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Related Concept Videos

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

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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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The rate-determining step, or RDS, in a chemical reaction is the slowest step that determines the overall reaction rate. It is identified by using the observed rate law and typically involves approximation methods like the RDS approximation or the steady-state approximation.In the RDS approximation, also known as the rate-limiting-step or equilibrium approximation, the reaction mechanism consists of one or more reversible reactions near equilibrium, followed by a slower RDS, and then one or...
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Enzymes speed up reactions by lowering the activation energy of the reactants. The speed at which the enzyme turns reactants into products is called the rate of reaction. Several factors impact the rate of reaction, including the number of available reactants. Enzyme kinetics is the study of how an enzyme changes the rate of a reaction.
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Nonsynonymous substitution rate (Ka) is a relatively consistent parameter for defining fast-evolving and

Dapeng Wang1, Fei Liu, Lei Wang

  • 1CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, PR China.

Biology Direct
|February 24, 2011
PubMed
Summary

Comparative genomics reveals distinct gene evolution rates and functional specializations across mammalian clades. Ka calculation effectively sorts genes by evolution rate, aiding in understanding functional classes and interaction networks.

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Area of Science:

  • Evolutionary biology
  • Genomics
  • Bioinformatics

Background:

  • Mammalian genome sequencing generates vast data, offering opportunities for comparative genomics.
  • Understanding gene variability, conservation, and evolutionary dynamics is crucial.

Purpose of the Study:

  • To analyze orthologous protein-coding genes across mammalian genomes.
  • To investigate the relationship between gene evolution rates and functional classes.
  • To identify functional specializations of major mammalian clades.

Main Methods:

  • Comparative genomic analysis of human and eleven mammalian genomes, plus an avian outgroup.
  • Calculation and evaluation of nonsynonymous (Ka) and synonymous (Ks) substitution rates for orthologous genes.
  • Utilized eight common methods for Ka and Ks calculation, assessing their uniformity.

Main Results:

  • Ka calculation showed more uniform results than Ks or Ka/Ks across methods.
  • Fast-evolving genes (immune system signal transducers) and slow-evolving genes (immune function modulators, CNS genes) were identified.
  • Gene expression negatively correlated with evolution rate; slow-evolving genes had higher expression.
  • Functional specializations identified: primates (sensory perception, oncogenesis), large mammals (reproduction, hormone regulation), rodents (immunity, angiotensin).

Conclusions:

  • Ka is a reliable parameter for sorting genes by evolution rate and categorizing protein functions.
  • Gene evolution analysis using Ka and Ks is feasible with large mammalian genome datasets.
  • This approach aids in defining gene interaction networks within lineages or subgroups.