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Related Experiment Videos

Hierarchical phylogenetic models for analyzing multipartite sequence data.

Marc A Suchard1, Christina M R Kitchen, Janet S Sinsheimer

  • 1Department of Biomathematics, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California 90095-1766, USA. msuchard@ucla.edu

Systematic Biology
|October 8, 2003
PubMed
Summary

We introduce a Bayesian hierarchical phylogenetic model to improve phylogenetic analysis of multipartite sequence data. This method enhances estimate precision by pooling information across partitions, offering a balanced approach to phylogenetic inference.

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Evolutionary Biology

Background:

  • Phylogenetic analyses of multipartite sequence data face challenges in integrating information from different partitions.
  • Existing methods include strict combined-data (concatenation) and consensus/independence approaches, with mixtures offering a compromise.
  • A need exists for methods that pool information across partitions while allowing for partition-specific variation.

Purpose of the Study:

  • To propose a Bayesian hierarchical phylogenetic model as an alternative approach for analyzing multipartite sequence data.
  • To improve the precision of phylogenetic estimates by pooling information across data partitions.
  • To enable the estimation and testing of across-partition quantities, such as shared evolutionary histories or parameters.

Main Methods:

Related Experiment Videos

  • Construction of a Bayesian hierarchical phylogenetic model.
  • Application of standard hierarchical priors on continuous evolutionary parameters across partitions.
  • Illustration with three case studies: guinea pig mitochondrial genes, prokaryotic genes (horizontal gene transfer), and longitudinal HIV sequences.

Main Results:

  • The hierarchical model yielded substantially more precise continuous parameter estimates for guinea pig mitochondrial data compared to an independent parameter approach.
  • Analysis of prokaryotic genes estimated a 17% frequency of horizontal gene transfer when simultaneously inferring species and gene topologies.
  • Preliminary analysis of HIV sequences suggests post-treatment CCR5 virus evolution rather than reemergence of initial CCR5 virus.

Conclusions:

  • The proposed Bayesian hierarchical model offers a robust framework for phylogenetic inference from multipartite sequence data.
  • This approach effectively pools information across partitions, enhancing estimate precision and allowing for the investigation of across-partition patterns.
  • The model demonstrates utility across diverse biological systems, including evolutionary history, horizontal gene transfer, and viral evolution.