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A Practical Guide to Phylogenetics for Nonexperts
12:00

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Published on: February 5, 2014

Sufficient statistics and expectation maximization algorithms in phylogenetic tree models.

Hisanori Kiryu1

  • 1Department of Computational Biology, Faculty of Frontier Science, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan. kiryu-h@k.u-tokyo.ac.jp

Bioinformatics (Oxford, England)
|July 16, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel statistical method using fractional nucleotide duration (F(d)) to identify evolutionary constraints in genomes. This approach offers distinct evolutionary information compared to substitution counts, aiding in discovering new functional elements.

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Area of Science:

  • Genomics and Evolutionary Biology
  • Computational Biology and Bioinformatics

Background:

  • Evolutionary conservation analysis is crucial for identifying functional genomic elements.
  • Continuous time Markov models (CTMMs) are used for this, but their probabilistic structures are underexplored.

Purpose of the Study:

  • To investigate a sufficient statistic for CTMMs, focusing on fractional nucleotide duration (F(d)) and substitution counts (N(s)).
  • To develop and evaluate an expectation maximization (EM) algorithm for phylogenetic model parameter estimation.
  • To explore the genome-wide distribution and evolutionary information content of F(d).

Main Methods:

  • Derived basic properties of a sufficient statistic comprising fractional nucleotide duration (F(d)) and substitution counts (N(s)).
  • Developed an expectation maximization (EM) algorithm for estimating CTMM parameters, iteratively computing sufficient statistic expectations.
  • Analyzed the genome-wide distribution of F(d) and compared its evolutionary information to N(s).

Main Results:

  • The derived EM algorithm converges significantly faster than numerical gradient descent methods.
  • Fractional duration F(d) provides evolutionary information distinct from substitution counts N(s).
  • F(d) analysis reveals potential for detecting novel types of evolutionary constraints in the human genome.

Conclusions:

  • The proposed sufficient statistic and EM algorithm offer an efficient method for phylogenetic modeling.
  • Fractional nucleotide duration (F(d)) is a valuable metric for uncovering previously unrecognized evolutionary constraints.
  • This approach enhances the identification of functional elements within genome sequences.