使用深度卷积和循环神经网络发现非正规的GRHL1结合部位
Sebastian Proft1,2, Janna Leiz3,4,5, Udo Heinemann6
1Exploratory Diagnostic Sciences, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, 10117, Berlin, Germany.
BMC genomics
|December 4, 2023
概括
卷积循环神经网络识别了传统方法遗漏的新型转录因子结合位点 (TFBS). 这种深度学习方法准确地预测了结合亲和力,揭示了对人类转录调节的新见解.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 分子生物学分子生物学
背景情况:
- 转录因子 (TFs) 与特定的DNA序列 (TFBSs) 结合,以调节基因表达.
- 传统的位置重量矩阵 (PWM) 模型对TFBS预测有局限性,包括无法捕获核酸相互依赖性和识别非正规位点.
- 像深度学习这样的先进方法为更高的准确性和发现新型TFBS提供了潜力.
研究的目的:
- 调查深度学习方法,特别是卷积循环神经网络 (CRNNs),是否可以识别新的,非正规的转录因子结合位 (TFBS).
- 与传统方法相比,评估CRNNs在预测TF结合亲缘关系方面的准确性.
- 探索CRNNs在发现与人类转录调节相关的先前意想不到的结合位点方面的潜力.
主要方法:
- 使用HT-SELEX数据训练了一个卷积循环神经网络 (CRNN),用于GRHL1转录因子结合.
- 应用训练CRNN模型来预测人类细胞中CHIP-Seq数据中的GRHL1结合位点.
- 利用异热定位热量计和突变发生试验来实验验证预测的GRHL1结合点及其亲属性.
主要成果:
- 确定了46个非正规的GRHL1结合点,这些结合点没有被传统的PWM方法检测到.
- 发现了新的结合序列,缺乏以前被认为是GRHL1.1的强制性CNNG核心基因.
- 在CRNN预测的结合强度和实验验证的结合亲和力之间显示出强烈的相关性,超过PWM和其他深度学习方法.
结论:
- 卷积性循环神经网络 (CRNN) 有效地发现意外的转录因子结合位 (TFBS).
- 转录因子结合亲缘关系的定量预测由CRNNs提供.
- 深度学习模型提供了一个强大的工具,通过识别新的监管元素来推进对人类转录调节的理解.
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