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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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Transposons make up a significant part of genomes of various organisms. Therefore, it is believed that transposition played a major evolutionary role in speciation by changing genome sizes and modifying gene expression patterns. For example, in bacteria, transposition can lead to conferring antibiotic resistance. Movement of transposable elements within the genetic pool of pathogenic bacteria can aid in transfer of antibiotic-resistant genetic elements. In eukaryotes, transposons can carry out...
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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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The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
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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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Updated: Sep 23, 2025

Recombineering Homologous Recombination Constructs in Drosophila
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Deep learning identifies and quantifies recombination hotspot determinants.

Yu Li1,2,3,4, Siyuan Chen2,3, Trisevgeni Rapakoulia5

  • 1Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), 999077, Hong Kong SAR, China.

Bioinformatics (Oxford, England)
|May 13, 2022
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Summary
This summary is machine-generated.

A new deep learning method, RHSNet, identifies and quantifies genetic recombination hotspot determinants. This computational approach advances understanding of recombination, crucial for sexually reproducing organisms.

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Area of Science:

  • Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Recombination is a vital genetic process in sexually reproducing organisms.
  • Recombination hotspots are regions with elevated recombination frequencies.
  • Factors like PRDM9 binding motifs are linked to hotspots, but their precise contributions and other determinants remain unclear.

Purpose of the Study:

  • To develop a computational method for identifying and quantifying recombination hotspot determinants.
  • To analyze the contributions of various factors to recombination hotspot formation in a data-driven manner.

Main Methods:

  • A deep learning and signal processing approach named RHSNet was developed.
  • RHSNet utilizes diverse datasets from various studies, populations, sexes, and species.
  • The method is purely data-driven, aiming to uncover unknown determinants.

Main Results:

  • RHSNet significantly outperforms existing sequence-based methods across multiple datasets and species.
  • The method accurately identifies hotspot regions and known determinants.
  • RHSNet quantifies the contributions of PRDM9 binding motifs, histone modification, and GC content to hotspot formation.
  • Cross-species and cross-population analyses demonstrate the method's generalization power for identifying evolutionary determinant motifs.

Conclusions:

  • RHSNet provides a powerful tool for understanding recombination hotspot formation.
  • The method offers novel insights into the quantitative contributions of various factors.
  • RHSNet has the potential to identify and quantify evolutionary determinant motifs across diverse biological contexts.