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Related Concept Videos

Point and Frameshift Mutations01:30

Point and Frameshift Mutations

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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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Per-Unit Sequence Models01:26

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Related Experiment Videos

Estimating empirical codon hidden Markov models.

Nicola De Maio1, Ian Holmes, Christian Schlötterer

  • 1Institut für Populationsgenetik, Vetmeduni Vienna, Wien, Austria. nicola.de.maio.85@gmail.com

Molecular Biology and Evolution
|November 29, 2012
PubMed
Summary
This summary is machine-generated.

New empirical codon models (ECMs) and ecHMMs from Drosophila genomes improve evolutionary analysis. These models enhance tests for positive selection, reducing false positives caused by multiple nucleotide substitutions (MNS).

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Area of Science:

  • Evolutionary biology
  • Genomics
  • Molecular evolution

Background:

  • Empirical codon models (ECMs) outperform mechanistic models in describing protein evolution.
  • ECMs implicitly account for amino acid properties and multiple nucleotide substitutions (MNS).
  • Estimating ECMs traditionally required large datasets, which were previously scarce.

Purpose of the Study:

  • To leverage new Drosophila genomes for estimating ECMs from genome-wide data.
  • To determine optimal divergence levels for ECM estimation, mitigating MNS overestimation.
  • To develop novel empirical codon hidden Markov models (ecHMMs) accounting for rate variation.

Main Methods:

  • Utilized multiple Drosophila species genomes for genome-wide data analysis.
  • Developed and implemented empirical codon hidden Markov models (ecHMMs).
  • Devised new likelihood ratio tests (LRTs) for positive selection based on ECMs and ecHMMs.

Main Results:

  • ECMs and ecHMMs derived from genome-wide data show improved fit to evolutionary processes.
  • New LRTs demonstrate reduced false-positive rates compared to classical LRTs, especially in small phylogenies.
  • The developed ecHMMs significantly outperform previous models based on maximum likelihood values.

Conclusions:

  • Genome-wide data from Drosophila enables robust estimation of ECMs and ecHMMs.
  • The new LRTs offer a more reliable method for detecting positive selection, addressing MNS-related biases.
  • These advancements provide better tools for studying molecular evolution and detecting selection pressures.