Protein domain recurrence and order can enhance prediction of protein functions

Handle URI:
http://hdl.handle.net/10754/325434
Title:
Protein domain recurrence and order can enhance prediction of protein functions
Authors:
Abdel Messih, Mario A.; Chitale, Meghana; Bajic, Vladimir B. ( 0000-0001-5435-4750 ) ; Kihara, Daisuke; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Motivation: Burgeoning sequencing technologies have generated massive amounts of genomic and proteomic data. Annotating the functions of proteins identified in this data has become a big and crucial problem. Various computational methods have been developed to infer the protein functions based on either the sequences or domains of proteins. The existing methods, however, ignore the recurrence and the order of the protein domains in this function inference. Results: We developed two new methods to infer protein functions based on protein domain recurrence and domain order. Our first method, DRDO, calculates the posterior probability of the Gene Ontology terms based on domain recurrence and domain order information, whereas our second method, DRDO-NB, relies on the nave Bayes methodology using the same domain architecture information. Our large-scale benchmark comparisons show strong improvements in the accuracy of the protein function inference achieved by our new methods, demonstrating that domain recurrence and order can provide important information for inference of protein functions. The Author(s) 2012. Published by Oxford University Press.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computational Bioscience Research Center (CBRC)
Citation:
Messih MA, Chitale M, Bajic VB, Kihara D, Gao X (2012) Protein domain recurrence and order can enhance prediction of protein functions. Bioinformatics 28: i444-i450. doi:10.1093/bioinformatics/bts398.
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
Issue Date:
7-Sep-2012
DOI:
10.1093/bioinformatics/bts398
PubMed ID:
22962465
PubMed Central ID:
PMC3436825
Type:
Article
ISSN:
13674803
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAbdel Messih, Mario A.en
dc.contributor.authorChitale, Meghanaen
dc.contributor.authorBajic, Vladimir B.en
dc.contributor.authorKihara, Daisukeen
dc.contributor.authorGao, Xinen
dc.date.accessioned2014-08-27T09:51:10Z-
dc.date.available2014-08-27T09:51:10Z-
dc.date.issued2012-09-07en
dc.identifier.citationMessih MA, Chitale M, Bajic VB, Kihara D, Gao X (2012) Protein domain recurrence and order can enhance prediction of protein functions. Bioinformatics 28: i444-i450. doi:10.1093/bioinformatics/bts398.en
dc.identifier.issn13674803en
dc.identifier.pmid22962465en
dc.identifier.doi10.1093/bioinformatics/bts398en
dc.identifier.urihttp://hdl.handle.net/10754/325434en
dc.description.abstractMotivation: Burgeoning sequencing technologies have generated massive amounts of genomic and proteomic data. Annotating the functions of proteins identified in this data has become a big and crucial problem. Various computational methods have been developed to infer the protein functions based on either the sequences or domains of proteins. The existing methods, however, ignore the recurrence and the order of the protein domains in this function inference. Results: We developed two new methods to infer protein functions based on protein domain recurrence and domain order. Our first method, DRDO, calculates the posterior probability of the Gene Ontology terms based on domain recurrence and domain order information, whereas our second method, DRDO-NB, relies on the nave Bayes methodology using the same domain architecture information. Our large-scale benchmark comparisons show strong improvements in the accuracy of the protein function inference achieved by our new methods, demonstrating that domain recurrence and order can provide important information for inference of protein functions. The Author(s) 2012. Published by Oxford University Press.en
dc.language.isoenen
dc.publisherOxford University Press (OUP)en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/3.0en
dc.subjectproteinen
dc.subjectBayes theoremen
dc.subjectphysiologyen
dc.subjectprotein tertiary structureen
dc.subjectsequence analysisen
dc.subjectstatistical modelen
dc.subjectBayes Theoremen
dc.subjectModels, Statisticalen
dc.subjectProtein Structure, Tertiaryen
dc.subjectProteinsen
dc.subjectSequence Analysis, Proteinen
dc.titleProtein domain recurrence and order can enhance prediction of protein functionsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalBioinformaticsen
dc.identifier.pmcidPMC3436825en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Computer Science, Purdue University, West Lafayette, IN, United Statesen
dc.contributor.institutionDepartment of Biological Sciences, College of Science, Purdue University, West Lafayette, IN, United Statesen
dc.contributor.institutionMarkey Center for Structural Biology, Purdue University, West Lafayette, IN, United Statesen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorBajic, Vladimir B.en
kaust.authorGao, Xinen
kaust.authorAbdel Messih, Mario A.en
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