Search ENCODE Data

When to Use

• User wants to find ENCODE experiments matching specific criteria (assay, organ, cell type, target)
• User asks "what ENCODE data exists for [tissue/target/assay]?"
• User wants to explore available data before downloading
• User needs to find specific file types (BED, BAM, bigWig) across experiments
• User wants to know how many experiments exist for a condition
• User asks about available assays, organisms, or biosamples in ENCODE

Help the user find ENCODE experiments and files. Use the appropriate tools based on what they need.

Search Strategy

1. Finding experiments: Use encode_search_experiments with filters:

   • assay_title: "Histone ChIP-seq", "ATAC-seq", "RNA-seq", "TF ChIP-seq", "Hi-C", "CUT&RUN", "WGBS", etc.
   • organ: "pancreas", "brain", "liver", "heart", "kidney", "lung", etc.
   • biosample_type: "tissue", "cell line", "primary cell", "organoid"
   • biosample_term_name: specific name like "GM12878", "HepG2", "K562"
   • target: ChIP/CUT&RUN target like "H3K27me3", "H3K4me3", "CTCF", "p300"
   • organism: "Homo sapiens" (default) or "Mus musculus"

2. Finding files across experiments: Use encode_search_files when the user wants specific file types from multiple experiments.

3. Exploring available data: Use encode_get_facets to see counts of what exists before searching. Use encode_get_metadata to list valid filter values.

4. Getting experiment details: Use encode_get_experiment for full metadata on a single experiment. Use encode_list_files to see all files for one experiment.

Search Strategy Guide

Effective ENCODE searching follows a three-phase pattern: explore, search, refine. Jumping straight to a filtered search often produces empty results or misses relevant data.

Phase 1: Explore with Facets

Always start with encode_get_facets to understand what data exists. Facets return counts per filter value, so you can see immediately whether your target organ, assay, or biosample has data.

encode_get_facets(organ="pancreas")
  -> Shows: Histone ChIP-seq (42), ATAC-seq (8), RNA-seq (15), TF ChIP-seq (6), ...
  -> Also shows: biosample types, life stages, labs, replication types

This avoids the frustrating pattern of searching for data that does not exist. Facets may also reveal data you did not expect -- for example, CUT&RUN data where you only anticipated ChIP-seq, or organoid samples alongside tissue.

Phase 2: Validate Filter Values

Before searching, confirm that your filter values match ENCODE's controlled vocabulary. A mistyped assay name returns zero results with no error.

encode_get_metadata(metadata_type="assays")
  -> Returns all valid assay_title values: "Histone ChIP-seq", "TF ChIP-seq", "ATAC-seq", ...

Available metadata types: assays, organisms, organs, biosample_types, file_formats, output_types, output_categories, assemblies, life_stages, replication_types, statuses, file_statuses.

Phase 3: Search and Refine

Start with broad filters and add constraints one at a time. If a search returns too many results (>100), add a filter. If it returns zero, remove the most restrictive filter first.

# Too broad: 2,400 results
encode_search_experiments(assay_title="Histone ChIP-seq")

# Add organ: 42 results
encode_search_experiments(assay_title="Histone ChIP-seq", organ="pancreas")

# Add target: 6 results
encode_search_experiments(assay_title="Histone ChIP-seq", organ="pancreas", target="H3K27ac")

Pitfalls & Edge Cases

1. Wrong assay_title values: Assay names must match ENCODE's controlled vocabulary exactly. Run encode_get_metadata(metadata_type="assays") first to discover valid values. For example, use "Histone ChIP-seq" not "ChIP-seq" or "H3K27ac ChIP".
2. Confusing biosample_term_name vs organ: organ is a broad anatomical system (e.g., "pancreas", "brain"). biosample_term_name is a specific cell or tissue name (e.g., "GM12878", "islet of Langerhans"). Use organ for tissue-level exploration, biosample_term_name when you know the exact biosample.
3. Not exploring first: Always call encode_get_facets before searching to see what data exists. This avoids empty results and reveals unexpected data availability. For example, facets may show CUT&RUN data exists for your organ when you only expected ChIP-seq.
4. Mixing organisms: Human and mouse experiments use different assemblies (GRCh38 vs mm10) and cannot be directly compared. Always filter by organism to avoid mixing species in results.
5. Expecting file-level results from experiment search: encode_search_experiments returns experiments, not individual files. If the user wants specific BED or bigWig files, use encode_search_files instead with file_format and output_type filters.
6. Searching for deprecated data: The default status="released" is correct for most use cases. Archived or revoked experiments may have known quality issues. Only change status if the user explicitly needs historical data.

Gotchas

organ vs biosample_term_name vs biosample_type

These three filters address different levels of the biosample hierarchy. Using the wrong one produces unexpected results.

Common mistake: using biosample_term_name="pancreas" when you mean organ="pancreas". The term name "pancreas" matches whole-pancreas tissue samples only, missing islets, acinar cells, and other pancreatic substructures that are classified under the pancreas organ.

assay_title Must Match Exactly

ENCODE uses a controlled vocabulary for assay names. Common mistakes:

Always run encode_get_metadata(metadata_type="assays") to see valid values.

target Names Are Case-Sensitive

Histone mark targets use a specific capitalization pattern. Common mistakes:

Pattern: H3K{number}{modification} where the modification is lowercase ("me3", "ac", "me1"). Transcription factor targets use all-uppercase names ("CTCF", "POLR2A", "EP300").

Experiment Status Meanings

Common Filter Combinations

Ready-to-use filter combinations for common research questions: