": 1739452800.22}],
       "count": 1}   (linked_at is epoch seconds)
 
Step 3: Fetch full article metadata from PubMed
  get_article_metadata(pmids=["32728249"])
  -> Returns title, authors, journal, abstract, DOI
 
Step 4: Find related papers
  find_related_articles(pmids=["32728249"], link_type="pubmed_pubmed", max_results=10)
  -> Returns similar articles for further reading

Step 1: Get experiment details
  encode_get_experiment(accession="ENCSR000AKS")
  -> Note the target (e.g., H3K27me3), biosample_summary, and description
  -> (the response carries no DOIs; use encode_get_references for linked identifiers)
 
Step 2: Search bioRxiv for related preprints
  search_preprints(category="genomics", recent_days=90, limit=20)
  -> Review abstracts for mentions of the target or ENCODE accession
 
Step 3: Link discovered preprint
  encode_link_reference(
    experiment_accession="ENCSR000AKS",
    reference_type="preprint_doi",
    reference_id="10.1101/2024.06.15.598765",
    description="Preprint analyzing H3K27me3 patterns in same tissue"
  )

Step 1: Check experiment metadata for GEO cross-references
  encode_search_experiments(search_term="ENCSR133RZO")
  -> Look in the result's dbxrefs for "GEO:GSExxxxxx"
     (dbxrefs is a search-result field; encode_get_experiment does not return it)
 
Step 2: Link the GEO accession
  encode_link_reference(
    experiment_accession="ENCSR133RZO",
    reference_type="geo_accession",
    reference_id="GSE125066",
    description="GEO submission with same raw data and supplementary files"
  )

Step 1: Get the target from an ENCODE experiment
  encode_get_experiment(accession="ENCSR...")
  -> Target: "TP53" (a TF ChIP-seq experiment)
 
Step 2: Search ClinicalTrials.gov for trials involving the target
  search_trials(condition="cancer", intervention="TP53", status=["RECRUITING"])
  -> Returns active trials targeting TP53
 
Step 3: Link a relevant trial
  encode_link_reference(
    experiment_accession="ENCSR...",
    reference_type="nct_id",
    reference_id="NCT04567890",
    description="Phase 2 trial targeting TP53 in solid tumors"
  )

Step 1: Find ENCODE experiments for a transcription factor
  encode_search_experiments(
    assay_title="TF ChIP-seq",
    target="CTCF",
    organism="Homo sapiens"
  )
  -> Returns experiments where CTCF binding is mapped genome-wide
  -> Note the accessions, biosamples, and labs
 
Step 2: Look up CTCF in Open Targets
  search_entities(query_strings=["CTCF"])
  -> Returns: {"id": "ENSG00000102974", "entity": "target"}
  -> This Ensembl Gene ID is required for all Open Targets GraphQL queries
 
Step 3: Query Open Targets for disease associations
  query_open_targets_graphql(
    query_string='query { target(ensemblId: "ENSG00000102974") {
      approvedName
      approvedSymbol
      associatedDiseases(page: {index: 0, size: 10}) {
        rows {
          disease { id name }
          score
          datasourceScores { componentId score }
        }
      }
    } }'
  )
  -> Returns diseases ranked by association score
  -> CTCF disruption is linked to various cancers and developmental disorders
  -> The datasourceScores breakdown shows which evidence types drive the association
 
Step 4: Query for drugs targeting CTCF-regulated pathways
  query_open_targets_graphql(
    query_string='query { target(ensemblId: "ENSG00000102974") {
      knownDrugs(size: 10) {
        rows { drug { name mechanismOfAction } disease { name } phase status }
      }
      tractability { label modality value }
    } }'
  )
  -> Tractability scores indicate whether the target is druggable
  -> knownDrugs lists any compounds in clinical development
  -> Note: many TFs have low tractability -- pathway-level interventions may be more relevant
 
