Home > Publications database > Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set > print

001     878102
005     20220930130246.0
024 7 _ |a 10.1261/rna.073809.119
|2 doi
024 7 _ |a 1355-8382
|2 ISSN
024 7 _ |a 1469-9001
|2 ISSN
024 7 _ |a altmetric:79607459
|2 altmetric
024 7 _ |a pmid:32276988
|2 pmid
024 7 _ |a WOS:000541897400003
|2 WOS
037 _ _ |a FZJ-2020-02633
082 _ _ |a 610
100 1 _ |a Pucci, Fabrizio
|0 P:(DE-Juel1)177018
|b 0
|e Corresponding author
|u fzj
245 _ _ |a Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set
260 _ _ |a Stanford, Calif.
|c 2020
|b HighWire Press
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1601908059_7720
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a RNA 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.
536 _ _ |a 511 - Computational Science and Mathematical Methods (POF3-511)
|0 G:(DE-HGF)POF3-511
|c POF3-511
|f POF III
|x 0
536 _ _ |a Forschergruppe Schug (hkf6_20170901)
|0 G:(DE-Juel1)hkf6_20170901
|c hkf6_20170901
|f Forschergruppe Schug
|x 1
536 _ _ |0 G:(DE-Juel1)PHD-NO-GRANT-20170405
|x 2
|c PHD-NO-GRANT-20170405
|a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Zerihun, Mehari B.
|0 P:(DE-Juel1)179110
|b 1
|u fzj
700 1 _ |a Peter, Emanuel K.
|0 P:(DE-Juel1)177673
|b 2
|u fzj
700 1 _ |a Schug, Alexander
|0 P:(DE-Juel1)173652
|b 3
|u fzj
773 _ _ |a 10.1261/rna.073809.119
|g Vol. 26, no. 7, p. 794 - 802
|0 PERI:(DE-600)1475737-0
|n 7
|p 794 - 802
|t RNA
|v 26
|y 2020
|x 1469-9001
856 4 _ |u https://juser.fz-juelich.de/record/878102/files/Invoice_1167156_e43024.pdf
856 4 _ |u https://juser.fz-juelich.de/record/878102/files/Invoice_1167156_e43024.pdf?subformat=pdfa
|x pdfa
856 4 _ |u https://juser.fz-juelich.de/record/878102/files/RNA_DCA_Revision.pdf
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/878102/files/RNA_DCA_Revision.pdf?subformat=pdfa
|x pdfa
|y Restricted
909 C O |o oai:juser.fz-juelich.de:878102
|p VDB
|p OpenAPC
|p openCost
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)177018
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)179110
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)177673
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)173652
913 1 _ |a DE-HGF
|b Key Technologies
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-511
|2 G:(DE-HGF)POF3-500
|v Computational Science and Mathematical Methods
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|l Supercomputing & Big Data
914 1 _ |y 2020
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b RNA : 2018
|d 2019-12-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2019-12-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2019-12-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0310
|2 StatID
|b NCBI Molecular Biology Database
|d 2019-12-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2019-12-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2019-12-21
915 _ _ |a WoS
|0 StatID:(DE-HGF)0110
|2 StatID
|b Science Citation Index
|d 2019-12-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2019-12-21
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
|d 2019-12-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2019-12-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2019-12-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2019-12-21
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2019-12-21
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2019-12-21
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a APC
980 _ _ |a UNRESTRICTED
980 1 _ |a APC

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21