DeepSplice: Deep classification of novel splice junctions revealed by RNA-seq

摘要

Alternative splicing (AS) is a regulated process that enables the production of multiple mRNA transcripts from a single multi-exon gene. The availability of large-scale RNA-seq datasets has made it possible to predict splice junctions, as well as splice sites through spliced alignment to the reference genome. This greatly enhances the capability to decipher gene structures and explore the diversity of splicing variants. However, existing ab initio aligners are vulnerable to false positive spliced alignments as a result of sequence errors and random sequence matches. These spurious alignments can lead to a significant set of false positive splice junction predictions, confusing downstream analyses of splice variant detection and abundance estimation. In this work, we illustrate that splice junction sequence characteristics can be ascertained from experimental data with deep learning techniques. We employ deep convolutional neural networks for a novel splice junction classification tool named DeepSplice that (i) outperforms state-of-the-art methods for predicting splice sites, (ii) shows high computational efficiency and (iii) can be applied to self-defined training data by users.