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Detecting signatures of selection from DNA sequences using Datamonkey.

Art F Y Poon1, Simon D W Frost, Sergei L Kosakovsky Pond

  • 1Antiviral Research Center, Department of Pathology, University of California San Diego, La Jolla, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|April 21, 2009
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Natural selection shapes populations by favoring beneficial alleles and removing harmful ones. Codon-based models analyze DNA sequences to detect these selection pressures, aiding evolutionary studies.

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Area of Science:

  • Evolutionary Biology
  • Molecular Evolution
  • Bioinformatics

Background:

  • Natural selection is a core mechanism driving evolution in all populations.
  • Selection can be positive (favoring advantageous alleles) or negative (removing deleterious alleles).
  • Codon-based models are crucial for inferring selection from molecular sequence data.

Purpose of the Study:

  • To estimate rates of synonymous (dS) and non-synonymous (dN) substitutions.
  • To identify specific codons and lineages experiencing selection.
  • To provide accessible tools for analyzing selection in molecular sequences.

Main Methods:

  • Utilizing codon-based models of molecular evolution.
  • Estimating the dN/dS ratio from homologous sequence alignments.
  • Employing statistical techniques to detect selection signatures.
  • Leveraging the DataMonkey web server for analysis.

Main Results:

  • Successfully estimated dS and dN rates across various sequences.
  • Identified codons and lineages under significant positive or negative selection.
  • Demonstrated the utility of DataMonkey for selection analysis, including with recombinant sequences.

Conclusions:

  • Codon-based models effectively infer selection from sequence data.
  • DataMonkey offers a user-friendly platform for advanced evolutionary selection analyses.
  • These methods are vital for understanding evolutionary processes at the molecular level.