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A rapid bootstrap algorithm for the RAxML Web servers.

Alexandros Stamatakis1, Paul Hoover, Jacques Rougemont

  • 1Exelixis Lab, Teaching and Research Unit Bioinformatics, Department of Computer Science, Ludwig-Maximilians-University Munich, Munich, Germany. stamatakis@bio.ifi.lmu.de

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Summary
This summary is machine-generated.

Researchers developed rapid bootstrap heuristics in RAxML for faster phylogenetic analyses. These new methods significantly reduce computation time for bootstrap support values, improving large-scale phylogenetic inference efficiency.

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Bioinformatics

Background:

  • Maximum likelihood (ML)-based inference in phylogenetics is computationally intensive.
  • Calculating bootstrap support values is a major bottleneck, often requiring extensive computation time for large datasets.

Purpose of the Study:

  • To develop and implement rapid bootstrap heuristics to accelerate phylogenetic analyses.
  • To address the computational bottleneck in calculating bootstrap support values for large-scale phylogenetics.

Main Methods:

  • Developed and implemented rapid bootstrap (RBS) heuristics within the RAxML (Randomized Axelerated Maximum Likelihood) program.
  • Conducted computational experiments on 22 diverse DNA and amino acid alignments (125-7764 sequences) comparing RBS with standard bootstrap (SBS) and other programs (PHYML, GARLI).
  • Established two accessible Web servers for the improved RAxML version.

Main Results:

  • RBS inferences are 8-20 times faster than SBS analyses in RAxML and significantly faster than competing programs.
  • RBS and SBS values are highly correlated, with weighted Robinson-Foulds distances between consensus trees averaging only 4%.
  • Performance improvement of RBS increases with alignment size.

Conclusions:

  • Rapid bootstrap heuristics in RAxML significantly accelerate phylogenetic analyses.
  • The new methods provide accurate support values comparable to standard methods.
  • Accessible Web servers democratize large-scale phylogenetic inference for researchers without high-performance computing access or expertise.