Identification of Viral Variants from Functional Genomics Data.

Area of Science:

• Virology
• Genomics
• Bioinformatics

Background:

• Virus mutants are crucial for studying viral protein functions during infections.
• Functional genomics experiments using sequencing assays (e.g., RNA-seq, ATAC-seq) are common for analyzing infected cells.
• Existing mutant virus strains often lack adequate documentation, especially older ones.

Purpose of the Study:

• To develop a pipeline for directly identifying viral mutations from functional genomics data.
• To overcome limitations of existing variant callers in handling coverage variations in functional genomics data.

Main Methods:

• A pipeline combining existing SNP callers with novel methods for deletion and insertion identification was developed.
• The pipeline identifies mutations directly from sequencing-based functional genomics data.
• Novel methods were created to address poor performance of structural variant callers on data with significant read coverage fluctuations.

Main Results:

• The pipeline was successfully evaluated on RNA-seq data from Herpes simplex virus 1 (HSV-1) knockout mutants.
• The identified variants accurately matched the known mutations from original publications.
• The pipeline demonstrated effectiveness in recovering introduced mutations in viral genomes.

Conclusions:

• The developed pipeline provides a rapid and user-friendly method for identifying viral genome variants.
• This approach negates the requirement for additional genome sequencing.
• The pipeline is implemented as a Watchdog workflow (VariantCallerPipeline) and is publicly available.