LAVA

LAVA visualizes viral allele frequency dynamics from longitudinal sequencing data to track genomic variation over time.

Key Features:

• Genome-wide allele visualization: Displays allele frequency trajectories and variant positions across the viral genome over longitudinal samples.
• Automated longitudinal analysis: Accepts a reference FASTA file, paired-end FASTQ files for subsequent samples, and a metadata sheet with collection times or passages to analyze time-resolved sequence data.
• Output format and quantitative metrics: Produces single-file HTML outputs that encode read depth and allele frequency information for inspection and sharing.
• Implementation and performance: Implemented in Python 3, runs on Mac and Linux, and processes typical use cases in minutes.

Scientific Applications:

• Longitudinal viral evolution studies: Tracks temporal dynamics of viral genomes to identify evolving variants over time.
• Selection pressure analysis: Supports investigation of viral responses to selective pressures such as immune responses or antiviral treatments.
• Minor allele variant monitoring: Enables detection and visualization of low-frequency (minor) allele variants across serial samples.
• Evolutionary trend and fitness assessment: Aids identification of mutation rates, evolutionary trends, and potential impacts of genetic changes on viral fitness and pathogenicity.

Methodology:

Integrates sequence data from multiple time points and processes input files (reference FASTA, paired-end FASTQ, metadata sheet) to generate visualizations that highlight allele frequency changes and genomic variations across longitudinal samples.