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

IF 11.8 2区生物学 Q1 MULTIDISCIPLINARY SCIENCES

GigaScience Pub Date : 2024-01-02 DOI:10.1093/gigascience/giae065

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, Michal Okoniewski, Louis du Plessis, Uwe Schmitt, Franziska Singer, Tanja Stadler, Niko Beerenwinkel

引用次数: 0

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.

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V-pipe 3.0：用于样本内病毒遗传多样性估计的可持续管道。

下一代测序技术产生的病毒基因组数据集数量庞大、种类繁多，给计算数据分析工作流程带来了一系列挑战，包括严格的质量控制、扩展到大样本量以及针对特定应用的定制步骤。在此，我们介绍 V-pipe 3.0，这是一种专为分析短病毒基因组下一代测序数据而设计的计算管道。它的开发旨在实现病毒样本遗传多样性的可重复、可扩展、可调整和透明推断。通过介绍两个大型数据分析项目，我们展示了 V-pipe 3.0 在支持可持续病毒基因组数据科学方面的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

GigaScience MULTIDISCIPLINARY SCIENCES-

CiteScore

15.50

自引率

1.10%

发文量

119

审稿时长

1 weeks

期刊介绍： GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.