DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape

计算机科学 转录因子 深层神经网络 人工智能 计算生物学 人工神经网络 序列(生物学) 模式识别(心理学) 基因 生物 遗传学
作者
Pengju Ding,Yifei Wang,Xinyu Zhang,Xin Gao,Guozhu Liu,Bin Yu
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:24 (4) 被引量:20
标识
DOI:10.1093/bib/bbad231
摘要

Precise targeting of transcription factor binding sites (TFBSs) is essential to comprehending transcriptional regulatory processes and investigating cellular function. Although several deep learning algorithms have been created to predict TFBSs, the models' intrinsic mechanisms and prediction results are difficult to explain. There is still room for improvement in prediction performance. We present DeepSTF, a unique deep-learning architecture for predicting TFBSs by integrating DNA sequence and shape profiles. We use the improved transformer encoder structure for the first time in the TFBSs prediction approach. DeepSTF extracts DNA higher-order sequence features using stacked convolutional neural networks (CNNs), whereas rich DNA shape profiles are extracted by combining improved transformer encoder structure and bidirectional long short-term memory (Bi-LSTM), and, finally, the derived higher-order sequence features and representative shape profiles are integrated into the channel dimension to achieve accurate TFBSs prediction. Experiments on 165 ENCODE chromatin immunoprecipitation sequencing (ChIP-seq) datasets show that DeepSTF considerably outperforms several state-of-the-art algorithms in predicting TFBSs, and we explain the usefulness of the transformer encoder structure and the combined strategy using sequence features and shape profiles in capturing multiple dependencies and learning essential features. In addition, this paper examines the significance of DNA shape features predicting TFBSs. The source code of DeepSTF is available at https://github.com/YuBinLab-QUST/DeepSTF/.
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