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Ensembl Database

Google DeepMindOfficialPublished by Google DeepMind

  • Skill
  • Tool integration
  • Science and research
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Query the Ensembl database to resolve gene, transcript, and protein IDs, fetch genomic or protein sequences, retrieve gene structures (exons), and get variant consequence and effect predictions (VEP). Use this skill as a primary ID translator, genomic sequence database and variant effect prediction tool.
— the description by Google DeepMind

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Ensembl database skill provides a structured interface to the Ensembl REST API, enabling users to perform complex genomic data lookups. It supports ID resolution across various biological databases, retrieval of genomic and protein sequences, and detailed analysis of transcript structures. Additionally, it integrates Variant Effect Predictor (VEP) functionality to assess the functional impact of genomic variants using various open-licensed plugins. The skill includes helper scripts to manage API rate limits and provides standardized output formats for downstream analysis.

What it does

  • Resolves gene symbols, aliases, and RefSeq IDs to Ensembl stable identifiers.
  • Cross-references Ensembl IDs with external databases like UniProt, HGNC, and RefSeq.
  • Fetches raw genomic DNA or protein sequences for specific regions or IDs.
  • Retrieves detailed transcript metadata, including exon coordinates and UTR regions.
  • Predicts variant consequences and effects using the Ensembl VEP tool.
  • Calculates transcription start site (TSS) coordinates for canonical transcripts.

When to use it

  • When you need to map between different biological identifier systems.
  • When you require raw genomic or protein sequence data for specific features.
  • When analyzing the functional impact of a specific genomic variant.
  • When you need to extract structural details of a gene or transcript, such as exon boundaries.
  • When performing batch lookups for gene or transcript metadata.

Advantages

  • Automated management of API rate limits ensures reliable data retrieval.
  • Provides a unified interface for both standard lookups and complex VEP analysis.
  • Supports multiple assemblies, including GRCh38 and GRCh37.
  • Simplifies complex tasks like TSS calculation and transcript filtering.
  • Outputs data in structured formats suitable for programmatic processing.

Usage examples

“What is the Ensembl ID for the TP53 gene in humans?”
The agent uses the resolve-gene command to return the ENSG ID for TP53, defaulting to the human species.
“Get the protein sequence for transcript ENST00000269305.”
The agent executes the protein-sequence command and saves the amino acid FASTA output to a file.
“Predict the effect of the variant 9:21971147:T:C.”
The agent runs the VEP command and presents a detailed list of consequences and plugin predictions to the user.
“Find the transcription start site for the canonical transcript of BRCA1.”
The agent uses the canonical-tss command to resolve the symbol and output the specific integer coordinate.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google-deepmind/ensembl-database/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip ensembl-database.zip -d .agents/skills

Documentation by Google DeepMind

Ensembl Database: ID Mapping and Genomic Features

Prerequisites

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.

  2. User Notification: If .licenses/ensembl_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://useast.ensembl.org/index.html and https://github.com/Ensembl/ensembl-rest/wiki, then (2) create the file recording the notification text and timestamp.

Overview

The Ensembl database is a resource for genome annotation. This skill allows you to interact with the Ensembl REST API to resolve ambiguous symbols, cross-reference IDs (RefSeq, HGNC, UniProt, ENSG), fetch raw sequences, and retrieve detailed transcript structures.

Key Concepts:

  • ENSG (Gene): Stable identifier for a human gene. Other species will have different three-letter species codes.

  • ENST (Transcript): Stable identifier for a transcript (splicing isoform).

  • ENSP (Protein): Stable identifier for a translated protein.

  • MANE Select: The consensus primary transcript agreed upon by Ensembl and NCBI.

  • Canonical: Ensembl's representative transcript (used if MANE is not available or non-human).

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.

  • Default Species: If the species is absent or ambiguous in the prompt, default to "human". You MUST explicitly flag this default to the user to ensure they are aware.

  • Primary Transcripts: When listing transcripts for a gene, only return the MANE Select transcript (for human) or the Canonical transcript (for others) unless the user explicitly asks for all alternative isoforms. You MUST flag to the user when multiple transcripts are available and you are defaulting to the primary one.

  • Assembly Handling: The default assembly is GRCh38. For GRCh37 requests, you MUST use the --assembly GRCh37 flag. You MUST explicitly flag to the user when a non-default assembly is being used.

  • Output Location: The script writes full JSON/FASTA output to temporary files in /tmp by default, or to a user-specified file using the --output flag. It also prints a concise summary to stdout.

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

Available Commands

1. Resolve Gene ID — Resolve a symbol, alias, or RefSeq ID to ENSG ID(s). Automatically falls back to resolving synonyms if primary symbol is not found.

uv run scripts/ensembl_api.py resolve-gene TP53 --species human --output tp53.json
uv run scripts/ensembl_api.py resolve-gene PCL2 --output pcl2.json # Falls back to synonym resolution

2. Map ID to External Database — Cross-reference an Ensembl ID to UniProt, HGNC, RefSeq, etc.

uv run scripts/ensembl_api.py map-id ENSG00000141510 --external-db UniProt --output uniprot_map.json
uv run scripts/ensembl_api.py map-id ENST00000269305 --external-db RefSeq_mRNA --output refseq_map.json

