V-pipe: a computational pipeline for assessing viral genetic diversity from high-throughput data

Area of Science:

• Bioinformatics
• Genomics
• Virology

Background:

• High-throughput sequencing (HTS) is crucial for viral genomics, surveillance, and diagnostics.
• Analyzing intra-host viral genetic diversity is essential for understanding transmission, virulence, and pathogenesis.
• Key challenges in HTS viral diversity analysis include distinguishing true biological variants from errors and managing large data volumes.

Purpose of the Study:

• To develop an automated bioinformatics pipeline (V-pipe) for end-to-end analysis of viral HTS data.
• To provide tools for quality control, read mapping, low-frequency mutation calling, and viral haplotype inference.
• To establish a standardized benchmarking environment for evaluating different pipeline configurations and tools.

Main Methods:

• Developed V-pipe, a modular bioinformatics pipeline integrating state-of-the-art statistical models and computational tools.
• Introduced ngshmmalign, a novel profile hidden Markov model-based method for high-quality read alignment of small, diverse viral genomes.
• Implemented benchmarking functionality to compare performance of different read aligners and variant callers.

Main Results:

• V-pipe automates quality control, read mapping, alignment, low-frequency mutation calling, and viral haplotype inference.
• The ngshmmalign method enhances read alignment quality for viral genomes.
• Benchmarking demonstrated the impact of different aligners and variant callers on single-nucleotide variant calling in intra-host viral populations.

Conclusions:

• V-pipe offers a comprehensive solution for automated viral HTS data analysis.
• The pipeline's modular design allows for adaptation to evolving sequencing technologies.
• V-pipe facilitates robust assessment of viral genetic diversity, aiding research and clinical applications.