TY - JOUR
T1 - FastDFE
T2 - Fast and Flexible Inference of the Distribution of Fitness Effects
AU - Sendrowski, Janek
AU - Bataillon, Thomas
N1 - Publisher Copyright:
© 2024 The Author(s). Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.
PY - 2024/5
Y1 - 2024/5
N2 - 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.
AB - 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.
KW - ancestral-Allele annotation
KW - distribution of fitness effects
KW - genomics
KW - site-degeneracy annotation
KW - site-frequency spectrum
KW - software
UR - http://www.scopus.com/inward/record.url?scp=85194098268&partnerID=8YFLogxK
U2 - 10.1093/molbev/msae070
DO - 10.1093/molbev/msae070
M3 - Journal article
C2 - 38577958
AN - SCOPUS:85194098268
SN - 0737-4038
VL - 41
JO - Molecular Biology and Evolution
JF - Molecular Biology and Evolution
IS - 5
M1 - msae070
ER -