Encode Ccres Database

ENCODE Database Skill

This skill allows you to query the ENCODE Registry of cCREs (candidate cis-Regulatory Elements) via the SCREEN GraphQL API. It helps identify functional non-coding DNA elements (like Promoters, Enhancers, and insulators) by analyzing biochemical signatures (DNase, H3K4me3, H3K27ac, CTCF).

Prerequisites

1. uv: Read the uv skill and follow its Setup instructions to ensure

uv is installed and on PATH.

1. User Notification: If LICENSE_NOTIFICATION.txt does not already exist in

this skill directory then (1) prominently notify the user to check the terms at https://www.encodeproject.org/help/rest-api/, then (2) create the file recording the notification text and timestamp.

Core Rules

• Use the Wrapper: ALWAYS execute the provided helper scripts to query the

database rather than accessing the database directly. The scripts automatically enforce the required rate limit gracefully.

• Parsing Output: Do NOT use cat to read the entire JSON output file

into context, as it can be extremely large. You MUST use jq to efficiently parse and extract relevant fields.

• Notification: If this skill is used, ensure this is mentioned in the

output.

Quick Start

# Search cCREs by coordinates
uv run scripts/screen_api.py search --chromosome chr11 \
  --start 5205263 --end 5207263 \
  --output /tmp/search.json

# Get details for a specific cCRE
uv run scripts/screen_api.py details EH38E2941922 \
  --output /tmp/details.json

All subcommands write JSON to disk. Always save output in a temporary location like /tmp/.

Identifying High-Confidence ("Type A") Biosamples

Biosamples in ENCODE are often categorized by their data completeness. "Type A" (or high-confidence) biosamples are those that have experimental data for all four core epigenetic markers: DNase, H3K4me3, H3K27ac, and CTCF.

The biosamples and details commands automatically enrich their output with an is_type_a boolean flag for each biosample.

Example: Finding high-confidence cell types

uv run scripts/screen_api.py biosamples --output /tmp/biosamples.json
# Use jq to filter for Type A biosamples
jq '.data.ccREBiosampleQuery.biosamples[] | select(.is_type_a == true) | .displayname' /tmp/biosamples.json

Parsing Output (CRITICAL)

Do NOT use cat to read the entire JSON output file into context, as it can be extremely large. Instead, you MUST use jq to efficiently parse and extract the relevant fields from the JSON file saved by the script. If jq is not available on the system, write your own Python filtering code (e.g., python3 -c "import json...") to extract the necessary data.

For a complete reference of the JSON structure returned by eachmcommand (so you know which fields to query with jq), read references/json_output_structure.md.

Available Commands

• search: Search cCREs by coordinates, accessions, or epigenetic signals.

``bash uv run scripts/screen_api.py search \ --chromosome chr11 --start 5205263 --end 5207263 \ --output /tmp/search.json ``

• nearby-genes: Find nearby genes for given cCRE accessions.

``bash uv run scripts/screen_api.py nearby-genes \ EH38E1516972 --output /tmp/nearby.json ``

• details: Get detailed information and biosample-specific max Z-scores for

a specific cCRE.

``bash uv run scripts/screen_api.py details EH38E2941922 \ --output /tmp/details.json ``

• biosamples: Get biosample metadata for an assembly.

``bash uv run scripts/screen_api.py biosamples \ --output /tmp/biosamples.json ``

• orthologs: Get orthologous cCREs in another assembly.

``bash uv run scripts/screen_api.py orthologs EH38E2941922 \ --output /tmp/orthologs.json ``

• linked-genes: Find linked genes via methods like HiC or eQTLs.

``bash uv run scripts/screen_api.py linked-genes \ EH38E1516972 --output /tmp/linked.json ``

• gene-expression: Get gene expression (TPM) across all biosamples for a

named gene. Internally resolves the gene symbol to an Ensembl gene ID, then queries per-biosample RNA-seq quantifications.

``bash uv run scripts/screen_api.py gene-expression GAPDH \ --output /tmp/gene_expr.json ``

• entex: Get ENTEx data for a cCRE or genomic region.

``bash uv run scripts/screen_api.py entex \ --accession EH38E1310345 \ --output /tmp/entex.json ``

``bash uv run scripts/screen_api.py entex \ --region chr1:1000068:1000409 \ --output /tmp/entex.json ``

• gwas: Query genome-wide association studies, SNPs, or enrichment data.

``bash uv run scripts/screen_api.py gwas studies \ --output /tmp/gwas.json ``

``bash uv run scripts/screen_api.py gwas snps --study \ Ahola-Olli_AV-27989323-Eotaxin_levels \ --output /tmp/gwas_snps.json ``

You can supply the --assembly mm10 or --assembly grch38 flag to explicitly request a specific assembly for most commands. By default, the script targets grch38 but will automatically fall back to mm10 if no results are found or if the query fails.

ENCODE Portal REST API (Direct Access)

For accessing raw experiments, ChIP-seq peaks, or other datasets that are not represented as cCREs in SCREEN, use the scripts/encode_portal_api.py script. It allows custom queries to the ENCODE Portal REST API.

Usage

uv run scripts/encode_portal_api.py search "type=Experiment&target.label=ZNF549" --output /tmp/znf549_experiments.json

Data Analysis Tips

When analyzing .bed or .bigBed files downloaded from ENCODE, standard bioinformatics tools are highly recommended for finding overlaps (e.g., between gene promoters and peaks):

• bedtools: For fast mathematical operations on genomic intervals.
• bigBedToBed: For converting binary BigBed files to readable BED

format.

• pybedtools: A Python wrapper for bedtools.

Write custom logic if these tools are not pre-installed.

Custom Queries (SCREEN GraphQL)

If you need to make a complex GraphQL query that the script does not support, read references/graphql_schema.md for a reference of available queries, arguments, and return fields in the SCREEN GraphQL API.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: google-deepmind
• Source: google-deepmind/science-skills
• License: Apache-2.0
• Homepage: https://antigravity.google/use-cases/science

Install and usage instructions live in the source repository linked above.