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RY-Coding and Non-Homogeneous Models Can Ameliorate the Maximum-Likelihood Inferences From Nucleotide Sequence Data

Sohta A Ishikawa1, Yuji Inagaki, Tetsuo Hashimoto

  • 1Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan.

Evolutionary Bioinformatics Online
|July 17, 2012
PubMed
Summary

Phylogenetic analyses using homogeneous models can produce errors due to similar base frequencies. RY-coding and non-homogeneous models offer superior performance in phylogenetic analysis, accurately reflecting evolutionary relationships.

Keywords:
RY-codingcompositional heterogeneitylong-branch attractionmodel misspecificationnon-homogeneous model

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Area of Science:

  • Molecular Evolution
  • Bioinformatics
  • Computational Biology

Background:

  • Homogeneous phylogenetic models assume constant base composition, which can lead to inaccurate evolutionary trees when sequences have differing base frequencies.
  • Artifactual groupings can arise from parallel evolution of similar base compositions under homogeneous models.
  • RY-coding and non-homogeneous models are alternative approaches to address base composition heterogeneity in phylogenetic analyses.

Purpose of the Study:

  • To evaluate the performance of RY-coding and non-homogeneous models in phylogenetic analyses using simulated data.
  • To compare these methods against traditional homogeneous models when sequences exhibit parallel convergence in base composition.
  • To assess the robustness of non-homogeneous models with real-world sequence data exhibiting base heterogeneity.

Main Methods:

  • Simulated nucleotide sequence data with parallel convergence to similar base compositions were generated.
  • Maximum-likelihood phylogenetic analyses were conducted using homogeneous models, RY-coding, and non-homogeneous models.
  • Performance was assessed by comparing the accuracy of the resulting phylogenetic trees.

Main Results:

  • Both RY-coding and non-homogeneous analyses significantly outperformed homogeneous model-based analyses.
  • RY-coding analysis performance was sensitive to simulation parameters, unlike non-homogeneous analysis.
  • Non-homogeneous model performance was validated on a real dataset with substantial base heterogeneity.

Conclusions:

  • RY-coding and non-homogeneous models are effective strategies for improving phylogenetic accuracy when base composition varies.
  • Non-homogeneous models demonstrate greater robustness and are recommended for analyzing sequences with compositional heterogeneity.
  • Further investigation into the specific parameters influencing RY-coding performance is warranted.