DotAligner: Identification and Clustering of RNA Structure Motifs

Genome Biol. 2017 Dec 28;18(1):244. doi: 10.1186/s13059-017-1371-3.

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.

Keywords: Functional genome annotation; Functions of RNA structures; Machine learning; RNA structure clustering; RNA–protein interactions; Regulation by non-coding RNAs.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Base Sequence
  • Cluster Analysis*
  • Computational Biology / methods*
  • Genomics / methods
  • Nucleic Acid Conformation*
  • Nucleotide Motifs*
  • RNA / chemistry*
  • RNA / genetics*
  • RNA / metabolism
  • RNA, Untranslated / chemistry
  • RNA, Untranslated / genetics
  • RNA-Binding Proteins / metabolism
  • Reproducibility of Results

Substances

  • RNA, Untranslated
  • RNA-Binding Proteins
  • RNA