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deepTFBS:使用深度多任务和转移学习改进转录因子结合的物种内部和跨物种预测

Jingjing Zhai^1,2, Yuzhou Zhang¹, Chujun Zhang^1,2

¹State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, 712100, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|May 24, 2025

概括

深度学习框架deepTFBS准确地预测了植物中的转录因子结合点 (TFBS). 它改善了跨物种TFBS预测,帮助基因调节研究,即使数据有限.

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.
计算生物学计算生物学

背景情况:

准确预测转录因子结合位点 (TFBSs) 对于理解基因调节至关重要.
现有的方法在预测TFBS方面面临挑战,特别是在不同的植物物种或有限的实验数据中.

研究的目的:

介绍深度TFBS,深度学习 (DL) 框架,用于在植物物种内和植物物种之间进行强大的TFBS预测.
利用多任务DL和转移学习来提高TFBS预测准确性,特别是在低数据场景中.

主要方法:

开发了深度TFBS,这是一个全面的DL框架,利用DNA语言模型来捕获TF绑定语法.
采用多任务DL和转移学习来利用大规模的TF绑定配置文件来改进预测.
评估性能与既有方法相比,如位置重量矩阵,deepSEA和DanQ.

主要成果:

深度TFBS在预测Arabidopsis中的TFBS方面显著优于基线模型,在精度回忆曲线 (PRAUC) 下的区域显著改善.
与现有策略相比,在小麦中表现出优异的跨物种TFBS预测准确性,PRAUC显著增加.
展示了将基因保护和结合动机信息纳入有效TFBS预测的实用性.

结论:

deepTFBS为植物中TFBS预测提供了一种强大而通用的工具,增强了我们对基因调节的理解.
该框架能够进行跨物种预测并利用有限的数据,这使得它对各种基因组研究具有价值.
一个使用WUSCHEL转录因子的案例研究强调了深度TFBS在比较基因组学中的实际应用潜力.

关键词:

生物信息学是一种生物信息学.跨物种预测的跨物种预测深度学习是一种深度学习.机器学习是机器学习.转录性监管网络转录性监管网络

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