---
name: vep-variant-annotator-f01403a4727d
description: Variant Effect Predictor skill for comprehensive variant annotation with clinical database integration
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Edit
  - WebFetch
  - WebSearch
  - Bash
metadata:
  version: "1.0"
  category: bioinformatics
  tags:
    - variant-analysis
    - annotation
    - clinical
    - functional
graph:
  domains: [domain:bioinformatics]
  specializations: [specialization:biomedical-informatics]
  skillAreas: [skill-area:statistical-analysis, skill-area:data-analysis, skill-area:python-data-pipelines]
  workflows: [workflow:experiment-design]
  roles: [role:research-engineer, role:biomedical-engineer]
---

# VEP Variant Annotator Skill

## Purpose
Provide comprehensive variant annotation using Variant Effect Predictor with clinical database integration.

## Capabilities
- Functional consequence prediction
- Population frequency annotation (gnomAD)
- Clinical database integration (ClinVar, COSMIC)
- Custom annotation plugins
- Pathogenicity score integration (CADD, REVEL)
- Regulatory region annotation

## Usage Guidelines
- Configure VEP with relevant annotation sources
- Include population frequency databases
- Add clinical databases for interpretation
- Use pathogenicity predictors for prioritization
- Document annotation database versions
- Update annotations regularly

## Dependencies
- Ensembl VEP
- ANNOVAR
- SnpEff

## Process Integration
- Whole Genome Sequencing Pipeline (wgs-analysis-pipeline)
- Clinical Variant Interpretation (clinical-variant-interpretation)
- Pharmacogenomics Analysis (pharmacogenomics-analysis)
- Rare Disease Diagnostic Pipeline (rare-disease-diagnostics)


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences
Roles: Clinical Research, Data Scientist, Researcher
Specialties: Specialty not specified
Tools: Annovar, Ensembl Vep, Snpeff
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: dc8458eac0d7b38463c262f46132fbde5a650e0554eb80c80b31b0c6f8936e8a