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

Pengju Ding, Yifei Wang, Xinyu Zhang, Xin Gao, Guozhu Liu, Bin Yu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.
Original languageEnglish (US)
JournalBriefings in bioinformatics
StatePublished - Jun 16 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-06-19
Acknowledgements: National Natural Science Foundation of China (62172248); Natural Science Foundation of Shandong Province of China (ZR2021MF098).

ASJC Scopus subject areas

  • Molecular Biology
  • Information Systems


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