CAPHEINE, or Everything and the Kitchen Sink: A Workflow for Automating Selection Analyses Using HyPhy.

IF 3.3 2区生物学 Q2 EVOLUTIONARY BIOLOGY

Genome Biology and Evolution Pub Date : 2026-08-01 DOI:10.1093/gbe/evag173

Hannah Verdonk, Danielle Callan, Sergei Pond

引用次数: 0

Abstract

Here, we present CAPHEINE, a computational workflow that starts with a set of unaligned pathogen sequences and a reference genome and performs a comprehensive exploratory evolutionary analysis of the input data. CAPHEINE pairs nicely with studies of site-level selection dynamics, gene-level positive selection, and lineage-specific shifts in selective pressure. Our workflow is portable across Mac OS, Windows, and Linux, allowing researchers to focus on results. CAPHEINE is freely available at https://github.com/veg/CAPHEINE, along with a set of usage instructions.

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CAPHEINE，或一切和厨房水槽：使用HyPhy自动化选择分析的工作流程。

在这里，我们提出CAPHEINE，这是一个计算工作流，从一组未对齐的病原体序列和参考基因组开始，并对输入数据进行全面的探索性进化分析。CAPHEINE与位点水平的选择动力学、基因水平的正选择和选择压力的谱系特异性转移的研究很好地结合在一起。我们的工作流程可在Mac OS、Windows和Linux上移植，使研究人员能够专注于结果。CAPHEINE可在https://github.com/veg/CAPHEINE免费获得，并附有一套使用说明。

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

Genome Biology and Evolution EVOLUTIONARY BIOLOGY-GENETICS & HEREDITY

CiteScore

5.80

自引率

6.10%

发文量

169

审稿时长

1 months

期刊介绍： About the journal Genome Biology and Evolution (GBE) publishes leading original research at the interface between evolutionary biology and genomics. Papers considered for publication report novel evolutionary findings that concern natural genome diversity, population genomics, the structure, function, organisation and expression of genomes, comparative genomics, proteomics, and environmental genomic interactions. Major evolutionary insights from the fields of computational biology, structural biology, developmental biology, and cell biology are also considered, as are theoretical advances in the field of genome evolution. GBE’s scope embraces genome-wide evolutionary investigations at all taxonomic levels and for all forms of life — within populations or across domains. Its aims are to further the understanding of genomes in their evolutionary context and further the understanding of evolution from a genome-wide perspective.