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Fast Dating Using Least-Squares Criteria and Algorithms.

Thu-Hien To1, Matthieu Jung2, Samantha Lycett3

  • 1Institut de Biologie Computationnelle, LIRMM, UMR 5506 CNRS - Université de Montpellier, France;

Systematic Biology
|October 2, 2015
PubMed
Summary
This summary is machine-generated.

New algorithms rapidly date evolutionary events in large genetic datasets, offering a faster alternative to existing methods for viral phylogenetics. These tools accurately estimate substitution rates and ancestral node dates, even for trees with over 10,000 taxa.

Keywords:
Active-set methodalgorithmscomputer simulationsdatinginfluenza (H1N1)least-squareslinear algebramolecular clockserial datasubstitution rate estimationtemporal precedence constraintsviruses

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Area of Science:

  • Computational Biology
  • Evolutionary Genetics
  • Bioinformatics

Background:

  • Phylogenetic trees are crucial for understanding evolutionary history, with large datasets (e.g., human immunodeficiency virus) becoming increasingly common.
  • Accurate dating of ancestral events is essential but current sophisticated methods struggle with large-scale datasets.
  • Existing molecular clock models and rooting methods face computational limitations with increasing data size.

Purpose of the Study:

  • To develop and present novel, computationally efficient algorithms for dating ancestral events in large phylogenetic trees.
  • To provide a practical and fast alternative to existing sophisticated probabilistic and standard rooting methods.
  • To enable accurate estimation of substitution rates and ancestral node dates, even for datasets exceeding 10,000 taxa.

Main Methods:

  • Developed very fast dating algorithms based on a Gaussian model, closely related to the Langley-Fitch molecular-clock model.
  • Algorithms exploit the recursive tree structure and linear algebra for efficient computation, handling both rooted and unrooted trees.
  • Distinguished between unconstrained and temporally constrained settings, employing linear algebra and active-set methods for efficient solutions.

Main Results:

  • The new algorithms demonstrate computational speeds significantly faster than standard methods (e.g., root-to-tip, r8s, BEAST) while maintaining similar estimation accuracy.
  • Algorithms successfully process very large trees (>10,000 taxa) and transform them into time-scaled trees.
  • Application to Influenza virus data (1194 strains) confirmed the algorithms' speed and accuracy as a viable alternative.

Conclusions:

  • The presented least-squares dating (LSD) algorithms offer a robust, fast, and accurate solution for dating ancestral events in large-scale phylogenetic analyses.
  • These algorithms are particularly valuable for viral evolution studies and other fields dealing with massive genetic datasets.
  • The LSD software provides a practical tool for researchers, downloadable with comprehensive data and results.