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Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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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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Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
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Detecting Adaptation in Protein-Coding Genes Using a Bayesian Site-Heterogeneous Mutation-Selection Codon

Nicolas Rodrigue1, Nicolas Lartillot2

  • 1Department of Biology, Institute of Biochemistry, and School of Mathematics and Statistics, Carleton University, Ottawa, Canada nicolas.rodrigue@carleton.ca.

Molecular Biology and Evolution
|October 17, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a mutation-selection framework as a null model for detecting adaptation in protein-coding genes. The model effectively identifies evolutionary adaptation with high accuracy and a low false-positive rate.

Keywords:
Dirichlet processMarkov chain Monte Carloepistasisfitness landscapenearly neutral evolution

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Genomics

Background:

  • Traditional codon substitution models focus on synonymous vs. non-synonymous rates.
  • Mutation-selection models explicitly account for amino acid fitness and site-specific variation.

Purpose of the Study:

  • To evaluate a mutation-selection framework as a null model for detecting evolutionary adaptation.
  • To assess the framework's performance in identifying deviations from neutral evolution.

Main Methods:

  • Utilized a mutation-selection model incorporating a Dirichlet process for across-site amino acid fitness heterogeneity.
  • Introduced a deviation parameter to quantify non-synonymous rate surpluses or deficits.
  • Validated the approach using simulations and real biological datasets.

Main Results:

  • The mutation-selection framework demonstrated good statistical power for detecting adaptation.
  • The model exhibited a low false-positive rate, indicating reliability.
  • The approach effectively captures deviations from expected evolutionary patterns.

Conclusions:

  • Mutation-selection models show significant potential for robust adaptation detection in protein-coding genes.
  • Further refinement and large-scale application of these models are warranted.
  • This framework offers a powerful tool for evolutionary genomic analyses.