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相关概念视频

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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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In-vitro Mutagenesis01:16

In-vitro Mutagenesis

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To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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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.
In contrast, regions which code...
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相关实验视频

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Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens
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从表达数据中预测基因删除的进化目标和参数.

Andre Luiz Campelo Dos Santos1, Michael DeGiorgio1, Raquel Assis1,2

  • 1Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, United States.

Bioinformatics advances
|January 29, 2024
PubMed
概括

基因删除经常被忽视,可以通过删除多余的基因来驱动适应. 我们的新机器学习工具CLOUDe可以预测被删除的基因功能,揭示删除主要针对独特的基因,有助于进化研究.

科学领域:

  • 进化生物学是进化的生物学.
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 基因删除传统上被视为消除基因组冗余性的非适应性过程.
  • 新出现的证据表明,删除可以通过"少即多"假设推动适应.
  • 了解被删除基因的功能性质对于进化研究至关重要.

研究的目的:

  • 开发机器学习方法来预测被删除事件所准的基因的功能作用.
  • 在被删除的基因中区分冗余和独特的功能.
  • 研究影响基因删除的进化参数.

主要方法:

  • 开发CLOUDe,一种机器学习套件,利用表达数据来预测基因删除目标.
  • 使用奥恩斯坦-乌伦贝克过程建模基因表达进化.
  • 使用多层神经网络,梯度增强,随机森林和支持向量机器.

主要成果:

  • CLOUDe准确地区分冗余和独特的基因功能,并估计进化参数.
  • 在 CLOUDe 中的神经网络架构表现出最佳性能.
  • 对 *Drosophila* 数据的应用表明,基因删除主要针对独特的功能,特别是那些参与蛋白质二维基因化的人.

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结论:

  • CLOUDe提供了一个强大的工具,用于分析基因删除的进化影响.
  • 基因删除在功能进化和适应中起着重要作用.
  • 这些发现强调了独特的基因功能在删除驱动进化的背景下的重要性.