vcf-annotator

You can annotate VCF variants by automating the integration of Ensembl VEP, ClinVar, and gnomAD APIs for variant effect prediction, pathogenicity classification, and allele frequency calculation. This generates a reproducible report with complete workflow history.

Ranking genomic variants by impact involves classifying variant effects into HIGH, MODERATE, LOW, and MODIFIER categories. This impact ranking is automatically generated alongside pathogenicity and allele frequency data during the VCF annotation process.

Yes, Ensembl VEP works with Python for variant annotation by utilizing the cyvcf2 library for VCF parsing. This integration enables automated variant effect prediction and pathogenicity classification directly within Python-based genomic workflows.

Generating reproducible reports for clinical diagnostics from VCF files is supported by capturing a complete workflow history during variant annotation. This targets researchers and clinicians working with genomic data for variant interpretation and diagnostics.

Yes, Python and cyvcf2 are required dependencies to run these variant annotation workflows, along with ensembl-vep. They provide the necessary environment to parse VCF files and interface with external databases like ClinVar and gnomAD.

Pathogenicity classification in VCF annotation utilizes the ClinVar and gnomAD databases, integrated alongside Ensembl VEP. This combination provides comprehensive variant effect prediction and allele frequency calculation for genomic data analysis.