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Related Concept Videos

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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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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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
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Beyond Genomics: Studying Evolution with Gene Coexpression Networks.

Colin Ruprecht1, Neha Vaid2, Sebastian Proost2

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Gene coexpression networks offer new ways to study how biological pathways evolve alongside organismal complexity. Merging these networks with genomic data allows functional knowledge transfer between species.

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Area of Science:

  • Evolutionary biology
  • Genomics
  • Systems biology

Background:

  • Understanding genome evolution and the emergence of biological complexity is a key evolutionary question.
  • Traditional molecular evolution studies link gene/family appearance to pathway and morphological evolution.
  • These methods are limited as functionally related genes operate within dynamic gene networks.

Purpose of the Study:

  • To discuss recent advancements in using gene coexpression networks for studying pathway evolution.
  • To explore integrating coexpression analyses with genomic approaches.
  • To enable functional knowledge transfer across species for pathway evolution studies.

Main Methods:

  • Analyzing gene coexpression networks to understand gene regulatory relationships.
  • Integrating coexpression data with existing genomic datasets.
  • Comparative genomics approaches to infer pathway evolution.

Main Results:

  • Gene coexpression networks provide insights into the evolution of functionally related genes.
  • Integrating coexpression and genomic data facilitates the study of pathway emergence and expansion.
  • This approach allows for the transfer of functional annotations and pathway knowledge between species.

Conclusions:

  • Gene coexpression networks are a valuable resource for studying the evolution of biological pathways.
  • Combining coexpression analysis with genomic methods enhances our understanding of evolutionary processes.
  • This integrated approach aids in deciphering how biological complexity arises through pathway evolution.