Paper Info
Title
Convolutional neural networks for classification of alignments of non-coding RNA sequences.
Abstract
Motivation: The convolutional neural network (CNN) has been applied to the classification problem of DNA sequences, with the additional purpose of motif discovery. The training of CNNs with distributed representations of four nucleotides has successfully derived position weight matrices on the learned kernels that corresponded to sequence motifs such as protein-binding sites. Results: We propose a novel application of CNNs to classification of pairwise alignments of sequences for accurate clustering of sequences and show the benefits of the CNN method of inputting pairwise alignments for clustering of non-coding RNA (ncRNA) sequences and for motif discovery. Classification of a pairwise alignment of two sequences into positive and negative classes corresponds to the clustering of the input sequences. After we combined the distributed representation of RNA nucleotides with the secondary-structure information specific to ncRNAs and furthermore with mapping profiles of next-generation sequence reads, the training of CNNs for classification of alignments of RNA sequences yielded accurate clustering in terms of ncRNA families and outperformed the existing clustering methods for ncRNA sequences. Several interesting sequence motifs and secondary-structure motifs known for the snoRNA family and specific to microRNA and tRNA families were identified.
Year
DOI
Venue
2018
10.1093/bioinformatics/bty228
BIOINFORMATICS
Field
DocType
Volume
RNA,Pairwise comparison,Computer science,Convolutional neural network,Transfer RNA,Sequence motif,Artificial intelligence,DNA sequencing,Computational biology,Cluster analysis,Non-coding RNA,Machine learning
Journal
34
Issue
ISSN
Citations 
13
1367-4803
4
PageRank 
References 
Authors
0.42
10
2
Name
Order
Citations
PageRank
Genta Aoki140.42
Yasubumi Sakakibara276962.91