DNA-Level Splice Junction Prediction using Deep Recurrent Neural Networks

TL;DR

This paper introduces a deep recurrent neural network approach for predicting splice junctions in DNA sequences, significantly improving accuracy over previous methods by modeling sequence dependencies effectively.

Contribution

It presents a novel application of various RNN architectures for splice junction prediction, outperforming existing machine learning and deep learning techniques.

Findings

Deep RNN models outperform traditional methods in accuracy.

LSTM and GRU units show superior performance in sequence modeling.

The approach reduces false positives in splice junction detection.

Abstract

A eukaryotic gene consists of multiple exons (protein coding regions) and introns (non-coding regions), and a splice junction refers to the boundary between a pair of exon and intron. Precise identification of spice junctions on a gene is important for deciphering its primary structure, function, and interaction. Experimental techniques for determining exon/intron boundaries include RNA-seq, which is often accompanied by computational approaches. Canonical splicing signals are known, but computational junction prediction still remains challenging because of a large number of false positives and other complications. In this paper, we exploit deep recurrent neural networks (RNNs) to model DNA sequences and to detect splice junctions thereon. We test various RNN units and architectures including long short-term memory units, gated recurrent units, and recently proposed iRNN for in-depth design space exploration. According to our experimental results, the proposed approach significantly outperforms not only conventional machine learning-based methods but also a recent state-of-the-art deep belief network-based technique in terms of prediction accuracy.