ensembl-annotation
Query the Ensembl REST API for regulatory feature annotations, variant effect prediction (VEP), coordinate liftover, gene lookups, and cross-references. Use when the user needs to annotate variants with VEP (consequence, CADD, REVEL, SpliceAI), check Ensembl Regulatory Build overlap for ENCODE regions, convert coordinates between GRCh37 and GRCh38, resolve gene IDs (Ensembl ↔ symbol ↔ RefSeq), look up gene phenotype associations, or cross-reference ENCODE targets with Ensembl annotations. Also use when the user mentions Ensembl, VEP, variant effect predictor, liftover, assembly conversion, reg
npx skills add majiayu000/claude-skill-registry --skill ensembl-annotation-ammawla-encode-toolkit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Query the Ensembl REST API ## When to Use - User wants to annotate variants with Ensembl VEP (Variant Effect Predictor) consequences - User asks about "VEP", "Ensembl", "variant annotation", "regulatory build", or "gene annotation" - User needs to convert coordinates between assemblies using Ensembl's liftover API - User wants to check the Ensembl Regulatory Build for overlap with ENCODE elements - Example queries: "run VEP on my variant list", "annotate SNPs with regulatory consequences", "check Ensembl regulatory build for my peaks" Annotate variants, look up regulatory features, convert coordinates, and resolve gene identifiers using the Ensembl REST API. ## Scientific Rationale **The question**: "What does the Ensembl Regulatory Build say about this region, and what is the predicted effect of this variant?" The Ensembl Regulatory Build integrates ENCODE, Roadmap Epigenomics, and Blueprint data into a unified annotation of regulatory features across human cell types. The Variant Effect Predictor (VEP) is the standard tool for variant consequence prediction, integrating 50+ annotation sources including CADD, REVEL, SpliceAI, and AlphaMissense. ### Ensembl ↔ ENCODE Feedback Loop
- When to Use
- Scientific Rationale
- Ensembl ↔ ENCODE Feedback Loop
- Literature Support
- API Reference
- Step 1: Regulatory Feature Overlap
- Regulatory Feature Types
- Step 2: Variant Effect Prediction (VEP)
- Single Variant (by region notation)
- Batch VEP (POST, up to 200 variants)
- Key VEP Parameters
- VEP Consequence Hierarchy (most to least severe)
- Step 3: Coordinate Conversion (LiftOver)
- Step 4: Gene Lookup and Cross-References
Get regulatory features in a region curl -H "Content-type: application/json" \ Also get TF binding motifs VEP annotation for a variant By rs ID curl -X POST -H "Content-type: application/json" \ GRCh37 → GRCh38 GRCh38 → GRCh37 By Ensembl ID By symbol
What does the ensembl-annotation skill do?
Query the Ensembl REST API for regulatory feature annotations, variant effect prediction (VEP), coordinate liftover, gene lookups, and cross-references. Use when the user needs to annotate variants with VEP (consequence, CADD, REVEL, SpliceAI), check Ensembl Regulatory Build overlap for ENCODE regions, convert coordinates between GRCh37 and GRCh38, resolve gene IDs (Ensembl ↔ symbol ↔ RefSeq), look up gene phenotype associations, or cross-reference ENCODE targets with Ensembl annotations. Also use when the user mentions Ensembl, VEP, variant effect predictor, liftover, assembly conversion, reg
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ensembl-annotation-ammawla-encode-toolkit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.