Mining frequent stem patterns from unaligned RNA sequences

Michiaki Hamada, Koji Tsuda, Taku Kudo, Taishin Kin, Kiyoshi Asai

研究成果: Article

35 引用 (Scopus)

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Motivation: In detection of non-coding RNAs, it is often necessary to identify the secondary structure motifs from a set of putative RNA sequences. Most of the existing algorithms aim to provide the best motif or few good motifs, but biologists often need to inspect all the possible motifs thoroughly. Results: Our method RNAmine employs a graph theoretic representation of RNA sequences and detects all the possible motifs exhaustively using a graph mining algorithm. The motif detection problem boils down to finding frequently appearing patterns in a set of directed and labeled graphs. In the tasks of common secondary structure prediction and local motif detection from long sequences, our method performed favorably both in accuracy and in efficiency with the state-of-the-art methods such as CMFinder.

元の言語English
ページ(範囲)2480-2487
ページ数8
ジャーナルBioinformatics
22
発行部数20
DOI
出版物ステータスPublished - 2006 10 1

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ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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