Differential Analysis of Alternative Splicing Events in gene regions using Residual Neural Networks
Several computational methods for the differential analysis of alternative splicing (AS) events among RNA-seq samples typically rely on estimating isoform-level gene expression. However, these approaches are often error-prone due to the interplay of individual AS events, which results in different isoforms with locally similar sequences. Moreover, methods based on isoform-level quantification usually need annotated transcripts. In this work, we leverage the ability of deep learning networks to learn features from images, to propose deepSpecas, a novel method for event-based AS differential analysis between two RNA-seq samples. Our method does not rely on isoform abundance estimation, neither on a specific annotation. deepSpecas employs an image embedding scheme to represent the alignments of the two samples on the same region and utilizes a residual neural network to predict the AS events possibly expressed within that region. To our knowledge deepSpecas is the first deep learning approach for performing an event-based AS analysis of RNA-seq samples. To validate deepSpecas, we also address the lack of high quality AS benchmark datasets. For this purpose, we manually curated a set of regions exhibiting AS events. These regions were used for training our model and for comparing our method with state-of-the-art event-based AS analysis tools. Our results highlight that deepSpecas achieves higher precision at the expense of a small reduction in sensitivity. The tool and the manually curated regions are available at https://github.com/sciccolella/deepSpecas.