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Related Experiment Videos

Spatial and temporal heterogeneity in nucleotide sequence evolution.

Simon Whelan1

  • 1Faculty of Life Sciences, University of Manchester, Michael Smith Building, Manchester M13 9PT, United Kingdom.

Molecular Biology and Evolution
|May 27, 2008
PubMed
Summary
This summary is machine-generated.

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Evolutionary models often oversimplify nucleotide substitution processes. This study introduces a temporal hidden Markov model (THMM) to reveal pervasive spatial and temporal heterogeneity in sequence evolution, improving phylogenetic accuracy.

Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Phylogenetics

Background:

  • Standard nucleotide substitution models assume uniform evolutionary processes across sites and lineages.
  • This uniformity assumption can lead to systematic errors in phylogenetic analyses due to real-world evolutionary heterogeneity.
  • Existing methods may not adequately distinguish between different types of heterogeneity.

Purpose of the Study:

  • To develop and rigorously assess a temporal hidden Markov model (THMM) for describing evolutionary heterogeneity.
  • To differentiate between among-site (spatial) and among-lineage (temporal) heterogeneity in nucleotide substitution.
  • To quantitatively evaluate the impact of different forms of heterogeneity on phylogenetic data.

Main Methods:

  • Application of several THMM versions to 16 diverse sets of aligned sequences.

Related Experiment Videos

  • Quantitative assessment of spatial rate heterogeneity (rates across sites, RAS) and temporal heterogeneity.
  • Model comparison to determine the best fit for describing evolutionary processes.

    • Main Results:

    • The most general THMM demonstrated the best fit across all datasets, confirming pervasive evolutionary heterogeneity.
    • Rates across sites (RAS) is the most prevalent form of heterogeneity, but its independent inclusion is crucial to reveal temporal heterogeneity.
    • Substantial temporal and spatial heterogeneity in nucleotide composition and substitution bias were detected, varying in importance across datasets.

    Conclusions:

    • The THMM framework effectively captures complex evolutionary heterogeneity, outperforming simpler models.
    • Failure to account for RAS can mask significant temporal heterogeneity, potentially leading to underestimation of evolutionary variability.
    • The identified heterogeneity, particularly temporal, poses challenges for deep phylogenetic analyses due to its weak correlation with divergence levels.