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Related Concept Videos

Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
Point and Frameshift Mutations01:30

Point and Frameshift Mutations

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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Mutations01:39

Mutations

Overview
Mutations01:35

Mutations

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.
Chromosomal Alterations Are Large-Scale Mutations
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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The embedding problem for markov models of nucleotide substitution.

Klara L Verbyla1, Von Bing Yap, Anuj Pahwa

  • 1Computational Genomics Laboratory, John Curtin School of Medical Research, The Australian National University, Canberra, Australian. klara.verbyla@csiro.au

Plos One
|August 13, 2013
PubMed
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Markov models for sequence evolution can be non-embeddable, meaning they violate time-homogeneity assumptions. While detected, this violation minimally impacts phylogenetic predictions, suggesting its minor relevance in evolutionary studies.

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Genetics

Background:

  • Continuous-time Markov processes model sequence evolution.
  • Simplifying assumptions, like time-homogeneity, are often made for tractability.
  • The validity of the time-homogeneity assumption in Markov models for sequence evolution has not been previously explored.

Purpose of the Study:

  • To investigate the existence and implications of non-embeddability in continuous-time Markov models of sequence evolution.
  • To determine if sequence evolution models violate the time-homogeneity assumption.
  • To assess the impact of non-embeddability on phylogenetic reconstruction.

Main Methods:

  • Demonstrated non-embeddability in sequence evolution using Markov models.
  • Identified non-embeddability by testing for embeddability in a continuous time-homogeneous Markov process.
  • Analyzed evidence of non-embeddability at the third codon position, outgroup edges, and deeper time depths.
  • Examined individual edges of triads across diverse alignments.
  • Performed phylogenetic reconstruction analyses to evaluate the impact of non-embeddability.

Main Results:

  • Non-embeddability was demonstrated to exist in models of sequence evolution.
  • Evidence of non-embeddability was primarily found at the third codon position and on outgroup edges with deeper time depths.
  • Low levels of non-embeddability were detected across diverse alignments.
  • Non-embeddability was shown to potentially impact phylogenetic prediction, but at extremely low levels.

Conclusions:

  • While non-embeddability exists in sequence evolution models, violations of local time homogeneity are minimal.
  • The detected non-embeddability has a minor impact on phylogenetic reconstruction.
  • The time-homogeneity assumption in Markov models for sequence evolution is largely valid in practice.