Sep 18, 2014

DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning

Leslie TurnerXin Gao


Alternative polyadenylation (APA) is a crucial step in post-transcriptional regulation. Previous bioinformatic works have mainly focused on the recognition of polyadenylation sites (PAS) in a given genomic sequence, which is a binary classification problem. Recently, computational methods for predicting the usage level of alternative PAS in a same gene have been proposed. However, all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PAS into account. To address this, here we propose a deep learning architecture, DeeReCT-APA, to quantitatively predict the usage of all alternative PAS of a given gene. To accommodate different genes with potentially different numbers of PAS, DeeReCT-APA treats the problem as a regression task with a variable-length target. Based on a CNN-LSTM architecture, DeeReCT-APA extracts sequence features with CNN layers, uses bidirectional LSTM to explicitly model the interactions among competing PAS, and outputs percentage scores representing the usage levels of all PAS of a gene. In addition to the fact that only our method can predict quantitatively the usage of all the PAS within a gene, we show that our method consistently outperform...Continue Reading

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