V-pipe 3.0: a sustainable pipeline for within-sample viral genetic diversity estimation
Lara Fuhrmann, Kim Philipp Jablonski, Ivan Topolsky, Aashil A. Batavia, Nico Borgsmüller, Pelin Icer Baykal, Matteo Carrara, Chaoran Chen, Arthur Dondi, Monica Dragan, David Dreifuss, Anika John, Benjamin Langer, Michał Okoniewski, Louis du Plessis, Uwe Schmitt, Franziska Singer, Tanja Stadler, Niko Beerenwinkel
Abstract
The 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, scaling to large sample sizes, 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 2 large-scale data analysis projects, we demonstrate the effectiveness of V-pipe 3.0 in supporting sustainable viral genomic data science.