Calculating Site-Specific Evolutionary Rates at the Amino-Acid or Codon Level Yields Similar Rate Estimates

Abstract

Site-specific evolutionary rates can be estimated from codon sequences or from amino-acid sequences. For codon sequences, the most popular methods use some variation of the dN=dS ratio. For amino-acid sequences, one widely-used method is called Rate4Site,and it assigns a relative conservation score to each site in an alignment. How site-wise dN=dS values relate to Rate4Site scores is not known. Here we elucidate the relationship between these two rate measurements. We simulate sequences with known dN=ds, using either dN=dS models or mutation selection models for simulation. We then infer Rate4Site scores on the simulated alignments, and we compare those scores to either true or inferred dN=dS values on the same alignments. We find that Rate4Sitescores generally correlate well with true dN=dS, and the correlation strengths increase in alignments with greater sequence divergence and more taxa. Moreover, Rate4Sitescores correlate very well with inferred (as opposed to true) dN=dS values, even for small alignments with little divergence. Finally, we verify this relationship betweenRate4Site and dN=dS in a variety of empirical datasets. We conclude that codon-level and amino-acid-level analysis frameworks are directly comparable and yield very similar inferences.

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Document Details

Document Type
Technical Report
Publication Date
May 30, 2017
Accession Number
AD1057629

Entities

People

  • Claus O. Wilke
  • Dariya K. Sydykova

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DTIC Thesaurus Topics

  • Amino Acids
  • Bayesian Inference
  • Bayesian Networks
  • Biological Sciences
  • Biology
  • Chemistry
  • Computational Biology
  • Computational Science
  • Computer Programming
  • Genetics
  • Information Science
  • Membrane Proteins
  • Models
  • Molecular Biology
  • Proteins
  • Simulations
  • Three Dimensional

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  • Biology

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  • Computational Modeling and Simulation
  • Marine Ecotoxicology
  • Molecular Genetics

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  • AI & ML
  • AI & ML - Bayesian Inference