Bioinformatics Research Center, Aarhus University, Aarhus, Denmark.
Mol Biol Evol. 2024 May 3;41(5). doi: 10.1093/molbev/msae070.
Estimating the distribution of fitness effects (DFE) of new mutations is of fundamental importance in evolutionary biology, ecology, and conservation. However, existing methods for DFE estimation suffer from limitations, such as slow computation speed and limited scalability. To address these issues, we introduce fastDFE, a Python-based software package, offering fast, and flexible DFE inference from site-frequency spectrum (SFS) data. Apart from providing efficient joint inference of multiple DFEs that share parameters, it offers the feature of introducing genomic covariates that influence the DFEs and testing their significance. To further simplify usage, fastDFE is equipped with comprehensive VCF-to-SFS parsing utilities. These include options for site filtering and stratification, as well as site-degeneracy annotation and probabilistic ancestral-allele inference. fastDFE thereby covers the entire workflow of DFE inference from the moment of acquiring a raw VCF file. Despite its Python foundation, fastDFE incorporates a full R interface, including native R visualization capabilities. The package is comprehensively tested and documented at fastdfe.readthedocs.io.
估算新突变的适应度效应（DFE）分布在进化生物学、生态学和保护生物学中具有重要意义。然而，现有的 DFE 估计方法存在计算速度慢和可扩展性有限等问题。为了解决这些问题，我们引入了 fastDFE，这是一个基于 Python 的软件包，提供了从位点频率谱（SFS）数据中快速、灵活的 DFE 推断。除了提供共享参数的多个 DFE 的高效联合推断外，它还提供了引入影响 DFE 并测试其显著性的基因组协变量的功能。为了进一步简化使用，fastDFE 配备了全面的 VCF 到 SFS 解析实用程序。这些实用程序包括位点过滤和分层选项，以及位点简并性注释和概率祖先等位基因推断。fastDFE 因此涵盖了从获取原始 VCF 文件到 DFE 推断的整个工作流程。尽管基于 Python，但 fastDFE 包含了完整的 R 接口，包括本地 R 可视化功能。该软件包在 fastdfe.readthedocs.io 上进行了全面测试和记录。
