FastDFE: Fast and Flexible Inference of the Distribution of Fitness Effects

Janek Sendrowski*, Thomas Bataillon

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Abstract

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.

Original languageEnglish
Article numbermsae070
JournalMolecular Biology and Evolution
Volume41
Issue5
ISSN0737-4038
DOIs
Publication statusPublished - May 2024

Keywords

  • ancestral-Allele annotation
  • distribution of fitness effects
  • genomics
  • site-degeneracy annotation
  • site-frequency spectrum
  • software

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