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Bioinformatics Advance Access published online on August 14, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl431
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 12, 2006
Revised July 26, 2006
Accepted August 3, 2006

Article

Mining frequent stem patterns from unaligned RNA sequences

Michiaki Hamada 1 *, Koji Tsuda 2, Taku Kudo 3, Taishin Kin 4, and Kiyoshi Asai 5

1 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-43 Aomi, Koto-ku, Tokyo, Japan; Mizuho Information & Research Institute, Inc, 2-3, Kanda-Nishikicho, Chiyoda-ku, Tokyo 101-8443, Japan; Department of Computational Intelligence and System Science, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, 226-8503, Japan
2 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-43 Aomi, Koto-ku, Tokyo, Japan; Max Planck Institute for Biological Cybernetics, Spemannstr. 38, 72076 Tübingen, Germany
3 Google Japan, Inc., 26-1, Sakuracho, Shibuya, Tokyo, 150-8512, Japan
4 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-43 Aomi, Koto-ku, Tokyo, Japan
5 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-43 Aomi, Koto-ku, Tokyo, Japan; Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-8562, Japan

* To whom correspondence should be addressed.
Michiaki Hamada, E-mail: hamada-michiaki{at}aist.go.jp


  Abstract

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.

Availability: The software is available on request.

Supplementary information: Visit the following URL for supplementary information, software availability and the information about the web server. http://www.ncrna.org/RNAMINE/.

Associate Editor: Golan Yona
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