Back to Search Start Over

ExRec : a python pipeline for generating recombination-filtered multi-locus datasets.

Authors :
McCarthy Potter S
Jennings WB
Source :
Bioinformatics advances [Bioinform Adv] 2023 Nov 29; Vol. 3 (1), pp. vbad174. Date of Electronic Publication: 2023 Nov 29 (Print Publication: 2023).
Publication Year :
2023

Abstract

Summary: ExRec (Exclusion of Recombined DNA) is a dependency-free Python pipeline that implements the four-gamete test to automatically filter out recombined DNA blocks from thousands of DNA sequence loci. This procedure helps all loci better meet the "no intralocus recombination" assumption common to many coalescent-based analyses in population genomic, phylogeographic, and shallow-scale phylogenomic studies. The user-friendly pipeline contains five standalone applications-four file conversion scripts and one main script that performs the recombination filtering procedures. The pipeline outputs recombination-filtered data in a variety of common formats and a tab-delimited table that displays descriptive statistics for all loci and the analysis results. A novel feature of this software is that the user can select whether to output the longest nonrecombined sequence blocks from recombined loci (current best practice) or randomly select nonrecombined blocks from loci (a newer approach). We tested ExRec with six published phylogenomic datasets that ranged in size from 27 to 2237 loci and came in a variety of input file formats. In all trials the data could be easily analyzed in only seconds for the smaller datasets and <30 min for the largest using a simple laptop computer.<br />Availability and Implementation: ExRec was written in Python 3 under the MIT license. The program applications, user manual (including step-by-step tutorials), and sample data are freely available at https://github.com/Sammccarthypotter/ExRec.<br />Competing Interests: None declared.<br /> (© The Author(s) 2023. Published by Oxford University Press.)

Details

Language :
English
ISSN :
2635-0041
Volume :
3
Issue :
1
Database :
MEDLINE
Journal :
Bioinformatics advances
Publication Type :
Academic Journal
Accession number :
38089112
Full Text :
https://doi.org/10.1093/bioadv/vbad174