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  Mining frequent stem patterns from unaligned RNA sequences

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CFBF-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-6FB7-2
Genre: Journal Article

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Hamada, M, Author
Tsuda, K1, Author              
Kudo , T, Author
Kin, T, Author
Asai, K, Author
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1External Organizations, ou_persistent22              

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 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.

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 Dates: 2006-10
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1093/bioinformatics/btl431
BibTex Citekey: 4144
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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 22 (20) Sequence Number: - Start / End Page: 2480 - 2487 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991