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

Fuhrmann, L.; Jablonski, K. P.; Topolsky, I.; Batavia, A. A.; Borgsmueller, N.; Icer Baykal, P.; Carrara, M.; Chen, C.; Dondi, A.; Dragan, M.; Dreifuss, D.; John, A.; Langer, B.; Okoniewski, M.; du Plessis, L.; Schmitt, U.; Singer, F.; Stadler, T.; Beerenwinkel, N.

doi:10.1101/2023.10.16.562462

bioRxiv · 10.1101/2023.10.16.562462

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

Fuhrmann, L.·Jablonski, K. P.·Topolsky, I.·Batavia, A. A.·Borgsmueller, N.·Icer Baykal, P.·Carrara, M.·Chen, C.·Dondi, A.·Dragan, M.·Dreifuss, D.·John, A.·Langer, B.·Okoniewski, M.·du Plessis, L.·Schmitt, U.·Singer, F.·Stadler, T.·Beerenwinkel, N.

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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, 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.

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Fuhrmann, L., Jablonski, K. P., Topolsky, I., Batavia, A. A., Borgsmueller, N., Icer Baykal, P., Carrara, M., Chen, C., Dondi, A., Dragan, M., Dreifuss, D., John, A., Langer, B., Okoniewski, M., du Plessis, L., Schmitt, U., Singer, F., Stadler, T., Beerenwinkel, N.. 2023-10-16. V-pipe 3.0: a sustainable pipeline for within-sample viral genetic diversity estimation. https://doi.org/10.1101/2023.10.16.562462

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