deepTFBS: Improving within- and cross-species prediction of transcription factor binding using deep multi-task and transfer learning

Zhai, J.; Zhang, Y.; Zhang, C.; Yi, X.; Song, M.; Tang, C.; Ding, P.; Li, Z.; Ma, C.

doi:10.1101/2025.03.19.644233

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bioRxiv · 10.1101/2025.03.19.644233

deepTFBS: Improving within- and cross-species prediction of transcription factor binding using deep multi-task and transfer learning

Zhai, J.·Zhang, Y.·Zhang, C.·Yi, X.·Song, M.·Tang, C.·Ding, P.·Li, Z.·Ma, C.

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Abstract

The precise prediction of transcription factor binding sites (TFBSs) is crucial in understanding gene regulation. In this study, we present deepTFBS, a comprehensive deep learning (DL) framework that builds a robust DNA language model of TF binding grammar for accurately predicting TFBSs within and across plant species. Taking advantages of multi-task DL and transfer learning, deepTFBS is capable of leveraging the knowledge learned from large-scale TF binding profiles to enhance the prediction of TFBSs under small-sample training and cross-species prediction tasks. When tested using available information on 359 Arabidopsis TFs, deepTFBS outperformed previously described prediction strategies, including position weight matrix, deepSEA and DanQ, with a 244.49%, 49.15%, and 23.32% improvement of the area under the precision-recall curve (PRAUC), respectively. Further cross-species prediction of TFBS in wheat showed that deepTFBS yielded a significant PRAUC improvement of 30.6% over these three baseline models. deepTFBS can also utilize information from gene conservation and binding motifs, enabling efficient TFBS prediction in species where experimental data availability is limited. A case study, focusing on the WUSCHEL (WUS) transcription factor, illustrated the potential use of deepTFBS in cross-species applications, in our example between Arabidopsis and wheat. deepTFBS is publically available at https://github.com/cma2015/deepTFBS. One sentence summaryThe high-performing deep learning framework deepTFBS can flexibly perform within- and cross-species prediction of TF binding, transferring the transcription regulation from model to non-model species.

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Zhai, J., Zhang, Y., Zhang, C., Yi, X., Song, M., Tang, C., Ding, P., Li, Z., Ma, C.. 2025-03-20. deepTFBS: Improving within- and cross-species prediction of transcription factor binding using deep multi-task and transfer learning. https://doi.org/10.1101/2025.03.19.644233

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