Stem kernels for RNA sequence analyses

Yasubumi Sakakibara, Kiyoshi Asai, Kengo Sato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Several computational methods based on stochastic context-free grammars have been developed for modeling and analyzing functional RNA sequences. These grammatical methods have succeeded in modeling typical secondary structures of RNA and are used for structural alignment of RNA sequences. However, such stochastic models cannot sufficiently discriminate member sequences of an RNA family from non-members and hence detect non-coding RNA regions from genome sequences. A novel kernel function, stem kernel, for the discrimination and detection of functional RNA sequences using support vector machines (SVM) is proposed. The stem kernel is a natural extension of the string kernel, specifically the all-subsequences kernel, and is tailored to measure the similarity of two RNA sequences from the viewpoint of secondary structures. The stem kernel examines all possible common base-pairs and stem structures of arbitrary lengths, including pseudoknots between two RNA sequences and calculates the inner product of common stem structure counts. An efficient algorithm was developed to calculate the stem kernels based on dynamic programming. The stem kernels are then applied to discriminate members of an RNA family from non-members using SVM. The study indicates that the discrimination ability of the stem kernel is strong compared with conventional methods. Further, the potential application of the stem kernel is demonstrated by the detection of remotely homologous RNA families in terms of secondary structures. This is because the string kernel is proven to work for the remote homology detection of protein sequences. These experimental results have convinced us to apply the stem kernel to find novel RNA families from genome sequences.

Original languageEnglish
Title of host publicationBioinformatics Research and Development - First International Conference, BIRD 2007 Proceedings
PublisherSpringer Verlag
Number of pages14
ISBN (Print)3540712321, 9783540712329
Publication statusPublished - 2007
Event1st International Conference on Bioinformatics Research and Development, BIRD 2007 - Berlin, Germany
Duration: 2007 Mar 122007 Mar 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4414 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other1st International Conference on Bioinformatics Research and Development, BIRD 2007


  • RNA
  • SVM
  • Secondary structure
  • Stem kernel
  • String kernel

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Stem kernels for RNA sequence analyses'. Together they form a unique fingerprint.

Cite this