Abstract
To comprehend DNA-protein binding specificity in diverse cell types is essential to reveal regulatory mechanisms in biological processes. Recently, deep learning has been successfully applied to predict DNA-protein binding sites from large-scale chromatin-profiling data. However, the precise identification of putative binding sites in specific cell types with low labeled samples remains challenging. To this end, we present a novel Transferable Transformer-based method, dubbed as BindTransNet, for cross-cell types DNA-protein binding prediction. Transfer learning and Transformer Encoder are simultaneously adopted in our presented approach to capture some shared long-range dependencies between various motifs available in cross-cell types. This unique design helps our method recognize putative binding sites without massive labeled samples by leveraging the above-mentioned standard features. This work is the first to apply a Transformer for DNA-protein binding sites prediction. The presented method is measured on TFs COREST and SRF in four cell types with eight cell-type TF pairs. For both 4-class prediction and binary-level prediction, BindTransNet can significantly outperform several state-of-the-art methods. Moreover, BindTransNet achieves considerable margin performance improvements by leveraging transfer learning. This is a presuasive indication that BindTransNet can indeed capture shared features available in other cell types.
This work is supported by the National Natural Science Foundation of China under Grant No. 61702058; the China Postdoctoral Science Foundation funded project No. 2017M612948.
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Wang, Z. et al. (2021). BindTransNet: A Transferable Transformer-Based Architecture for Cross-Cell Type DNA-Protein Binding Sites Prediction. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_18
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DOI: https://doi.org/10.1007/978-3-030-91415-8_18
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