COSSMO: Predicting Competitive Alternative Splice Site Selection using Deep Learning

Motivation Alternative splice site selection is inherently competitive and the probability of a given splice site to be used also depends strongly on the strength of neighboring sites. Here we present a new model named Competitive Splice Site Model (COSSMO), which explicitly models these competitive effects and predict the PSI distribution over any number of putative splice sites. We model an alternative splicing event as the choice of a 3’ acceptor site conditional on a fixed upstream 5’ donor site, or the choice of a 5’ donor site conditional on a fixed 3’ acceptor site. We build four different architectures that use convolutional layers, communication layers, LSTMS, and residual networks, respectively, to learn relevant motifs from sequence alone. We also construct a new dataset from genome annotations and RNA-Seq read data that we use to train our model. Results COSSMO is able to predict the most frequently used splice site with an accuracy of 70% on unseen test data, and achieve an R2 of 60% in modeling the PSI distribution. We visualize the motifs that COSSMO learns from sequence and show that COSSMO recognizes the consensus splice site sequences as well as many known splicing factors with high specificity. Availability Our dataset is available from http://cossmo.deepgenomics.com. Contact frey@deepgenomics.com Supplementary information Supplementary data are available at Bioinformatics online.