V-pipe 3.0: a sustainable pipeline for within-sample viral genetic diversity estimation

• DOI: 10.1101/2023.10.16.562462
• Publication Date: 2023-10-17T03:30:14Z
• Authors: Lara Fuhrmann; Kim Philipp Jablo...; Ivan Topolsky; Aashil A Batavia; Nico Borgsmüller; Pelin Icer Baykal; Matteo Carrara; Chaoran Chen; Arthur Dondi; Monica Dragan; David Dreifuss; Anika John; Benjamin Langer; Michal Okoniewski; Louis du Plessis; Uwe Schmitt; Franziska Singer; Tanja Stadler; Niko Beerenwinkel
• Keywords: Sustainable workflow; Global haplotype reconstruction; sustainable data analysis workflow; Next-generation sequencing; Data processing; Sustainable workflow; Benchmark; Global haplotype reconstruction; Research; NGS data processing; next-generation sequencing; NGS data processing; sustainable data analysis workflow; benchmark; global haplotype reconstruction; viral genetic diversity; Genetic Variation; High-Throughput Nucleotide Sequencing; Computational Biology; Genome, Viral; Genomics; Benchmark; viral genetic diversity; Data processing; benchmark; Viruses; Next-generation sequencing; Humans; next-generation sequencing; global haplotype reconstruction; Software

AbstractThe large amount and diversity of viral genomic datasets generated by next-generation sequencing technologies poses a set of challenges for computational data analysis workflows, including rigorous quality control, adaptation to higher sample coverage, and tailored steps for specific applications. Here, we present V-pipe 3.0, a computational pipeline designed for analyzing next-generation sequencing data of short viral genomes. It is developed to enable reproducible, scalable, adaptable, and transparent inference of genetic diversity of viral samples. By presenting two large-scale data analysis projects, we demonstrate the effectiveness of V-pipe 3.0 in supporting sustainable viral genomic data science.