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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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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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).
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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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Related Experiment Video

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

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Published on: February 3, 2023

Efficient algorithms for reconstructing gene content by co-evolution.

Hadas Birin1, Tamir Tuller

  • 1School of Computer Science, Tel Aviv University, Israel.

BMC Bioinformatics
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces efficient algorithms for the Ancestral Co-Evolutionary (ACE) problem, improving gene content reconstruction. The findings show these methods can infer current genomes and identify key genes for accurate reconstruction.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Previous work demonstrated co-evolutionary information enhances ancestral gene content reconstruction accuracy.
  • The Ancestral Co-Evolutionary (ACE) problem was defined and initial algorithms were developed.

Purpose of the Study:

  • To generalize and improve computational approaches for the ACE problem.
  • To explore computational hardness and identify practical polynomial-time solvable cases.
  • To extend the ACE problem for inferring genomes of extant organisms.

Main Methods:

  • Developed new efficient algorithms for the ACE problem via reductions to linear programming relaxation, quadratic programming, and min-cut.
  • Investigated computational hardness, including identifying practical polynomial-time solvable scenarios.
  • Designed a heuristic using evolutionary and co-evolutionary information to identify a 'dominant set' for genome reconstruction.

Main Results:

  • Implemented algorithms on a large dataset (95 organisms, 4,873 protein families), outperforming previous methods.
  • Demonstrated that a 'dominant set' can reconstruct up to 79% of a genome with a low error rate (0.11).
  • Identified 'dominant sets' as enriched in metabolic and regulatory genes with high evolutionary rates and low protein abundance/interactions.

Conclusions:

  • The ACE problem framework is efficiently extendable for inferring extant organism genomes.
  • The ACE problem is solvable in polynomial time for many practical instances.
  • Metabolic and regulatory genes are crucial for reconstructing gene content based on related genomes.