Step 5: Find and link relevant clinical trials
  search_trials(condition="cancer", intervention="CTCF", status=["RECRUITING"])
  -> If no direct CTCF trials, broaden to pathway:
  search_trials(condition="cancer", intervention="chromatin remodeling", status=["RECRUITING"])
  -> Link the most relevant trial:
  encode_link_reference(
    experiment_accession="ENCSR...",
    reference_type="nct_id",
    reference_id="NCT...",
    description="Trial targeting chromatin regulation pathway relevant to CTCF"
  )

Step 1: Find ENCODE H3K27ac experiments in liver
  encode_search_experiments(
    assay_title="Histone ChIP-seq",
    target="H3K27ac",
    organ="liver",
    organism="Homo sapiens"
  )
  -> Returns liver H3K27ac experiments
  -> Prefer experiments with status="released" and audit_error_count of 0
 
Step 2: Download the peak file
  encode_list_files(
    experiment_accession="ENCSR...",
    file_format="bed",
    output_type="IDR thresholded peaks",
    assembly="GRCh38"
  )
  -> Select the preferred_default file if available
  -> Download with encode_download_files for local analysis
 
Step 3: Annotate peaks with nearest genes
  -> Use the peak-annotation skill or run:
  bedtools closest -a peaks.bed -b gencode.v43.annotation.bed -d
  -> For each peak, record the nearest gene name, Ensembl ID, and distance
  -> Peaks overlapping a gene promoter (within 2 kb of TSS) are most interpretable
 
Step 4: Check expression of nearby genes in GTEx liver
  -> Use the gtex-expression skill:
  -> GTEx REST API: GET /expression/medianTranscriptExpression
     ?gencodeId=ENSG00000...&tissueSiteDetailId=Liver
  -> For each annotated gene, retrieve median TPM in liver
  -> Classification:
     TPM > 10   = highly expressed (strong evidence the enhancer is active)
     TPM 1-10   = moderately expressed
     TPM < 1    = low/absent expression in bulk tissue
     TPM = 0    = gene is not expressed in this tissue context
 
Step 5: Validate and interpret
  -> High H3K27ac + high GTEx expression = strong evidence for active enhancer
     driving the nearest gene in liver
  -> High H3K27ac + low GTEx expression = possible enhancer for a rare cell type
     within the liver (e.g., cholangiocytes, stellate cells, immune infiltrates)
     Consider single-cell data from CellxGene to resolve cell-type specificity
  -> Low H3K27ac + high GTEx expression = gene is transcribed but this specific
     enhancer may not be the primary driver; check for other active enhancers nearby

Step 1: Get variants from the GWAS Catalog
  -> Use the gwas-catalog skill to find variants for a trait
  -> Example: type 2 diabetes (EFO_0001360)
  -> Retrieve lead SNPs with their coordinates (chr, pos, ref, alt)
  -> Note the reported p-value, odds ratio, and mapped genes
  -> Key variants for T2D: rs7903146 (TCF7L2), rs1801282 (PPARG), rs5219 (KCNJ11)
 
Step 2: Check ClinVar for clinical significance
  -> Use the clinvar-annotation skill: query ClinVar API with rsID or coordinates
  -> Classifications to note:
     Pathogenic / Likely pathogenic = strong clinical evidence
     Risk factor = population-level association (common for GWAS hits)
     Uncertain significance (VUS) = insufficient evidence to classify
     Benign / Likely benign = not disease-causing
  -> ClinVar also provides the condition and review status (star rating)
 
Step 3: Find ENCODE data overlapping the variant position
  encode_search_experiments(
    assay_title="Histone ChIP-seq",
    organ="pancreas",
    organism="Homo sapiens"
  )
  -> Download relevant peak files:
  encode_search_files(
    file_format="bed",
    output_type="IDR thresholded peaks",
    assembly="GRCh38"
  )
  -> Check if the variant coordinates fall within any peak
  -> Overlap with H3K27ac = active enhancer; H3K4me1 = poised/active enhancer;
     H3K4me3 = promoter; H3K27me3 = repressed
 
