myVCF

myVCF organizes VCF files into an SQLite database and provides querying, summary statistics, and export of genetic variants and sample genotypes to support interpretation of next-generation sequencing data.


Key Features:

  • Efficient Data Management: Organizes data from VCF (Variant Call Format) files into a structured SQLite database, supporting multiple sequencing projects and storing genetic variants and sample genotypes.
  • Flexible Search Engine: Enables queries by chromosomal regions, specific genes, individual variants, and dbSNP IDs.
  • Data Visualization and Reporting: Generates summary statistics reports that aggregate information from VCF files to characterize mutation distributions across samples and genomic/exomic regions and aid functional interpretation of variants.
  • Export Functionality: Exports mutation data for downstream analysis and integration with other bioinformatics tools.

Scientific Applications:

  • Variant discovery and disease association: Facilitates discovery and interpretation of genetic variants associated with human diseases using next-generation sequencing data.
  • Functional analysis of variants: Supports aggregation and reporting required for functional analysis of genetic variations in wet-lab research.
  • Translational and clinical research: Provides variant data management and summaries to support variant interpretation in clinical and translational research contexts.

Methodology:

Ingests VCF files and loads variant and genotype records into an SQLite database, supports indexed queries by chromosomal region/gene/variant/dbSNP ID, generates summary statistics and mutation distribution reports from aggregated VCF information, and exports mutation datasets.

Topics

Details

Tool Type:
desktop application
Operating Systems:
Linux, Windows, Mac
Programming Languages:
Python
Added:
6/16/2018
Last Updated:
11/25/2024

Operations

Publications

Pietrelli A, Valenti L. myVCF: a desktop application for high-throughput mutations data management. Bioinformatics. 2017;33(22):3676-3678. doi:10.1093/bioinformatics/btx475. PMID:29036298.

Documentation