基于深度学习的细胞自由DNA上转录因子结合点的预测
Ting Qi1, Ying Zhou1, Yuqi Sheng1
1State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, People's Republic of China.
Journal of chemical information and modeling
|May 27, 2024
概括
这项研究引入了一种深度学习模型,以非侵入性地识别无细胞DNA (cfDNA) 中的转录因子结合位 (TFBS). 该方法可以准确地从血中预测TFBS,提供对基因调节和潜在疾病监测的见解.
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) 通过与特定的DNA位点 (TFBS) 结合来调节细胞活动.
- 无细胞DNA (cfDNA) 片段因对核酶的保护而在TFBS中得到丰富,但非侵入性TFBS识别仍然具有挑战性.
- 了解TFBS对于探索基因调节机制和疾病洞察至关重要.
研究的目的:
- 开发一种非侵入性深度学习方法,利用无细胞DNA (cfDNA) 序列信息预测转录因子结合位 (TFBS).
- 分析cfDNA片段模式和TF结合部位特征,以更好地理解基因调节.
- 探索血衍生cfDNA在非侵入性疾病监测和理解内在调节机制方面的潜力.
主要方法:
- 开发了一个结合卷积神经网络 (CNN) 和长期短期记忆 (LSTM) 的深度学习模型,从cfDNA序列数据中预测TFBS.
- 该模型在已知的TFBS序列上进行训练,以学习预测模式.
- 使用曲线下的面积 (AUC) 评估模型的性能,达到84%.
主要成果:
- 深度学习模型成功地从cfDNA预测了TFBS,AUC为84%.
- 在cfDNA片段中发现了一致的动机,其特征是预测TFBS的上游和下游的较低覆盖率.
- 在不同细胞系中观察到TF结合部位的差异,并检测到TF特定的向基因在癌症途径中被丰富.
结论:
- 建议的深度学习方法可以从血cfDNA中进行TFBS的非侵入性鉴定.
- 这种方法为了解内在基因调节机制提供了一个新的视角.
- 这些发现表明在动态疾病监测和临床实践中有潜在的应用.
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