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Related Concept Videos

Conservative Site-specific Recombination and Phase Variation02:53

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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
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Crossing over is the exchange of genetic information between homologous chromosomes during prophase I of meiosis I. Genetic recombination gives rise to allelic diversity in the newly formed daughter cells. In humans, crossing over produces genetically distinct haploid egg and sperm cells that undergo fertilization to produce unique offspring. Before cell division starts, the germ cell’s chromosome(s) undergo duplication in the S phase of the cell cycle. As the cells enter prophase I,...
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Crossing Over01:34

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Unlike mitosis, meiosis aims for genetic diversity in its creation of haploid gametes. Dividing germ cells first begin this process in prophase I, where each chromosome—replicated in S phase—is now composed of two sister chromatids (identical copies) joined centrally.
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The basic reaction of homologous recombination (HR) involves two chromatids that contain DNA sequences sharing a significant stretch of identity. One of these sequences uses a strand from another as a template to synthesize DNA in an enzyme-catalyzed reaction. The final product is a novel amalgamation of the two substrates. To ensure an accurate recombination of sequences, HR is restricted to the S and G2 phases of the cell cycle. At these stages, the DNA has been replicated already and the...
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Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
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Recombineering Homologous Recombination Constructs in Drosophila
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Assessing Differences Between Ancestral Recombination Graphs.

Mary K Kuhner1, Jon Yamato

  • 1Department of Genome Sciences, University of Washington, Seattle, WA, 98195-5065, USA, mkkuhner@uw.edu.

Journal of Molecular Evolution
|April 6, 2015
PubMed
Summary
This summary is machine-generated.

We developed a new framework to compare ancestral recombination graph (ARG) inferences. This method uses standard tree comparisons to evaluate how well genomic history reconstructions succeed, improving ARG inference accuracy.

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Area of Science:

  • Genomics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Ancestral recombination graphs (ARGs) model genomic history with recombination.
  • Inferring accurate ARGs is crucial for understanding evolutionary processes.
  • Current methods lack a robust metric for evaluating ARG inference success.

Purpose of the Study:

  • To propose a novel framework for comparing Ancestral Recombination Graph (ARG) inferences.
  • To establish a quantitative method for assessing the accuracy of ARG reconstruction.

Main Methods:

  • Developed a comparison framework by averaging standard tree comparison measures.
  • Applied the framework to simulated genomic data.
  • Evaluated performance using both all sites and variable sites only.

Main Results:

  • The proposed framework effectively distinguishes between better and worse ARG inferences.
  • Demonstrated the utility of the framework with simulated data.
  • Showcased the framework's ability to quantify inference quality.

Conclusions:

  • The new ARG comparison framework provides a vital metric for evaluating inference performance.
  • This approach can guide the development of more accurate ARG inference methods.
  • Facilitates progress in understanding genome evolution through improved ARG reconstruction.