Partition function and base pairing probabilities for RNA–RNA interaction prediction
Motivation: The RNA–RNA interaction problem (RIP) consists in finding the energetically optimal structure of two RNA molecules that bind to each other. The standard model allows secondary structures in both partners as well as additional base pairs between the two RNAs subject to certain restriction...
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| Published in | Bioinformatics Vol. 25; no. 20; pp. 2646 - 2654 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Oxford University Press
15.10.2009
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1460-2059 1367-4811 |
| DOI | 10.1093/bioinformatics/btp481 |
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| Abstract | Motivation: The RNA–RNA interaction problem (RIP) consists in finding the energetically optimal structure of two RNA molecules that bind to each other. The standard model allows secondary structures in both partners as well as additional base pairs between the two RNAs subject to certain restrictions that ensure that RIP is solvabale by a polynomial time dynamic programming algorithm. RNA–RNA binding, like RNA folding, is typically not dominated by the ground state structure. Instead, a large ensemble of alternative structures contributes to the interaction thermodynamics. Results: We present here an O(N6) time and O(N4) dynamics programming algorithm for computing the full partition function for RIP which is based on the combinatorial notion of ‘tight structures’. Albeit equivalent to recent work by H. Chitsaz and collaborators, our approach in addition provides a full-fledged computation of the base pairing probabilities, which relies on the notion of a decomposition tree for joint structures. In practise, our implementation is efficient enough to investigate, for instance, the interactions of small bacterial RNAs and their target mRNAs. Availability: The program rip is implemented in C. The source code is available for download from http://www.combinatorics.cn/cbpc/rip.html and http://www.bioinf.uni-leipzig.de/Software/rip.html. Contact: duck@santafe.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
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| AbstractList | The RNA-RNA interaction problem (RIP) consists in finding the energetically optimal structure of two RNA molecules that bind to each other. The standard model allows secondary structures in both partners as well as additional base pairs between the two RNAs subject to certain restrictions that ensure that RIP is solvabale by a polynomial time dynamic programming algorithm. RNA-RNA binding, like RNA folding, is typically not dominated by the ground state structure. Instead, a large ensemble of alternative structures contributes to the interaction thermodynamics.MOTIVATIONThe RNA-RNA interaction problem (RIP) consists in finding the energetically optimal structure of two RNA molecules that bind to each other. The standard model allows secondary structures in both partners as well as additional base pairs between the two RNAs subject to certain restrictions that ensure that RIP is solvabale by a polynomial time dynamic programming algorithm. RNA-RNA binding, like RNA folding, is typically not dominated by the ground state structure. Instead, a large ensemble of alternative structures contributes to the interaction thermodynamics.We present here an O(N(6)) time and O(N(4)) dynamics programming algorithm for computing the full partition function for RIP which is based on the combinatorial notion of 'tight structures'. Albeit equivalent to recent work by H. Chitsaz and collaborators, our approach in addition provides a full-fledged computation of the base pairing probabilities, which relies on the notion of a decomposition tree for joint structures. In practise, our implementation is efficient enough to investigate, for instance, the interactions of small bacterial RNAs and their target mRNAs.RESULTSWe present here an O(N(6)) time and O(N(4)) dynamics programming algorithm for computing the full partition function for RIP which is based on the combinatorial notion of 'tight structures'. Albeit equivalent to recent work by H. Chitsaz and collaborators, our approach in addition provides a full-fledged computation of the base pairing probabilities, which relies on the notion of a decomposition tree for joint structures. In practise, our implementation is efficient enough to investigate, for instance, the interactions of small bacterial RNAs and their target mRNAs.The program rip is implemented in C. The source code is available for download from http://www.combinatorics.cn/cbpc/rip.html and http://www.bioinf.uni-leipzig.de/Software/rip.html.AVAILABILITYThe program rip is implemented in C. The source code is available for download from http://www.combinatorics.cn/cbpc/rip.html and http://www.bioinf.uni-leipzig.de/Software/rip.html. Motivation: The RNA–RNA interaction problem (RIP) consists in finding the energetically optimal structure of two RNA molecules that bind to each other. The standard model allows secondary structures in both partners as well as additional base pairs between the two RNAs subject to certain restrictions that ensure that RIP is solvabale by a polynomial time dynamic programming algorithm. RNA–RNA binding, like RNA folding, is typically not dominated by the ground state structure. Instead, a large ensemble of alternative structures contributes to the interaction thermodynamics. Results: We present here an O(N6) time and O(N4) dynamics programming algorithm for computing the full partition function for RIP which is based on the combinatorial notion of ‘tight structures’. Albeit equivalent to recent work by H. Chitsaz and collaborators, our approach in addition provides a full-fledged computation of the base pairing probabilities, which relies on the notion of a decomposition tree for joint structures. In practise, our implementation is efficient enough to investigate, for instance, the interactions of small bacterial RNAs and their target mRNAs. Availability: The program rip is implemented in C. The source code is available for download from http://www.combinatorics.cn/cbpc/rip.html and http://www.bioinf.uni-leipzig.de/Software/rip.html. Contact: duck@santafe.edu Supplementary information: Supplementary data are available at Bioinformatics online. The RNA-RNA interaction problem (RIP) consists in finding the energetically optimal structure of two RNA molecules that bind to each other. The standard model allows secondary structures in both partners as well as additional base pairs between the two RNAs subject to certain restrictions that ensure that RIP is solvabale by a polynomial time dynamic programming algorithm. RNA-RNA binding, like RNA folding, is typically not dominated by the ground state structure. Instead, a large ensemble of alternative structures contributes to the interaction thermodynamics. We present here an O(N(6)) time and O(N(4)) dynamics programming algorithm for computing the full partition