DotAligner: identification and clustering of RNA structure motifs.

Journal: Genome biology
Published Date: Dec 28, 2017

Abstract

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.

Authors

  • Martin A Smith
    RNA Biology and Plasticity Group, Garvan Institute of Medical Research, 384 Victoria Street, Sydney, NSW 2010, Australia. m.smith@garvan.org.au.
  • Stefan E Seemann
    Center for non-coding RNA in Technology and Health (RTH), University of Copenhagen, Groennegaardsvej 3, Frederiksberg, 1870, Denmark.
  • Xiu Cheng Quek
    RNA Biology and Plasticity Group, Garvan Institute of Medical Research, 384 Victoria Street, Sydney, NSW 2010, Australia.
  • John S Mattick
    RNA Biology and Plasticity Group, Garvan Institute of Medical Research, 384 Victoria Street, Sydney, NSW 2010, Australia.

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