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A Bayesian Mutation-Selection Framework for Detecting Site-Specific Adaptive Evolution in Protein-Coding Genes.

Nicolas Rodrigue1, Thibault Latrille2, Nicolas Lartillot2

  • 1Department of Biology, Institute of Biochemistry, and School of Mathematics and Statistics, Carleton University, Ottawa, Canada.

Molecular Biology and Evolution
|October 12, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian framework to detect adaptive evolution site-specifically in protein-coding genes. The method shows higher sensitivity than traditional approaches for identifying adaptive substitution regimes.

Keywords:
Dirichlet processMarkov chain Monte Carlofitness landscapenearly neutral evolution

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Area of Science:

  • Evolutionary biology
  • Genomics
  • Computational biology

Background:

  • Codon substitution models are used to detect adaptive evolution in genes.
  • Current methods often detect global adaptive signals or require experimental fitness data.

Purpose of the Study:

  • To develop a Bayesian site-heterogeneous mutation-selection framework for site-specific adaptive evolution detection.
  • To enable analysis of protein-coding DNA alignments without experimental fitness profiles.

Main Methods:

  • Developed a Bayesian site-heterogeneous mutation-selection model.
  • Implemented the framework for analysis of protein-coding DNA alignments.
  • Evaluated performance using simulations and real data sets.

Main Results:

  • The new framework demonstrates greater sensitivity in detecting adaptive substitution regimes compared to traditional methods.
  • Initial analyses on real data suggest improved detection capabilities.

Conclusions:

  • The proposed Bayesian framework offers a more sensitive approach for site-specific adaptive evolution detection.
  • Further research is needed to assess model violations and empirical behavior on diverse datasets.