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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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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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相关实验视频

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使用递归特征机器进行合成死亡性选.

Cathy Cai1,2, Adityanarayanan Radhakrishnan1,3, Caroline Uhler1,2

  • 1Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard.

bioRxiv : the preprint server for biology
|December 18, 2023
PubMed
概括

我们开发了一个快速的机器学习管道,使用CRISPR查数据识别合成致命 (SL) 基因对. 这种方法准确地找到已知的SL对并发现新的,提供了针对癌症脆弱性的新方法.

科学领域:

  • 计算生物学是一种计算生物学.
  • 基因组学就是基因组学.
  • 癌症研究 癌症研究

背景情况:

  • 合成致死性 (SL) 描述了破坏两个基因导致细胞死亡的遗传相互作用,提供了癌症特定的向策略.
  • 大规模的基因扰动屏幕,如癌症依赖地图 (DepMap),可以使用机器学习自动识别SL基因对.

研究的目的:

  • 开发一个高效的机器学习管道,从CRISPR可行性数据中识别合成致命的基因对.
  • 利用递归特征机器 (RFM) 和细胞系嵌入来预测基因淘汰对生命力的影响.

主要方法:

  • 利用递归特征机器 (RFMs) 根据DepMap数据的基因表达和突变特征来预测细胞活力.
  • 使用平均梯度外积来确定预测可行性的特征重要性.
  • 应用基于关联的过器来完善特征的重要性,并确定低可行性的关键指标.

主要成果:

  • 开发的管道分析了DepMapCRISPR数据的候选SL对在不到3分钟.
  • 与以前的方法相比,该方法在恢复已知,经过实验验证的SL对方面表现出更高的准确性.
  • 确定了新的候选SL基因对,为癌症脆弱性研究提供了新的途径.

结论:

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  • 计算效率高的管道有效地使用机器学习识别合成致命的基因对.
  • 这种方法提高了癌症遗传弱点的发现,为向治疗铺平了道路.