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A Practical Guide to Phylogenetics for Nonexperts
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BEAGLE: an application programming interface and high-performance computing library for statistical phylogenetics.

Daniel L Ayres1, Aaron Darling, Derrick J Zwickl

  • 1Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA. ayres@umiacs.umd.edu

Systematic Biology
|October 4, 2011
PubMed
Summary
This summary is machine-generated.

BEAGLE is a new library accelerating statistical phylogenetic inference using graphics processing units (GPUs) and other hardware. This high-performance tool enhances evolutionary biology research by enabling analysis of large datasets.

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Area of Science:

  • Evolutionary Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Statistical phylogenetic inference is crucial for understanding life's evolution.
  • Current methods are computationally intensive, limiting large dataset analysis.
  • High-throughput sequencing generates vast data, demanding faster computational approaches.

Purpose of the Study:

  • To develop a high-performance library for statistical phylogenetic inference.
  • To leverage modern hardware, including GPUs, for accelerated calculations.
  • To provide a common API for diverse computational architectures.

Main Methods:

  • Introduction of the BEAGLE ( [Bio-Enhanced Analysis of GENomic data at Large scale] ) library and API.
  • Implementation of efficient phylogenetic likelihood calculations.
  • Support for multiple hardware platforms: GPUs (NVIDIA CUDA), CPUs (SSE), and multicore CPUs (OpenMP).

Main Results:

  • BEAGLE significantly accelerates statistical phylogenetic inference.
  • The library integrates seamlessly with existing phylogenetic software packages.
  • Demonstrated efficient utilization of GPUs and multicore CPUs.

Conclusions:

  • BEAGLE offers a powerful solution for computationally demanding phylogenetic analyses.
  • The library facilitates the use of advanced hardware for evolutionary studies.
  • BEAGLE promotes wider adoption of efficient computational methods in bioinformatics.