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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

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Published on: August 14, 2018

Many-core algorithms for statistical phylogenetics.

Marc A Suchard1, Andrew Rambaut

  • 1Department of Biomathematics, University of California, Los Angeles, CA 90095, USA. msuchard@ucla.edu

Bioinformatics (Oxford, England)
|April 17, 2009
PubMed
Summary
This summary is machine-generated.

We developed new algorithms for graphics processing units (GPUs) to speed up phylogenetic analysis using codon models. This enables practical, large-scale phylogenetic inference for the first time.

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Published on: August 16, 2017

Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Genomics

Background:

  • Statistical phylogenetics is computationally intensive, hindering large-scale analyses.
  • Codon-based models offer improved phylogenetic accuracy but are computationally burdensome.
  • High codon state numbers limit phylogenetic reconstruction, especially at genomic scales.

Purpose of the Study:

  • To develop novel algorithms for evaluating phylogenies under molecular evolutionary models on GPUs.
  • To enable efficient parallelization of calculations for large state-size models.
  • To overcome computational limitations in phylogenetic reconstruction using codon models.

Main Methods:

  • Implemented novel algorithms within an existing Bayesian framework (BEAST).
  • Utilized the BEAGLE library for cross-platform phylogenetic likelihood computation.
  • Leveraged GPU parallel processing for accelerated calculations.

Main Results:

  • Achieved a near 90-fold speed increase over optimized CPU computation.
  • Demonstrated a >140-fold increase compared to existing implementations.
  • Enabled the first practical use of codon models for whole mitochondrial or microorganism genome phylogenetics.

Conclusions:

  • GPU-based parallelization significantly accelerates phylogenetic inference under codon models.
  • This approach makes large-scale phylogenetic analysis with codon models feasible.
  • Facilitates more accurate evolutionary studies of protein-coding sequences.