3. Get Genomic Sequence — Fetch raw DNA for a coordinate window. Supports GRCh37 via --assembly GRCh37.

uv run scripts/ensembl_api.py get-sequence 17:7661779-7687550 --species human --output seq.txt
uv run scripts/ensembl_api.py get-sequence chr9:21971100-21971200 --assembly GRCh37 --output seq_grch37.txt

4. Gene Summary — High-level metadata: symbol, biotype, description, chromosomal location.

uv run scripts/ensembl_api.py gene-summary ENSG00000141510 --output gene_summary.json

5. List Transcripts — All transcripts for a gene, with optional --only-mane or --only-canonical filters. Output includes Transcript Support Level (TSL).

uv run scripts/ensembl_api.py transcripts ENSG00000141510 --only-mane --output transcripts_mane.json
uv run scripts/ensembl_api.py transcripts ENSG00000141510 --only-canonical --output transcripts_canonical.json
uv run scripts/ensembl_api.py transcripts ENSG00000141510 --output transcripts_all.json

5b. Canonical TSS — Get the single coordinate of the Transcription Start Site (TSS) for the canonical transcript of a gene.

[!NOTE] Unlike the standard transcripts command, canonical-tss accepts both symbols (e.g., TP53) and Ensembl IDs, and automatically resolves them. It also does the math for strand orientation (TSS is Start for + strand and End for - strand), outputting the single integer coordinate directly.

uv run scripts/ensembl_api.py canonical-tss TP53 --output tp53_tss.json
uv run scripts/ensembl_api.py canonical-tss ENSG00000141510 --output tss.json

6. Transcript Structure — Exon coordinates, CDS boundaries, and computed 5'/3' UTR regions for a transcript.

uv run scripts/ensembl_api.py transcript-structure ENST00000269305 --output structure.json

7. Protein Info — ENSP ID and sequence length for a transcript.

uv run scripts/ensembl_api.py protein-info ENST00000269305 --output protein_info.json

8. Protein Sequence — Amino acid FASTA for a transcript (ENST) or protein (ENSP) ID.

uv run scripts/ensembl_api.py protein-sequence ENST00000269305 --output protein.fasta
uv run scripts/ensembl_api.py protein-sequence ENSP00000269305 --output protein_ensp.fasta

9. Variant Consequence (VEP) — Predict molecular consequences for a genomic variant. Includes open-licensed plugins: AlphaMissense, Conservation, DosageSensitivity, IntAct, MaveDB, OpenTargets, LoF (Loftee), NMD, UTRAnnotator, mutfunc, LOEUF.

uv run scripts/ensembl_api.py vep 9:21971147:T:C --species human --output vep.json
uv run scripts/ensembl_api.py vep rs699 --species human --output vep_rs699.json

Example VEP stdout output:

[*] Variant: 9:21971147:T>C
[*] Most severe consequence: missense_variant
[*] Found 15 transcript consequences.

[*] VEP Predictions:

  - ENST00000304494 (CDKN2A): Consequence = missense_variant
  - ENST00000304494 (CDKN2A): Amino Acids = N/S
  - ENST00000304494 (CDKN2A): SIFT = deleterious (0.01)
  - ENST00000304494 (CDKN2A): AlphaMissense Class = likely_benign
  - ENST00000304494 (CDKN2A): AlphaMissense Pathogenicity = 0.2129
  - ENST00000304494 (CDKN2A): Conservation = 2.05
  - ENST00000304494 (CDKN2A): Dosage Sensitivity (Haplo) = 0.889228328567991
  - ENST00000304494 (CDKN2A): Dosage Sensitivity (Triplo) = 0.135514349094646
  - ENST00000304494 (CDKN2A): Loss of Function (LOEUF) = 0.791

Presenting VEP Results: After running the VEP command, you MUST present the full VEP Predictions list from stdout to the user. This list contains both standard VEP predictions (Consequence, Amino Acids, SIFT, PolyPhen) and open-license plugin results (AlphaMissense, Conservation, Dosage Sensitivity, LOEUF, Loftee LoF, NMD, UTRAnnotator, Mutfunc). Do NOT just summarize — show the complete list so the user can see all predictions. If the list is very long (many transcripts), show the MANE Select / canonical transcript rows in full and note that the complete data is in the JSON output.

Parsing Outputs

If the user needs detailed, nested structural data (like the precise integer coordinates of Exon 2 of a transcript) that isn't summarized in stdout:

  1. Locate the JSON file (either specified via --output or the temporary file path printed by the script).

  2. Use terminal tools like jq or write a quick, disposable python snippet to extract the specific data point requested. Do not attempt to read the entire JSON file into your context if it is very large.

Custom Queries

If you need to make an API call that the script does not support (e.g., fetching protein domain annotations, coordinate mapping between assemblies, homology searches, linkage disequilibrium, or phenotype lookups), read references/ensembl_rest_api_reference.md for a complete reference of available endpoints, parameters, and response fields.

CRITICAL: When writing custom scripts or using alternatives to the provided scripts, you MUST respect the Ensembl REST API rate limits (maximum 15 requests per second) and handle 429 Too Many Requests errors gracefully (e.g., with exponential backoff).

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

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Attribution and licence

Published by
Google DeepMind
Publisher
Google DeepMind (Official)
Source
google-deepmind/science-skills · skills/ensembl_database · commit 4de6786
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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