ensembl-database — Claude Code skill: query the Ensembl API
Resolves gene symbols into ENSG, ENST and ENSP IDs, pulls sequences and predicts variant effects with VEP. Useful when genomic data lives in your terminal.
npx skills add google-deepmind/science-skills --skill ensembl-database -a claude-code
npx skills add google-deepmind/science-skills --skill ensembl_database -a claude-codeWhat ensembl-database does
The skill wraps the Ensembl REST API in a set of Python commands. It resolves ambiguous symbols and aliases, cross-references IDs against RefSeq, HGNC and UniProt, fetches raw DNA for a coordinate window, returns transcript structure with exon coordinates and CDS boundaries, and produces amino acid FASTA for an ENST or ENSP ID. Variant work goes through VEP, which predicts molecular consequences and pulls in the AlphaMissense, Conservation, DosageSensitivity, IntAct, MaveDB, OpenTargets, LoF, NMD, UTRAnnotator, mutfunc and LOEUF plugins.
Everything runs through scripts/ensembl_api.py via uv run. The script enforces the required rate limit on its own, so hitting the database directly is the wrong move. Full JSON or FASTA output lands in a file under /tmp or wherever --output points, while stdout gets a short summary. The default assembly is GRCh38; GRCh37 requests need the --assembly GRCh37 flag.
Make sure uv is installed and on PATH before the first call. If .licenses/ensembl_database_LICENSE.txt is missing from the workspace root, the skill asks you to check the Ensembl terms and then writes that file with a timestamp. Species defaults to human, and transcript listings return only the MANE Select transcript for human or the Canonical one elsewhere unless you ask for every isoform.
What it helps with
- turn a gene symbol into a stable ENSG ID
- cross-reference an Ensembl ID to UniProt or RefSeq
- pull a DNA sequence for a chromosome window
- get exon coordinates and CDS boundaries for a transcript
- score a variant's consequences with VEP
Example prompts for the agent
- resolve TP53 to an ENSG ID for human
- show the transcript structure of ENST00000269305
- predict the effect of variant 9:21971147:T:C with VEP
How to install ensembl-database
1Skills CLI (Vercel)
npx skills add google-deepmind/science-skills --skill ensembl-database -a claude-codenpx skills add google-deepmind/science-skills --skill ensembl_database -a claude-code2Manually: put the files in place
~/.claude/skills/ensembl_database/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/ensembl_database/
1Skills CLI (Vercel)
npx skills add google-deepmind/science-skills --skill ensembl_database -a codex2Manually: put the files in place
.agents/skills/ensembl_database/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add google-deepmind/science-skills --skill ensembl_database -a cursor2Manually: put the files in place
.cursor/skills/ensembl_database/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install google-deepmind/science-skills ensembl_database2Manually: put the files in place
.github/skills/ensembl_database/SKILL.md1Skills CLI (Vercel)
npx skills add google-deepmind/science-skills --skill ensembl_database -a gemini-cli2Platform CLI
gemini skills install https://github.com/google-deepmind/science-skills --path skills/ensembl_databaseManually: put the files in place
.devin/skills/ensembl_database/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add google-deepmind/science-skills --skill ensembl_database -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about ensembl-database
How do I install ensembl-database in Claude?
Run npx skills add google-deepmind/science-skills --skill ensembl-database -a claude-code. The skill file ends up at ~/.claude/skills/ensembl_database/SKILL.md.
What does ensembl-database do?
It resolves gene, transcript and protein IDs, fetches genomic and protein sequences, returns exon structure, and predicts variant consequences through VEP.
What do I need before using it?
uv installed and on PATH. On the first run the skill also asks you to review the Ensembl terms and creates .licenses/ensembl_database_LICENSE.txt in the workspace root.
Source text
SKILL.md · Apache-2.0
---
name: ensembl-database
description: >
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.
---
# 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.
```bash
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.
```bash
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`.
```bash
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.
```bash
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).
```bash
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.
```bash
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.
```bash
uv run scripts/ensembl_api.py transcript-structure ENST00000269305 --output structure.json
```
**7. Protein Info** — ENSP ID and sequence length for a transcript.
```bash
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.
```bash
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.
```bash
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).
© google-deepmind · Apache-2.0 · google-deepmind/science-skills