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Journal Article

Mining frequent stem patterns from unaligned RNA sequences

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Hamada, M., Tsuda, K., Kudo, T., Kin, T., & Asai, K. (2006). Mining frequent stem patterns from unaligned RNA sequences. Bioinformatics, 22(20), 2480-2487. doi:10.1093/bioinformatics/btl431.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CFBF-5
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.