DeepBSI: a multimodal deep learning framework for predicting the transcription factor binding site and intensity

To fully understand the detailed regulation mechanism of genomes and their functions, increasing computational methods have been developed to predict the TF binding site and intensity mainly based on DNA sequences or epigenomic data but ignoring the TF binding data across cell types. To address this problem, we proposed a multimodal deep learning framework, DeepBSI, to predict TF binding site and intensity in target cell type by leveraging the corresponding TF binding data across cell types. The framework can not only detect associations between sequence context features but also incorporate the correlations between TF binding signal values within and across cell types to make the prediction. In addition, the front modules of the framework employ the same convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture model providing valuable information of TF motifs and their interactions, which make the framework interpretable. Applying DeepBSI to ten representative TFs across five cell types proved that models contain the TF binding information across cell types can significantly improve the performance of models in both TF binding site and intensity predicting tasks. The implemented code and experimental dataset are available online at https://github.com/yushenshashen/DeepBSI.