Ivan Topolsky

VILOCA improves mutation and haplotype calling accuracy for viral sequencing data, especially long-read data, by using quality-aware clustering and Dirichlet process mixture models.

The paper evaluates nine computational tools for identifying SARS-CoV-2 lineages in wastewater sequencing data using simulated samples, finding most tools reliably detect lineages above 5% frequency.

V-pipe 3.0 is a computational pipeline for analyzing viral genomic data, enabling reproducible and scalable estimation of within-sample genetic diversity.

V-pipe is a computational pipeline that assesses viral genetic diversity from high-throughput sequencing data by integrating quality control, alignment, mutation calling, and haplotype inference.

The paper examines SARS-CoV-2 genetic diversity within individual patients, finding that a small number of patients carry most of the diversity and that older patients tend to have more diverse viral variants.