Mining frequent stem patterns from unaligned RNA sequences.
BACKGROUND
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.


BACKGROUND
The software is available upon request.
Hamada
Michiaki
M
Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST) 2-43 Aomi, Koto-ku, Tokyo, Japan. hamada-michiaki@aist.go.jp
Tsuda
Koji
K
Kudo
Taku
T
Kin
Taishin
T
Asai
Kiyoshi
K
D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D030541:Databases, Genetic; D016247:Information Storage and Retrieval / Q000379:methods*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated; D012313:RNA / Q000737:chemistry* / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*
D012313:RNA