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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description | 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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D. ; Qin, Jing ; Reidys, Christian M. ; Stadler, Peter F.</creator><creatorcontrib>Huang, Fenix W. D. ; Qin, Jing ; Reidys, Christian M. ; Stadler, Peter F.</creatorcontrib><description>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.</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1460-2059</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btp481</identifier><identifier>PMID: 19671692</identifier><language>eng</language><publisher>Oxford: Oxford University Press</publisher><subject>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</subject><ispartof>Bioinformatics, 2009-10, Vol.25 (20), p.2646-2654</ispartof><rights>The Author 2009. Published by Oxford University Press. All rights reserved. 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D.</creatorcontrib><creatorcontrib>Qin, Jing</creatorcontrib><creatorcontrib>Reidys, Christian M.</creatorcontrib><creatorcontrib>Stadler, Peter F.</creatorcontrib><title>Partition function and base pairing probabilities for RNA–RNA interaction prediction</title><title>Bioinformatics</title><addtitle>Bioinformatics</addtitle><description>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.</description><subject>Algorithms</subject><subject>Base Pairing</subject><subject>Biological and medical sciences</subject><subject>Computational Biology - methods</subject><subject>Databases, Genetic</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects</subject><subject>Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)</subject><subject>Nucleic Acid Conformation</subject><subject>RNA - chemistry</subject><subject>RNA - metabolism</subject><subject>Thermodynamics</subject><issn>1367-4803</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkM1u1DAURi1ExbTTPgIoG8QqjB3b8Xg5VEwH9Ydp1SLUjXXt2MiQSVI7kWDXd-gb8iR1m9Egdt3YV_L5_F0dhN4S_JFgSWfat75xbdhA702c6b5jc_IK7RNW4rzAXL5OMy1FzuaYTtBBjD8x5oQx9gZNiCwFKWWxj76tIfS-922TuaExzwM0VaYh2qwDH3zzI-tCq0H7OnE2Zqk0u7pY_L1_SGfmm94GGINdsJV_Hg_RnoM62qPtPUU3y8_Xx6v87OvJl-PFWW44xX2upTSmpE4So00BFQA3glWWFYYwx3gpMaZSCy50AUQ7JgqTno2oiMMlo3SKPoz_phXvBht7tfHR2LqGxrZDVIIyPE8ySCL5SJrQxhisU13wGwh_FMHqyaj636gajabcu23DoDe2-pfaKkzA-y0A0UDtAjTGxx1XFEk7n_PE4ZFrh-7F3fkY8bG3v3chCL9UKajgavX9VvH18vz00-21uqSPvEunQA</recordid><startdate>20091015</startdate><enddate>20091015</enddate><creator>Huang, Fenix W. D.</creator><creator>Qin, Jing</creator><creator>Reidys, Christian M.</creator><creator>Stadler, Peter F.</creator><general>Oxford University Press</general><scope>BSCLL</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20091015</creationdate><title>Partition function and base pairing probabilities for RNA–RNA interaction prediction</title><author>Huang, Fenix W. D. ; Qin, Jing ; Reidys, Christian M. ; Stadler, Peter F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c530t-b99cc63f91cbc2adaa5c74de42c14f45690039b757b2a1bf472c4dec7d1f06433</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Algorithms</topic><topic>Base Pairing</topic><topic>Biological and medical sciences</topic><topic>Computational Biology - methods</topic><topic>Databases, Genetic</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects</topic><topic>Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)</topic><topic>Nucleic Acid Conformation</topic><topic>RNA - chemistry</topic><topic>RNA - metabolism</topic><topic>Thermodynamics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Fenix W. D.</creatorcontrib><creatorcontrib>Qin, Jing</creatorcontrib><creatorcontrib>Reidys, Christian M.</creatorcontrib><creatorcontrib>Stadler, Peter F.</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Huang, Fenix W. D.</au><au>Qin, Jing</au><au>Reidys, Christian M.</au><au>Stadler, Peter F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Partition function and base pairing probabilities for RNA–RNA interaction prediction</atitle><jtitle>Bioinformatics</jtitle><addtitle>Bioinformatics</addtitle><date>2009-10-15</date><risdate>2009</risdate><volume>25</volume><issue>20</issue><spage>2646</spage><epage>2654</epage><pages>2646-2654</pages><issn>1367-4803</issn><eissn>1460-2059</eissn><eissn>1367-4811</eissn><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.</abstract><cop>Oxford</cop><pub>Oxford University Press</pub><pmid>19671692</pmid><doi>10.1093/bioinformatics/btp481</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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subjects | 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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