RT Journal Article
SR Electronic
T1 A Deep Recurrent Neural Network Discovers Complex Biological Rules to Decipher RNA Protein-Coding Potential
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 200758
DO 10.1101/200758
A1 Steven T. Hill
A1 Rachael Kuintzle
A1 Amy Teegarden
A1 Erich Merrill III
A1 Padideh Danaee
A1 David A. Hendrix
YR 2017
UL http://biorxiv.org/content/early/2017/11/13/200758.1.abstract
AB The current deluge of newly identified RNA transcripts presents a singular opportunity for improved assessment of coding potential, a cornerstone of genome annotation, and for machine-driven discovery of biological knowledge. While traditional, feature-based methods for RNA classification are limited by current scientific knowledge, deep learning methods can independently discover complex biological rules in the data de novo. We trained a gated recurrent neural network (RNN) on human messenger RNA (mRNA) and long noncoding RNA (lncRNA) sequences. Our model, mRNA RNN (mRNN), surpasses state-of-the-art methods at predicting protein-coding potential. To understand what mRNN learned, we probed the network and uncovered several context-sensitive codons highly predictive of coding potential. Our results suggest that gated RNNs can learn complex and long-range patterns in full-length human transcripts, making them ideal for performing a wide range of difficult classification tasks and, most importantly, for harvesting new biological insights from the rising flood of sequencing data.