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相关概念视频

Crossing Over01:30

Crossing Over

4.3K
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,...
4.3K
Conservative Site-specific Recombination and Phase Variation02:53

Conservative Site-specific Recombination and Phase Variation

6.0K
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.
The recognition sites for Cre recombinase called LoxP...
6.0K
Homologous Recombination02:31

Homologous Recombination

50.4K
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...
50.4K
Gene Conversion02:08

Gene Conversion

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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...
9.7K
Exon Recombination02:32

Exon Recombination

3.6K
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. 
Exon shuffling follows “splice frame rules.” Each exon...
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相关实验视频

Updated: Jun 24, 2025

Author Spotlight: Decoding DNA Repair by Extrachromosomal NHEJ Assay and HR Assays
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Author Spotlight: Decoding DNA Repair by Extrachromosomal NHEJ Assay and HR Assays

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基于自动机器学习的重组热点的预测.

Dong-Xin Ye1, Jun-Wen Yu1, Rui Li1

  • 1School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.

Journal of molecular biology
|June 13, 2024
PubMed
概括

自动机器学习通过整合序列和物理化学数据来改善重组热点预测. 这种新的方法提高了准确性,为遗传进化研究提供了强大的工具.

关键词:
自动机器学习自动化机器学习可以解释的解释性.重组热点是一个重组热点.序列属性属性是序列的属性.

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Identification of Homologous Recombination Events in Mouse Embryonic Stem Cells Using Southern Blotting and Polymerase Chain Reaction
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Detection of Homologous Recombination Intermediates via Proximity Ligation and Quantitative PCR in Saccharomyces cerevisiae
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Identification of Homologous Recombination Events in Mouse Embryonic Stem Cells Using Southern Blotting and Polymerase Chain Reaction
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科学领域:

  • 遗传学 是一个遗传学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 介质重组对于遗传进化和生物多样性至关重要.
  • 目前的预测方法在特征提取和概括方面扎.
  • 深度学习模型在重组热点预测方面存在局限性.

研究的目的:

  • 开发一个改进的重组热点预测模型.
  • 解决现有的特征提取和泛化方法的局限性.
  • 利用自动机器学习来提高预测准确度.

主要方法:

  • 用自动机器学习方法来构建模型.
  • 组合序列信息与物理化学性质.
  • 使用TF-IDF-Kmer和DNA组合用于特征提取.

主要成果:

  • 该模型在三个数据集上实现了97.14%,79.71%和98.73%的高准确率.
  • 超过当前领先型号的表现2%至4%.
  • SHAP和AutoGluon用于模型解释性和特征影响分析.

结论:

  • 自动机器学习显著提高了重组热点预测.
  • 开发的模型提供了卓越的准确性和特征表示.
  • 这项工作突出了自动机器学习在基因序列预测方面的潜力,并为研究人员提供了实用工具.