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Bioinformatics Protocols for Quickly Obtaining Large-Scale Data Sets for Phylogenetic Inferences.

Hugo López-Fernández1,2,3,4,5, Pedro Duque6,7,8, Sílvia Henriques6,7

  • 1ESEI -Escuela Superior de Ingeniería Informática, Universidad de Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004, Ourense, Spain. hlfernandez@uvigo.es.

Interdisciplinary Sciences, Computational Life Sciences
|December 5, 2018
PubMed
Summary

Analyzing diverse genome data with SEDA software reveals gene evolution insights. New protocols streamline phylogenetic analysis for GULO and primate HLA genes, improving accuracy and efficiency in evolutionary studies.

Keywords:
AnimalsGULOHLALarge scale analysesPositive selectionSEDA

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Area of Science:

  • Genomics and Bioinformatics
  • Evolutionary Biology
  • Computational Biology

Background:

  • Analyzing large-scale genome datasets is crucial for understanding gene and gene family evolution.
  • Researchers often face challenges with data preparation for phylogenetic analyses due to informatics complexities.
  • Existing methods for data transformation can be time-consuming and require specialized skills.

Purpose of the Study:

  • To present simple, fast, and user-friendly protocols for preparing genomic data for phylogenetic analysis.
  • To improve upon existing methods for data preparation, making complex analyses more accessible.
  • To demonstrate the utility of these protocols in uncovering evolutionary insights in specific gene families.

Main Methods:

  • Utilized the SEDA (Software for Empirical Data Analysis) software with a graphical user interface.
  • Developed and applied two distinct protocols for data preparation: one for GULO gene evolution and another for primate HLA genes.
  • Implemented a new SEDA operation, "Remove isoforms," to expedite sequence data file generation.

Main Results:

  • Successfully identified putative functional GULO genes in diverse Prostomian groups (Molluscs, Priapulida, Arachnida) by analyzing comprehensive datasets.
  • Identified positively selected amino acid sites in primate HLA immunity genes, including MHC class II, comparable to MHC class I genes.
  • Demonstrated that comprehensive species sampling reduces the probability of missing key evolutionary findings.

Conclusions:

  • The developed SEDA protocols significantly accelerate data preparation for phylogenetic analyses.
  • These protocols enhance the ability to detect evolutionary patterns, such as the presence of functional genes and positive selection, across broad taxonomic groups.
  • The user-friendly nature of SEDA makes advanced phylogenetic analyses more accessible to researchers without extensive bioinformatics backgrounds.