通过强大的转录因子活动估计和以模型为导向的实验验证,在批量和单细胞数据中发现功能性基因调控网络
bioRxiv : the preprint server for biology
|July 15, 2025
概括
这项研究介绍了MERLIN+P+TFA,这是一种新的计算方法,通过对转录因子活性 (TFA) 进行可靠的估计,准确地重建基因调节网络 (GRNs). 该方法提高了GRN推断质量,并确定了小鼠胚胎干细胞中的关键调节者.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 重建基因组规模的基因调控网络 (GRNs) 是一个挑战.
- 目前估计转录因子活性 (TFA) 的方法对噪声敏感.
- 准确的GRN推断需要强大的TFA估计.
研究的目的:
- 开发一种可靠的方法来估计TFA和推断GRNs.
- 为了提高GRN重建的准确性和可靠性.
- 为了确定小鼠胚胎干细胞状态的关键调节者.
主要方法:
- 开发了MERLIN+P+TFA,这是一种使用先前知识引导的稀疏度规范化的新方法.
- 将该方法应用于酵母和哺乳动物系统的模拟和真实表达数据.
- 在小鼠胚胎干细胞中验证了推断的GRNs和优先考虑的关键调节者.
主要成果:
- MERLIN+P+TFA可稳定准确地估计TFA,并提高GRN推断质量.
- 该方法显示了GRN质量的改善,无论是批量数据集还是单细胞数据集.
- 实验验证了小鼠胚胎干细胞状态的58个关键调节因子,识别了已知的和新的因素.
结论:
- 规范化的TFA估计增强了各种GRN推断算法.
- 在系统生物学中,MERLIN+P+TFA为GRN重建提供了显著的改进.
- 推断网络可以精确地捕捉功能TF目标,突出需要特定环境的黄金标准.
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