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Evolutionary Relationships through Genome Comparisons

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

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Published on: August 14, 2018

Hidden Markov models for evolution and comparative genomics analysis.

Nadezda A Bykova1, Alexander V Favorov, Andrey A Mironov

  • 1A.A. Kharkevich Institute for Information Transmission Problems RAS, Moscow, Russia. nadya@bioinf.fbb.msu.ru

Plos One
|June 14, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a tree hidden Markov model (tHMM) to reconstruct ancestral biological states, even with uncertain extant species data. The tHMM approach improves accuracy in evolutionary analyses.

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Published on: December 10, 2012

Area of Science:

  • Evolutionary biology
  • Computational biology
  • Bioinformatics

Background:

  • Reconstructing ancestral states from extant species data is crucial in evolutionary studies.
  • Continuous-time Markov models are standard for discrete state evolution but struggle with uncertain data.
  • Bioinformatic predictions often yield uncertain state assignments for extant species.

Purpose of the Study:

  • To develop a novel method for ancestral state reconstruction that accounts for uncertainty in extant species data.
  • To adapt existing Markov models for phylogenetic analysis to handle probabilistic data.
  • To enhance the accuracy of inferring evolutionary history in the presence of prediction errors.

Main Methods:

  • Formulation of the problem as a tree hidden Markov model (tHMM).
  • Expansion of the continuous-time Markov model with emission probabilities for discrete states.
  • Development of a tHMM decoding algorithm for ancestral and extant state prediction.
  • Application of the model to continuous variables representing state probabilities (e.g., prediction scores).

Main Results:

  • The tHMM approach demonstrates higher accuracy compared to methods relying on discrete state assignments.
  • The decoding algorithm effectively predicts ancestral states and refines extant states using quantitative comparative genomics.
  • Successful application to evolutionary analysis of N-terminal signal peptides and transcription factor binding sites in bacteria.

Conclusions:

  • The tree hidden Markov model (tHMM) provides a robust framework for ancestral state reconstruction with uncertain data.
  • This method offers improved accuracy and refinement capabilities for phylogenetic and comparative genomics studies.
  • The developed tHMM approach and software are valuable tools for evolutionary and bioinformatics research.