从深度学习的促进器中转录因子结合位预测拼接模式
Tzu-Chieh Lin1, Cheng-Hung Tsai1, Cheng-Kai Shiau1
1Institute of Information Science, Academia Sinica, Taipei, 11529, Taiwan.
BMC genomics
|September 3, 2024
概括
促进体结合转录因子结合位 (TFBS) 影响替代拼接. 计算模型预测了TFBS变化的拼接变化,CTCFL被确定为一个关键的监管器.
科学领域:
- 分子生物学分子生物学
- 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:
- 替代拼接在元动物细胞中产生转录组可塑性和蛋白质组多样性.
- 促进者绑定转录因子和下游替代拼接之间的监管联系在很大程度上是未知的.
研究的目的:
- 通过计算来研究促进体结合转录因子结合位点 (TFBS) 和替代拼接模式之间的调控关系.
- 探索促进体中TFBS的变化如何影响外子拼接效率.
主要方法:
- 利用了ENCODE数据,包括DNase I过敏部位测序和15个人类组织的转录组.
- 开发了计算方法和机器学习模型,包括卷积神经网络 (CNN),以分析TFBS和拼接模式.
- 通过使用K562 CTCFL shRNA knock-down数据进行了in silico替代分析和经验验证.
主要成果:
- 机器学习模型显示了从TFBS占用中预测拼接模式的潜力,尽管对跨组织单元基因的概括是有限的.
- 在促销者TFBS变化上训练的CNN模型准确地预测了拼接模式的变化.
- 在分析中确定了潜在的拼接调节器,经验验证证证实了CTCFL在拼接调节中的重要作用.
结论:
- 发起人绑定的TFBS在下游替代拼接的监管中发挥着潜在的作用.
- 这项研究为发现新的替代拼接监管机制提供了洞察力.
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