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Mining frequent stem patterns from unaligned RNA sequences




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.

Author(s): Hamada, M. and Tsuda, K. and Kudo, T. and Kin, T. and Asai, K.
Journal: Bioinformatics
Volume: 22
Number (issue): 20
Pages: 2480-2487
Year: 2006
Month: October
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1093/bioinformatics/btl431
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Mining frequent stem patterns from unaligned RNA sequences},
  author = {Hamada, M. and Tsuda, K. and Kudo, T. and Kin, T. and Asai, K.},
  journal = {Bioinformatics},
  volume = {22},
  number = {20},
  pages = {2480-2487},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = oct,
  year = {2006},
  month_numeric = {10}