Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning

Background Alternative splicing is a crucial mechanism of post-transcriptional modification responsible for the transcriptome plasticity and proteome diversity of a metazoan cell. Although many splicing regulations around the exon/intron regions have been discovered, the relationship between promoter-bound transcription factors and the downstream alternative splicing remains largely unexplored. Results In this study, we present computational approaches to decipher the regulation relationship connecting the promoter-bound transcription factor binding sites (TFBSs) and the splicing patterns. We curated a fine data set, including DNase I hypersensitive sites sequencing and transcriptome in fifteen human tissues from ENCODE. Specifically, we proposed different representations of TF binding context and splicing patterns to tackle the associations between the promoter and downstream splicing events. Our results demonstrated that the convolutional neural network (CNN) models learned from the TF binding changes in the promoter to predict the splicing pattern changes. Furthermore, through an in silico perturbation-based analysis of the CNN models, we identified several TFs that considerably reduced the model performance of splicing prediction. Conclusion In conclusion, our finding highlights the potential role of promoter-bound TFBSs in influencing the regulation of downstream splicing patterns and provides insights for discovering alternative splicing regulations.