VarAFT: a variant annotation and filtration system for human next generation sequencing data

Area of Science:

• Genomics
• Bioinformatics
• Medical Genetics

Background:

• High-throughput sequencing technologies, such as next-generation sequencing (NGS), have transformed gene discovery and diagnosis.
• The transition from research to clinical practice has been rapid, with whole-genome sequencing becoming increasingly common.
• The primary limitation in genomic analysis has shifted from sequencing capabilities to the complex challenge of data analysis.

Purpose of the Study:

• To develop a software tool, VarAFT, for efficient annotation and identification of human disease-causing mutations.
• To provide a user-friendly solution for researchers and clinicians, irrespective of their bioinformatics expertise.
• To integrate diverse data layers for accurate pinpointing of pathogenic variants.

Main Methods:

• Development of VarAFT software for variant annotation and filtration.
• Integration of multiple data sources including dbNSFP, OMIM, HPO, Gene Ontology, pathways, UMD-Predictor, and Human Splicing Finder.
• Application of VarAFT to analyze data from multiple samples for various inheritance modes, cancers, and population genetics.

Main Results:

• VarAFT enables rapid and efficient identification of disease-causing mutations from large datasets.
• The software combines classical annotations with unique disease, phenotypic, gene, and variation-level data.
• Optimized filtration parameters can be stored and reused for large-scale analyses.

Conclusions:

• VarAFT addresses the critical challenge of analyzing vast amounts of genomic data generated by NGS.
• The software facilitates the identification of pathogenic mutations in both research and clinical settings.
• VarAFT is a freely accessible tool designed to empower scientists in genetic diagnosis and discovery.