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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Exploring phylogenetic hypotheses via Gibbs sampling on evolutionary networks.

Yun Yu1, Christopher Jermaine1, Luay Nakhleh2,3

  • 1Department of Computer Science, Rice University, Houston, Texas, 77005, USA.

BMC Genomics
|February 11, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using Gibbs sampling to explore complex evolutionary histories with phylogenetic networks. This approach allows simultaneous modeling of reticulate and non-reticulate evolutionary processes for genomic data.

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Genomics

Background:

  • Phylogenetic networks model complex evolutionary relationships beyond single trees.
  • Evolutionary networks accommodate probabilistic models but lack exploration tools.
  • Data-display networks are used for exploration but not probabilistic modeling.

Purpose of the Study:

  • To develop a method for statistical exploration of phylogenetic hypotheses using evolutionary networks.
  • To enable the incorporation of probabilistic models into phylogenetic network exploration.
  • To address limitations of current phylogenetic network applications.

Main Methods:

  • Application of Gibbs sampling to evolutionary networks.
  • Development of a novel statistical exploration technique.
  • Testing on real genomic data sets (mosquitoes, birds) and simulated data.

Main Results:

  • Demonstrated the utility of evolutionary networks for statistical exploration.
  • Enabled simultaneous modeling of reticulate and non-reticulate evolutionary processes.
  • Showcased performance on diverse genomic and simulated data sets.

Conclusions:

  • Introduced a Gibbs sampling approach for exploring phylogenetic hypotheses on evolutionary networks.
  • Facilitated simultaneous analysis of reticulate and non-reticulate evolutionary processes.
  • Validated the method on mosquito and bird genomic data, highlighting its broad applicability.