000878102 001__ 878102
000878102 005__ 20220930130246.0
000878102 0247_ $$2doi$$a10.1261/rna.073809.119
000878102 0247_ $$2ISSN$$a1355-8382
000878102 0247_ $$2ISSN$$a1469-9001
000878102 0247_ $$2altmetric$$aaltmetric:79607459
000878102 0247_ $$2pmid$$apmid:32276988
000878102 0247_ $$2WOS$$aWOS:000541897400003
000878102 037__ $$aFZJ-2020-02633
000878102 082__ $$a610
000878102 1001_ $$0P:(DE-Juel1)177018$$aPucci, Fabrizio$$b0$$eCorresponding author$$ufzj
000878102 245__ $$aEvaluating DCA-based method performances for RNA contact prediction by a well-curated data set
000878102 260__ $$aStanford, Calif.$$bHighWire Press$$c2020
000878102 3367_ $$2DRIVER$$aarticle
000878102 3367_ $$2DataCite$$aOutput Types/Journal article
000878102 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1601908059_7720
000878102 3367_ $$2BibTeX$$aARTICLE
000878102 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000878102 3367_ $$00$$2EndNote$$aJournal Article
000878102 520__ $$aRNA molecules play many pivotal roles in a cell that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analyzed by methods such as direct coupling analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated data set of about 70 RNA structures of high resolution and compare different nucleotide–nucleotide contact prediction methods available in the literature. We observe only minor differences between the performances of the different methods. Moreover, we discuss how robust these predictions are for different contact definitions and how strongly they depend on procedures used to curate and align the families of homologous RNA sequences.
000878102 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000878102 536__ $$0G:(DE-Juel1)hkf6_20170901$$aForschergruppe Schug (hkf6_20170901)$$chkf6_20170901$$fForschergruppe Schug$$x1
000878102 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x2
000878102 588__ $$aDataset connected to CrossRef
000878102 7001_ $$0P:(DE-Juel1)179110$$aZerihun, Mehari B.$$b1$$ufzj
000878102 7001_ $$0P:(DE-Juel1)177673$$aPeter, Emanuel K.$$b2$$ufzj
000878102 7001_ $$0P:(DE-Juel1)173652$$aSchug, Alexander$$b3$$ufzj
000878102 773__ $$0PERI:(DE-600)1475737-0$$a10.1261/rna.073809.119$$gVol. 26, no. 7, p. 794 - 802$$n7$$p794 - 802$$tRNA$$v26$$x1469-9001$$y2020
000878102 8564_ $$uhttps://juser.fz-juelich.de/record/878102/files/Invoice_1167156_e43024.pdf
000878102 8564_ $$uhttps://juser.fz-juelich.de/record/878102/files/Invoice_1167156_e43024.pdf?subformat=pdfa$$xpdfa
000878102 8564_ $$uhttps://juser.fz-juelich.de/record/878102/files/RNA_DCA_Revision.pdf$$yRestricted
000878102 8564_ $$uhttps://juser.fz-juelich.de/record/878102/files/RNA_DCA_Revision.pdf?subformat=pdfa$$xpdfa$$yRestricted
000878102 8767_ $$81167156$$92020-07-07$$d2020-09-17$$eHybrid-OA$$jZahlung erfolgt$$z1500.00 USD
000878102 8767_ $$81167156$$92020-07-07$$d2020-09-17$$eOther$$jZahlung erfolgt$$z59.40 USD
000878102 909CO $$ooai:juser.fz-juelich.de:878102$$pOpenAPC$$pVDB$$popenCost
000878102 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)177018$$aForschungszentrum Jülich$$b0$$kFZJ
000878102 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179110$$aForschungszentrum Jülich$$b1$$kFZJ
000878102 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)177673$$aForschungszentrum Jülich$$b2$$kFZJ
000878102 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173652$$aForschungszentrum Jülich$$b3$$kFZJ
000878102 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000878102 9141_ $$y2020
000878102 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bRNA : 2018$$d2019-12-21
000878102 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2019-12-21
000878102 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2019-12-21
000878102 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database$$d2019-12-21
000878102 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2019-12-21
000878102 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2019-12-21
000878102 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index$$d2019-12-21
000878102 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2019-12-21
000878102 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2019-12-21
000878102 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2019-12-21
000878102 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2019-12-21
000878102 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2019-12-21
000878102 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2019-12-21
000878102 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2019-12-21
000878102 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000878102 980__ $$ajournal
000878102 980__ $$aVDB
000878102 980__ $$aI:(DE-Juel1)JSC-20090406
000878102 980__ $$aAPC
000878102 980__ $$aUNRESTRICTED
000878102 9801_ $$aAPC