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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Many-core algorithms for high-dimensional gradients on phylogenetic trees.

Karthik Gangavarapu1, Xiang Ji2, Guy Baele3

  • 1Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, United States.

Bioinformatics (Oxford, England)
|January 20, 2024
PubMed
Summary
This summary is machine-generated.

We developed faster GPU algorithms for phylogenetic analysis, significantly speeding up infectious disease monitoring. These methods enable previously intractable evolutionary inferences for large datasets.

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Area of Science:

  • Genomics
  • Computational Biology
  • Evolutionary Biology

Background:

  • High-throughput sequencing generates vast pathogen genomic data, necessitating efficient phylogenetic analysis for global infectious disease monitoring.
  • Hamiltonian Monte Carlo (HMC) is a key Bayesian inference technique, but its computational cost increases with dataset size (N).
  • Calculating the gradient of the log-likelihood, crucial for HMC, traditionally requires O(N^2) operations, limiting its application.

Purpose of the Study:

  • To develop and implement novel, massively parallel algorithms for calculating the gradient of the log-likelihood with respect to branch-length-specific parameters.
  • To leverage graphics processing units (GPUs) to accelerate these calculations, overcoming limitations of previous CPU-based approaches for complex models.
  • To enable more efficient and scalable phylogenetic inference for large genomic datasets.

Main Methods:

  • Developed massively parallel algorithms for computing the gradient of the log-likelihood wrt branch-length-specific parameters.
  • Implemented these algorithms on graphics processing units (GPUs) for significant computational speedup.
  • Integrated the GPU algorithms into the BEAGLE library (v4.0.0) for broad accessibility in statistical phylogenetics.

Main Results:

  • Achieved >128-fold speedup for codon-based models and >8-fold speedup for nucleotide-based models compared to CPU implementations.
  • Demonstrated the practical utility by estimating the introduction timing of West Nile virus in the US, an previously intractable task.
  • Successfully benchmarked algorithms on diverse datasets including dengue viruses, carnivore mitochondria, and yeasts.

Conclusions:

  • The novel GPU algorithms provide substantial computational acceleration for phylogenetic inference.
  • These advancements make complex phylogenetic models and large-scale genomic datasets computationally tractable.
  • The implementation in BEAGLE facilitates wider adoption and application in infectious disease dynamics and evolutionary studies.