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Algorithms for MDC-based multi-locus phylogeny inference: beyond rooted binary gene trees on single alleles.

Yun Yu1, Tandy Warnow, Luay Nakhleh

  • 1Department of Computer Science, Rice University, Houston, Texas 77005, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|November 1, 2011
PubMed
Summary

This study introduces new methods for constructing species trees from gene trees, addressing challenges like incomplete lineage sorting (ILS). The new algorithms improve species tree inference accuracy even with complex gene tree data.

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Area of Science:

  • Phylogenetics
  • Computational Biology
  • Evolutionary Genetics

Background:

  • Species tree inference is crucial for understanding evolutionary relationships.
  • Incomplete lineage sorting (ILS) is a major challenge in inferring species trees from gene trees.
  • Existing algorithms for species tree inference under Minimize Deep Coalescence (MDC) are limited to rooted, binary gene trees.

Purpose of the Study:

  • To extend Minimize Deep Coalescence (MDC) methods for species tree inference to handle unrooted, non-binary, and multi-allele gene tree scenarios.
  • To develop new MDC formulations and algorithms applicable to a wider range of real-world phylogenetic data.
  • To evaluate the performance of the proposed methods using coalescent-based simulations.

Main Methods:

  • Developed novel formulations for Minimize Deep Coalescence (MDC) applicable to unrooted/binary, rooted/non-binary, and unrooted/non-binary gene trees.
  • Proved structural theorems to adapt existing algorithms for rooted/binary gene trees to the newly formulated cases.
  • Designed MDC-based algorithms to accommodate sampling of multiple alleles per species.
  • Conducted coalescent-based computer simulations to assess method performance.

Main Results:

  • Successfully extended MDC-based species tree inference algorithms to accommodate unrooted, non-binary, and multi-allele gene tree datasets.
  • Demonstrated the straightforward applicability of existing algorithms to these more complex scenarios through proven theorems.
  • Validated the performance of the new methods via simulations.

Conclusions:

  • The proposed MDC formulations and algorithms significantly enhance the robustness and applicability of species tree inference methods.
  • These advancements provide powerful tools for phylogenetic analysis, particularly when dealing with gene tree incongruence arising from ILS.
  • The developed methods offer improved accuracy in reconstructing evolutionary histories from diverse biological datasets.