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

Sam McCarthy Potter1, W Bryan Jennings2

  • 1Department of Biology, Carleton College, Northfield, MN 55057, United States.

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|December 13, 2023
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Summary
This summary is machine-generated.

ExRec is a Python pipeline that filters recombined DNA blocks, improving data for population genomic and phylogenomic studies. It offers flexibility in handling recombined loci, ensuring more accurate analyses.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Coalescent-based analyses in population genomics, phylogeography, and phylogenomics often assume no intralocus recombination.
  • Filtering recombined DNA blocks is crucial for meeting this assumption and improving analytical accuracy.

Purpose of the Study:

  • To introduce ExRec, a user-friendly, dependency-free Python pipeline for automated filtering of recombined DNA blocks.
  • To provide a tool that enhances the quality of DNA sequence data for various population genetic studies.

Main Methods:

  • Implementation of the four-gamete test to identify and exclude recombined DNA blocks.
  • Development of a pipeline with five standalone applications, including file conversion and recombination filtering scripts.
  • Inclusion of a novel feature allowing users to select either the longest nonrecombined blocks or randomly selected nonrecombined blocks from recombined loci.

Main Results:

  • ExRec successfully filtered recombined DNA blocks from six diverse published phylogenomic datasets.
  • Analysis of datasets ranging from 27 to 2237 loci was efficient, completing in seconds to under 30 minutes on a standard laptop.
  • The pipeline outputs filtered data in multiple common formats and provides descriptive statistics.

Conclusions:

  • ExRec is an effective and efficient tool for removing recombined DNA, thereby improving the quality of data for population genomic and phylogenomic research.
  • The pipeline's user-friendly design, flexibility in handling recombined loci, and efficient performance make it a valuable resource for researchers.