Deep Learning Deepens the Analysis of Alternative Splicing

The ever-increasing high-volume and high-dimensional genomics data on the one hand challenge traditional data analysis approaches, and on the other hand provide ample opportunities for developing novel analytic strategies. In recent years, deep learning has been driving the next wave of artificial intelligence and machine learning. Now, Yi Xing’s lab reported DARTS [1], a novel computational framework that leverages the power of both deep learning and Bayes hierarchical framework for differential alternative splicing (AS) analysis. Trained on the huge volume of publicly-available RNA-seq datasets, DARTS could largely increase the accuracy of AS analysis, in particular for those with low sequencing depth, by taking both genomic features and expression levels of RNA-binding proteins (RBPs) into consideration.