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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
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Phylogeny

Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire...
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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Published on: August 14, 2018

Efficient computation of the phylogenetic likelihood function on multi-gene alignments and multi-core architectures.

Alexandros Stamatakis1, Michael Ott

  • 1Department of Computer Science The Exelixis Lab, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany. alexandros.stamatakis@gmail.com

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|October 15, 2008
PubMed
Summary

This study introduces two methods to speed up phylogenetic maximum-likelihood (ML) calculations, crucial for analyzing large sequence datasets. These innovations accelerate computations on gappy alignments and optimize performance on multi-core processors.

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Bioinformatics

Background:

  • Increasing sequence data and multi-gene phylogenies challenge efficient phylogenetic maximum-likelihood (ML) computation.
  • Current ML and Bayesian inference programs spend over 95% of computational effort on likelihood calculations.
  • Emerging multi-core architectures present cache congestion issues impacting performance.

Purpose of the Study:

  • To propose novel methods for significantly accelerating phylogenetic likelihood computations.
  • To address computational challenges posed by large, gappy multi-gene alignments.
  • To optimize ML function performance on multi-core processor architectures.

Main Methods:

  • Developed a method and data structure for efficient likelihood scoring on 'gappy' multi-gene alignments (sampling-induced gaps).
  • Implemented a proof-of-concept in RAXML to test the gappy alignment approach.
  • Investigated fine-grained parallelization of the ML function, transitioning from OpenMP to Pthreads for multi-core architectures.

Main Results:

  • The gappy alignment method demonstrated an approximate tenfold acceleration for large, gappy alignments in RAXML.
  • Initial performance insights and results were obtained for multi-core architectures.
  • The transition to Pthreads-based parallelization showed promise for optimizing multi-core performance.

Conclusions:

  • The proposed methods offer significant speedups for phylogenetic likelihood computations.
  • Efficient handling of gappy alignments is critical for large-scale phylogenetic inference.
  • Further optimization for multi-core architectures is essential for advancing computational phylogenetics.