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DotAligner: identification and clustering of RNA structure motifs


The diversity of processed transcripts in eukaryotic genomes poses a challenge for the classification of their biological functions. Sparse sequence conservation in non-coding sequences and the unreliable nature of RNA structure predictions further exacerbate this conundrum. Here, we describe a computational method, DotAligner, for the unsupervised discovery and classification of homologous RNA structure motifs from a set of sequences of interest. Our approach outperforms comparable algorithms at clustering known RNA structure families, both in speed and accuracy. It identifies clusters of known and novel structure motifs from ENCODE immunoprecipitation data for 44 RNA-binding proteins.

Type Journal
ISBN 1474-760X (Electronic) 1474-7596 (Linking)
Authors Smith, M. A.; Seemann, S. E.; Quek, X. C.; Mattick, J. S.
Responsible Garvan Author (missing name)
Published Date 2017-12-28
Published Volume 18
Published Issue 1
Published Pages 244
Status Published in-print
DOI 10.1186/s13059-017-1371-3
URL link to publisher's version
OpenAccess link to author's accepted manuscript version