### Ensembl Database
Works with
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)...."
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionensembl-databaseExecute the skills CLI command in your project's root directory to begin installation:
Fetches ensembl-database from google-deepmind/science-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate ensembl-database. Access via /ensembl-database in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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| 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. |
uv: Read the uv skill and follow its Setup instructions to ensure
uv is installed and on PATH.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:
"human". You MUST explicitly flag this default to the user to
ensure they are aware.--assembly GRCh37 flag. You MUST explicitly flag to the
user when a non-default assembly is being used./tmp by default, or to a user-specified file using the --output
flag. It also prints a concise summary to stdout.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
transcriptscommand,canonical-tssaccepts both symbols (e.g.,TP53) and Ensembl IDs, and automatically resolves them. It also does the math for strand orientation (TSS isStartfor+strand andEndfor-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.
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:
--output or the temporary file
path printed by the script).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.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).
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
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✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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ensembl-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend ensembl-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: ensembl-database is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: ensembl-database is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: ensembl-database is focused, and the summary matches what you get after install.
ensembl-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
ensembl-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: ensembl-database is focused, and the summary matches what you get after install.
I recommend ensembl-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
ensembl-database fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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