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VEP Specifications

Process Details

  • Name: vep
  • Process UUID: 26y2psjp2yhg20xsdahujyhy89ygqq
  • Process Group: Misc.

Overview

The Variant Effect Predictor (VEP) is a comprehensive tool for annotating genetic variants with their predicted functional consequences. This process analyzes variants in VCF format and predicts the effects of variants on genes, transcripts, and protein sequences, providing detailed annotations including impact predictions, functional consequences, and regulatory effects.

This process is implemented in Bash, utilizing the Ensembl VEP tool within a containerized environment.

Key Functionality

  • Variant Annotation: Predicts the functional consequences of genetic variants on genes, transcripts, and proteins
  • Impact Assessment: Classifies variants by their predicted impact (HIGH, MODERATE, LOW, MODIFIER)
  • Regulatory Analysis: Identifies variants in regulatory regions and their potential effects on gene expression
  • Multi-format Output: Generates annotated VCF files with comprehensive variant effect predictions

Input/Output Specification

Inputs

Required Inputs

  • VCF File

    • Description: Input variant call format file containing genetic variants to be annotated
    • Format: VCF

  • Reference Genome

    • Description: Reference genome sequence in FASTA format used for variant annotation
    • Format: FASTA

  • Gene Annotation

    • Description: Gene annotation file containing transcript and gene structure information
    • Format: GTF

Outputs

  • Annotated VCF File
    • Description: Output VCF file containing original variants with added VEP annotations including consequence predictions, gene information, and impact assessments
    • Format: VCF

Parameters & Settings

This process uses default VEP parameters and does not expose user-configurable parameters in the Foundry UI.

References & Resources

  • Tool Documentation: Ensembl VEP Documentation
  • Related Papers: McLaren, W., Gil, L., Hunt, S.E., et al. (2016). The Ensembl Variant Effect Predictor. Genome Biology 17, 122. https://doi.org/10.1186/s13059-016-0974-4