Step 4: Check ENCODE cCREs via UCSC
  -> Use the ucsc-browser skill to query ENCODE cCRE tracks at the variant position
  -> UCSC REST API: GET /getData/track?genome=hg38&track=encodeCcreCombined
     &chrom=chr10&start=112998580&end=112998600
  -> Determine the cCRE classification:
     PLS = promoter-like signature (high H3K4me3, high DNase)
     pELS = proximal enhancer-like signature (high H3K27ac, within 2 kb of TSS)
     dELS = distal enhancer-like signature (high H3K27ac, distal from TSS)
     CTCF-only = CTCF binding without enhancer/promoter marks
     DNase-H3K4me3 = DNase signal with H3K4me3 but not matching PLS criteria
 
Step 5: Link everything back to the tracked experiment
  encode_link_reference(
    experiment_accession="ENCSR...",
    reference_type="other",
    reference_id="rs7903146",
    description="T2D GWAS lead SNP in TCF7L2 locus, overlapping H3K27ac peak in pancreatic islets"
  )
  -> Also link the ClinVar accession:
  encode_link_reference(
    experiment_accession="ENCSR...",
    reference_type="other",
    reference_id="RCV000012345",
    description="ClinVar: risk factor for type 2 diabetes"
  )

Step 1: Search Consensus for systematic reviews
  search(
    query="ATAC-seq chromatin accessibility systematic review",
    year_min=2020,
    sjr_max=2
  )
  -> Returns high-quality reviews of ATAC-seq methodology from Q1/Q2 journals
  -> Look for papers with high citation counts as authoritative references
 
Step 2: Find papers that cite ENCODE ATAC-seq data specifically
  search(
    query="ENCODE project ATAC-seq chromatin accessibility analysis pipeline"
  )
  -> Identifies how the community uses ENCODE accessibility data
  -> Note which analysis tools and pipelines are most commonly applied
 
Step 3: Search for benchmarking studies comparing methods
  search(
    query="ATAC-seq peak calling comparison benchmark ENCODE",
    year_min=2021,
    sjr_max=1
  )
  -> Finds methodological comparisons useful for justifying analysis choices
  -> These papers often include ENCODE data as ground truth or benchmarks
 
Step 4: Link key papers to tracked experiments
  -> Extract PMIDs from the Consensus results
  -> For each relevant paper:
  encode_link_reference(
    experiment_accession="ENCSR...",
    reference_type="pmid",
    reference_id="...",
    description="Methodological reference for ATAC-seq analysis"
  )
 
Step 5: Export a complete bibliography
  encode_get_citations(
    accession="ENCSR...",
    export_format="bibtex"
  )
  -> Generates a BibTeX file with all linked references
  -> Import directly into reference managers (Zotero, Mendeley, EndNote)

Step 1: Start with a GWAS hit
  -> GWAS Catalog: rs7903146 (TCF7L2 locus)
  -> This is the strongest known common variant association with type 2 diabetes
  -> Coordinates: chr10:112998590 (GRCh38)
  -> Risk allele: T, odds ratio ~1.4, p-value < 1e-200
 
Step 2: Check ENCODE regulatory landscape at the locus
  encode_search_experiments(
    assay_title="Histone ChIP-seq",
    organ="pancreas",
    organism="Homo sapiens"
  )
  -> Find H3K27ac and H3K4me1 experiments covering the TCF7L2 locus
  -> Also search for ATAC-seq / DNase-seq for chromatin accessibility:
  encode_search_experiments(
    assay_title="ATAC-seq",
    organ="pancreas",
    organism="Homo sapiens"
  )
  -> Download peak files and check if rs7903146 falls in:
     (a) an active enhancer (H3K27ac peak)
     (b) an accessible chromatin region (ATAC-seq peak)
     (c) a CTCF binding site (CTCF ChIP-seq peak)
  -> The variant falls in an islet-specific enhancer marked by H3K27ac and H3K4me1
 
