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Genomic Fishing and Data Processing for Molecular Evolution Research.

Héctor Lorente-Martínez1, Ainhoa Agorreta1, Diego San Mauro1

  • 1Department of Biodiversity, Ecology and Evolution, Complutense University of Madrid, 28040 Madrid, Spain.

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|March 22, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a workflow to streamline molecular evolution research by automating the preparation of gene sequence datasets. It simplifies the process of obtaining and aligning sequences for phylogenetic analysis and evolutionary studies.

Keywords:
blast searchdata mininggene familygenomicshigh-throughput sequencingmolecular evolutionphylogenysequence alignment

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Area of Science:

  • Genomics
  • Molecular Evolution
  • Bioinformatics

Background:

  • Molecular evolution studies require sequence alignment, evolutionary models, and phylogenetic trees.
  • High-throughput sequencing has generated vast genomic data, but accessing and preparing it for research is challenging.

Purpose of the Study:

  • To present a comprehensive and versatile workflow for preparing genome-extracted datasets for molecular evolution research.
  • To facilitate the mining of genomic data for evolutionary analyses.

Main Methods:

  • Sequence data retrieval ('fishing') from diverse genomic databases.
  • Sequence processing, depuration, and multiple sequence alignment.
  • Best-fit model selection and phylogenetic tree reconstruction.

Main Results:

  • A streamlined workflow for generating essential inputs for molecular evolution analyses.
  • Efficient extraction and preparation of gene sequences from large genomic datasets.

Conclusions:

  • The described workflow simplifies and accelerates the preparation of datasets for molecular evolution research.
  • This approach enhances the accessibility of genomic data for evolutionary studies.