function for RIP which is based on the combinatorial notion of 'tight structures'. Albeit equivalent to recent work by H. Chitsaz and collaborators, our approach in addition provides a full-fledged computation of the base pairing probabilities, which relies on the notion of a decomposition tree for joint structures. In practise, our implementation is efficient enough to investigate, for instance, the interactions of small bacterial RNAs and their target mRNAs. The program rip is implemented in C. The source code is available for download from http://www.combinatorics.cn/cbpc/rip.html and http://www.bioinf.uni-leipzig.de/Software/rip.html. Motivation: The RNA-RNA interaction problem (RIP) consists in finding the energetically optimal structure of two RNA molecules that bind to each other. The standard model allows secondary structures in both partners as well as additional base pairs between the two RNAs subject to certain restrictions that ensure that RIP is solvabale by a polynomial time dynamic programming algorithm. RNA-RNA binding, like RNA folding, is typically not dominated by the ground state structure. Instead, a large ensemble of alternative structures contributes to the interaction thermodynamics. Results: We present here an O(N 6) time and O(N 4) dynamics programming algorithm for computing the full partition function for RIP which is based on the combinatorial notion of 'tight structures'. Albeit equivalent to recent work by H. Chitsaz and collaborators, our approach in addition provides a full-fledged computation of the base pairing probabilities, which relies on the notion of a decomposition tree for joint structures. In practise, our implementation is efficient enough to investigate, for instance, the interactions of small bacterial RNAs and their target mRNAs. Availability: The program rip is implemented in C. The source code is available for download from http://www.combinatorics.cn/cbpc/rip.html and http://www.bioinf.uni-leipzig.de/Software/rip.html. Contact: duck@santafe.edu Supplementary information: Supplementary data are available at Bioinformatics online. Motivation: The RNA–RNA interaction problem (RIP) consists in finding the energetically optimal structure of two RNA molecules that bind to each other. The standard model allows secondary structures in both partners as well as additional base pairs between the two RNAs subject to certain restrictions that ensure that RIP is solvabale by a polynomial time dynamic programming algorithm. RNA–RNA binding, like RNA folding, is typically not dominated by the ground state structure. Instead, a large ensemble of alternative structures contributes to the interaction thermodynamics. Results: We present here an O(N6) time and O(N4) dynamics programming algorithm for computing the full partition function for RIP which is based on the combinatorial notion of ‘tight structures’. Albeit equivalent to recent work by H. Chitsaz and collaborators, our approach in addition provides a full-fledged computation of the base pairing probabilities, which relies on the notion of a decomposition tree for joint structures. In practise, our implementation is efficient enough to investigate, for instance, the interactions of small bacterial RNAs and their target mRNAs. Availability: The program rip is implemented in C. The source code is available for download from http://www.combinatorics.cn/cbpc/rip.html and http://www.bioinf.uni-leipzig.de/Software/rip.html. Contact: duck@santafe.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
| Author | Huang, Fenix W. D. Qin, Jing Stadler, Peter F. Reidys, Christian M. |
| Author_xml | – sequence: 1 givenname: Fenix W. D. surname: Huang fullname: Huang, Fenix W. D. organization: Center for Combinatorics, LPMC-TJKLC, College of Life Science, Nankai University Tianjin 300071, P.R. China, Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, RNomics Group, Fraunhofer Institut for Cell Therapy and Immunology, Perlickstraße 1,D-04103 Leipzig, Germany, Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria and The Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM, USA – sequence: 2 givenname: Jing surname: Qin fullname: Qin, Jing organization: Center for Combinatorics, LPMC-TJKLC, College of Life Science, Nankai University Tianjin 300071, P.R. China, Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, RNomics Group, Fraunhofer Institut for Cell Therapy and Immunology, Perlickstraße 1,D-04103 Leipzig, Germany, Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria and The Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM, USA – sequence: 3 givenname: Christian M. surname: Reidys fullname: Reidys, Christian M. organization: Center for Combinatorics, LPMC-TJKLC, College of Life Science, Nankai University Tianjin 300071, P.R. China, Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, RNomics Group, Fraunhofer Institut for Cell Therapy and Immunology, Perlickstraße 1,D-04103 Leipzig, Germany, Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria and The Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM, USA – sequence: 4 givenname: Peter F. surname: Stadler fullname: Stadler, Peter F. organization: Center for Combinatorics, LPMC-TJKLC, College of Life Science, Nankai University Tianjin 300071, P.R. China, Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, RNomics Group, Fraunhofer Institut for Cell Therapy and Immunology, Perlickstraße 1,D-04103 Leipzig, Germany, Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria and The Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM, USA |
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| Keywords | Partition Partition function RNA Interaction Prediction Probability Base pairing |
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| Notes | ArticleID:btp481 istex:C88077A326C7408C76A8949D01D1D374344A5896 To whom correspondence should be addressed. Associate Editor: Ivo Hofacker ark:/67375/HXZ-5PFMKBZT-Q ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
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| Snippet | Motivation: The RNA–RNA interaction problem (RIP) consists in finding the energetically optimal structure of two RNA molecules that bind to each other. The... Motivation: The RNA-RNA interaction problem (RIP) consists in finding the energetically optimal structure of two RNA molecules that bind to each other. The... The RNA-RNA interaction problem (RIP) consists in finding the energetically optimal structure of two RNA molecules that bind to each other. The standard model... |
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| SubjectTerms | Algorithms Base Pairing Biological and medical sciences Computational Biology - methods Databases, Genetic Fundamental and applied biological sciences. Psychology General aspects Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Nucleic Acid Conformation RNA - chemistry RNA - metabolism Thermodynamics |
| Title | Partition function and base pairing probabilities for RNA–RNA interaction prediction |
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