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RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming
Motivation: Considerable attention has been focused on predicting RNA–RNA interaction since it is a key to identifying possible targets of non-coding small RNAs that regulate gene expression post-transcriptionally. A number of computational studies have so far been devoted to predicting joint second...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935440/ https://www.ncbi.nlm.nih.gov/pubmed/20823308 http://dx.doi.org/10.1093/bioinformatics/btq372 |
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author | Kato, Yuki Sato, Kengo Hamada, Michiaki Watanabe, Yoshihide Asai, Kiyoshi Akutsu, Tatsuya |
author_facet | Kato, Yuki Sato, Kengo Hamada, Michiaki Watanabe, Yoshihide Asai, Kiyoshi Akutsu, Tatsuya |
author_sort | Kato, Yuki |
collection | PubMed |
description | Motivation: Considerable attention has been focused on predicting RNA–RNA interaction since it is a key to identifying possible targets of non-coding small RNAs that regulate gene expression post-transcriptionally. A number of computational studies have so far been devoted to predicting joint secondary structures or binding sites under a specific class of interactions. In general, there is a trade-off between range of interaction type and efficiency of a prediction algorithm, and thus efficient computational methods for predicting comprehensive type of interaction are still awaited. Results: We present RactIP, a fast and accurate prediction method for RNA–RNA interaction of general type using integer programming. RactIP can integrate approximate information on an ensemble of equilibrium joint structures into the objective function of integer programming using posterior internal and external base-paring probabilities. Experimental results on real interaction data show that prediction accuracy of RactIP is at least comparable to that of several state-of-the-art methods for RNA–RNA interaction prediction. Moreover, we demonstrate that RactIP can run incomparably faster than competitive methods for predicting joint secondary structures. Availability: RactIP is implemented in C++, and the source code is available at http://www.ncrna.org/software/ractip/ Contact: ykato@kuicr.kyoto-u.ac.jp; satoken@k.u-tokyo.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Text |
id | pubmed-2935440 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-29354402010-09-08 RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming Kato, Yuki Sato, Kengo Hamada, Michiaki Watanabe, Yoshihide Asai, Kiyoshi Akutsu, Tatsuya Bioinformatics Eccb 2010 Conference Proceedings September 26 to September 29, 2010, Ghent, Belgium Motivation: Considerable attention has been focused on predicting RNA–RNA interaction since it is a key to identifying possible targets of non-coding small RNAs that regulate gene expression post-transcriptionally. A number of computational studies have so far been devoted to predicting joint secondary structures or binding sites under a specific class of interactions. In general, there is a trade-off between range of interaction type and efficiency of a prediction algorithm, and thus efficient computational methods for predicting comprehensive type of interaction are still awaited. Results: We present RactIP, a fast and accurate prediction method for RNA–RNA interaction of general type using integer programming. RactIP can integrate approximate information on an ensemble of equilibrium joint structures into the objective function of integer programming using posterior internal and external base-paring probabilities. Experimental results on real interaction data show that prediction accuracy of RactIP is at least comparable to that of several state-of-the-art methods for RNA–RNA interaction prediction. Moreover, we demonstrate that RactIP can run incomparably faster than competitive methods for predicting joint secondary structures. Availability: RactIP is implemented in C++, and the source code is available at http://www.ncrna.org/software/ractip/ Contact: ykato@kuicr.kyoto-u.ac.jp; satoken@k.u-tokyo.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2010-09-15 2010-09-04 /pmc/articles/PMC2935440/ /pubmed/20823308 http://dx.doi.org/10.1093/bioinformatics/btq372 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Eccb 2010 Conference Proceedings September 26 to September 29, 2010, Ghent, Belgium Kato, Yuki Sato, Kengo Hamada, Michiaki Watanabe, Yoshihide Asai, Kiyoshi Akutsu, Tatsuya RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming |
title | RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming |
title_full | RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming |
title_fullStr | RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming |
title_full_unstemmed | RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming |
title_short | RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming |
title_sort | ractip: fast and accurate prediction of rna-rna interaction using integer programming |
topic | Eccb 2010 Conference Proceedings September 26 to September 29, 2010, Ghent, Belgium |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935440/ https://www.ncbi.nlm.nih.gov/pubmed/20823308 http://dx.doi.org/10.1093/bioinformatics/btq372 |
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