OVAS: an open-source variant analysis suite with inheritance modelling.

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• High-throughput sequencing generates vast amounts of data, outpacing current analytical tools.
• There is a growing need to distinguish disease-causing genetic variants from normal variations.
• Reliance on cloud-based services for sequence analysis raises concerns about data privacy and accessibility, especially with limited internet connectivity.

Purpose of the Study:

• To introduce a standalone, open-source variant analysis pipeline named OVAS.
• To provide a secure, accessible, and in-situ solution for processing patient genomic data.
• To address the challenges posed by large sequencing datasets and the limitations of cloud-based analysis.

Main Methods:

• OVAS is a modular, offline analysis environment processing Variant Call Format (VCF) files.
• It employs a three-stage process: annotation, filtering, and interpretation.
• Annotation includes variant-mapping, functional data appending, and heterozygosity determination. Filtering involves inheritance modeling and 11 other components, including quality control and penetrance models.
• Interpretation can include organ specificity via gene expression and protein domains.

Main Results:

• The OVAS pipeline was utilized to analyze an autosomal recessive case study.
• It effectively employed its filtering modules to identify a compound-heterozygous disease pattern.
• The pipeline successfully recapitulated the study's results using exome-capture sequence input samples.

Conclusions:

• OVAS is an offline, open-source, modular analysis environment for variant annotation and extraction from VCF files.
• It processes variants within an inheritance context using a top-down filtering schema.
• The pipeline runs entirely off a live bootable medium and is accessed locally via a web browser, ensuring data security and accessibility.