Step 3: Identify the target gene
  -> Peak annotation: which gene does this enhancer regulate?
  -> TCF7L2 is the nearest protein-coding gene -- a WNT pathway transcription factor
  -> Check GTEx for expression:
     GTEx API: tissueSiteDetailId=Pancreas, gencodeId=ENSG00000148737
  -> TCF7L2 is expressed in pancreatic islets (median TPM > 5)
  -> Also check expression in other T2D-relevant tissues: adipose, liver, skeletal muscle
  -> TCF7L2 is broadly expressed but has islet-specific regulatory patterns
 
Step 4: Assess population frequency and clinical significance
  -> gnomAD (via gnomad-variants skill):
     rs7903146 T allele frequency: ~30% in European populations, ~25% global
     This is a common variant, consistent with a GWAS hit
  -> ClinVar (via clinvar-annotation skill):
     rs7903146 is classified as "risk factor" for type 2 diabetes
     Review status: multiple submitters with criteria provided
  -> Also check Ensembl VEP for predicted functional consequences:
     Ensembl REST: GET /vep/human/id/rs7903146
     -> Regulatory region variant, falls in ENSR00000... (Ensembl Regulatory Build)
 
Step 5: Evaluate as a drug target via Open Targets
  search_entities(query_strings=["TCF7L2"])
  -> Returns: {"id": "ENSG00000148737", "entity": "target"}
 
  query_open_targets_graphql(
    query_string='query { target(ensemblId: "ENSG00000148737") {
      approvedName approvedSymbol
      tractability { label modality value }
      associatedDiseases(page: {index: 0, size: 5}) {
        rows { disease { name } score }
      }
      knownDrugs(size: 10) {
        rows { drug { name } disease { name } phase status }
      }
    } }'
  )
  -> Check tractability: is TCF7L2 druggable with small molecules, antibodies, or other modalities?
  -> Review disease associations: T2D should appear as a top association
  -> If TCF7L2 is not directly druggable, consider WNT pathway targets instead:
  search_entities(query_strings=["WNT3A", "CTNNB1", "GSK3B"])
  -> Query druggability for upstream/downstream WNT pathway members
 
Step 6: Find active clinical trials
  search_trials(
    condition="type 2 diabetes",
    intervention="WNT",
    status=["RECRUITING"]
  )
  -> Also search broader:
  search_trials(
    condition="type 2 diabetes",
    phase=["PHASE2", "PHASE3"],
    status=["RECRUITING"]
  )
  -> Look for trials targeting pathways regulated by TCF7L2
  -> Link the most relevant trial:
  encode_link_reference(
    experiment_accession="ENCSR...",
    reference_type="nct_id",
    reference_id="NCT...",
    description="Phase 3 T2D trial targeting WNT/TCF7L2 regulatory pathway"
  )
 
Step 7: Document the full chain with provenance
  -> Track all ENCODE experiments used:
  encode_track_experiment(accession="ENCSR...", fetch_publications=True)
  -> Link all references:
  encode_link_reference(experiment_accession="ENCSR...", reference_type="other", reference_id="rs7903146",
    description="GWAS lead SNP, TCF7L2 locus, T2D")
  encode_link_reference(experiment_accession="ENCSR...", reference_type="pmid", reference_id="...",
    description="Original GWAS discovery paper")
  encode_link_reference(experiment_accession="ENCSR...", reference_type="nct_id", reference_id="NCT...",
    description="Relevant clinical trial")
  -> Log any derived analysis files:
  encode_log_derived_file(
    source_accessions=["ENCSR..."],
    description="Intersection of rs7903146 with H3K27ac peaks in pancreatic islets",
    file_path="/path/to/variant_enhancer_overlap.bed"
  )
  -> Export the complete reference list:
  encode_get_citations(accession="ENCSR...", export_format="bibtex")