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Conference Paper: Predicting RNA secondary structures: one-grammar-fits-all solution

TitlePredicting RNA secondary structures: one-grammar-fits-all solution
Authors
Issue Date2015
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 11th International Symposium on Bioinformatics Research and Applications (ISBRA 2015), Norfolk, VA., 7-10 June 2015. In Lecture Notes in Computer Science, 2015, v. 9096, p. 211-222 How to Cite?
AbstractRNA secondary structures are known to be important in many biological processes. Many available programs have been developed for RNA secondary structure prediction. Based on our knowledge, however, there still exist secondary structures of known RNA sequences which cannot be covered by these algorithms. In this paper, we provide an efficient algorithm that can handle all RNA secondary structures found in Rfam database. We designed a new stochastic context-free grammar named Rectangle Tree Grammar (RTG) which significantly expands the classes of structures that can be modelled. Our algorithm runs in O(n 6) time and the accuracy is reasonably high, with average PPV and sensitivity over 75%. In addition, the structures that RTG predicts are very similar to the real ones.
DescriptionLNCS v. 9096 entitled: Bioinformatics Research and Applications: 11th International Symposium, ISBRA 2015 Norfolk, USA, June 7-10, 2015 Proceedings
Persistent Identifierhttp://hdl.handle.net/10722/219222
ISBN
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorLi, M-
dc.contributor.authorCheng, M-
dc.contributor.authorYe, Y-
dc.contributor.authorHon, WK-
dc.contributor.authorTing, HF-
dc.contributor.authorLam, TW-
dc.contributor.authorTang, CY-
dc.contributor.authorWong, T-
dc.contributor.authorYiu, SM-
dc.date.accessioned2015-09-18T07:18:07Z-
dc.date.available2015-09-18T07:18:07Z-
dc.date.issued2015-
dc.identifier.citationThe 11th International Symposium on Bioinformatics Research and Applications (ISBRA 2015), Norfolk, VA., 7-10 June 2015. In Lecture Notes in Computer Science, 2015, v. 9096, p. 211-222-
dc.identifier.isbn978-3-319-19047-1-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/219222-
dc.descriptionLNCS v. 9096 entitled: Bioinformatics Research and Applications: 11th International Symposium, ISBRA 2015 Norfolk, USA, June 7-10, 2015 Proceedings-
dc.description.abstractRNA secondary structures are known to be important in many biological processes. Many available programs have been developed for RNA secondary structure prediction. Based on our knowledge, however, there still exist secondary structures of known RNA sequences which cannot be covered by these algorithms. In this paper, we provide an efficient algorithm that can handle all RNA secondary structures found in Rfam database. We designed a new stochastic context-free grammar named Rectangle Tree Grammar (RTG) which significantly expands the classes of structures that can be modelled. Our algorithm runs in O(n 6) time and the accuracy is reasonably high, with average PPV and sensitivity over 75%. In addition, the structures that RTG predicts are very similar to the real ones.-
dc.languageeng-
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/-
dc.relation.ispartofLecture Notes in Computer Science-
dc.rightsThe final publication is available at Springer via http://dx.doi.org/[insert DOI]-
dc.titlePredicting RNA secondary structures: one-grammar-fits-all solution-
dc.typeConference_Paper-
dc.identifier.emailTing, HF: hfting@cs.hku.hk-
dc.identifier.emailLam, TW: twlam@cs.hku.hk-
dc.identifier.emailYiu, SM: smyiu@cs.hku.hk-
dc.identifier.authorityTing, HF=rp00177-
dc.identifier.authorityLam, TW=rp00135-
dc.identifier.authorityYiu, SM=rp00207-
dc.identifier.doi10.1007/978-3-319-19048-8_18-
dc.identifier.hkuros252494-
dc.identifier.volume9096-
dc.identifier.spage211-
dc.identifier.epage222-
dc.publisher.placeGermany-
dc.customcontrol.immutablesml 151014-

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