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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
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GEEES:从多模式单细胞数据推断细胞特异性基因增强剂相互作用.

Shuyang Chen1, Sündüz Keleş1,2

  • 1Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, United States.

Bioinformatics (Oxford, England)
|October 29, 2024
PubMed
概括

我们开发了GEEES以从单细胞多模式数据推断基因增强剂相互作用. 结合基因增强器距离显著提高了准确性,突出了更好的验证数据集的需要.

科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 分子生物学分子生物学

背景情况:

  • 基因增强剂相互作用对于转录调节至关重要.
  • 多模态单细胞数据集为研究这些细胞层面的相互作用提供了新的途径.
  • 现有的计算方法没有与基准数据集进行严格评估,并且往往缺乏单细胞分辨率.

研究的目的:

  • 开发和评估一种新的计算方法,GEEES,用于在单细胞水平上推断基因增强剂的关联.
  • 综合评估使用多模态单细胞数据推断基因增强剂相互作用的当前方法.
  • 确定影响这些推断准确性的关键因素,如基因增强器距离.

主要方法:

  • 开发了GEEES (多模态单细胞数据的基因增强器相互作用) 用于单细胞基因增强器相互作用推断.
  • 使用多模态单细胞数据 (转录组和染色质可访问性).
  • 与基准数据集对比评估了GEEES和替代方法,将基因增强器距离作为一个特征.

主要成果:

  • 在单细胞水平上,GEEES推断出基因增强剂的关联.
  • 整合基因增强器距离显著提高了所有测试方法的性能.
  • 在推断和黄金标准相互作用之间观察到差异,表明当前数据集和方法的局限性.

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  • GEEES为下游分析提供增强的细胞表示学习.

    • 结论:

    • GEEES为单细胞基因增强剂相互作用推断提供了一个有价值的工具.
    • 基因增强器距离是提高准确性的关键因素.
    • 迫切需要新的高通量实验来产生强大的基准数据集,以验证推断的基因增强剂相互作用.