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dc.contributor.authorZhang, Zhang
dc.contributor.authorLi, Jun
dc.contributor.authorCui, Peng
dc.contributor.authorDing, Feng
dc.contributor.authorLi, Ang
dc.contributor.authorTownsend, Jeffrey P
dc.contributor.authorYu, Jun
dc.date.accessioned2014-08-27T09:52:45Z
dc.date.available2014-08-27T09:52:45Z
dc.date.issued2012-03-23
dc.identifier.citationZhang Z, Li J, Cui P, Ding F, Li A, et al. (2012) Codon Deviation Coefficient: a novel measure for estimating codon usage bias and its statistical significance. BMC Bioinformatics 13: 43. doi:10.1186/1471-2105-13-43.
dc.identifier.issn14712105
dc.identifier.pmid22435713
dc.identifier.doi10.1186/1471-2105-13-43
dc.identifier.urihttp://hdl.handle.net/10754/325470
dc.description.abstractBackground: Genetic mutation, selective pressure for translational efficiency and accuracy, level of gene expression, and protein function through natural selection are all believed to lead to codon usage bias (CUB). Therefore, informative measurement of CUB is of fundamental importance to making inferences regarding gene function and genome evolution. However, extant measures of CUB have not fully accounted for the quantitative effect of background nucleotide composition and have not statistically evaluated the significance of CUB in sequence analysis.Results: Here we propose a novel measure--Codon Deviation Coefficient (CDC)--that provides an informative measurement of CUB and its statistical significance without requiring any prior knowledge. Unlike previous measures, CDC estimates CUB by accounting for background nucleotide compositions tailored to codon positions and adopts the bootstrapping to assess the statistical significance of CUB for any given sequence. We evaluate CDC by examining its effectiveness on simulated sequences and empirical data and show that CDC outperforms extant measures by achieving a more informative estimation of CUB and its statistical significance.Conclusions: As validated by both simulated and empirical data, CDC provides a highly informative quantification of CUB and its statistical significance, useful for determining comparative magnitudes and patterns of biased codon usage for genes or genomes with diverse sequence compositions. 2012 Zhang et al; licensee BioMed Central Ltd.
dc.language.isoen
dc.publisherSpringer Nature
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.subjectBackground nucleotide composition
dc.subjectBootstrapping
dc.subjectCdc
dc.subjectCodon deviation coefficient
dc.subjectCodon usage bias
dc.subjectCub
dc.subjectGC content
dc.subjectPurine content
dc.subjectStatistical significance
dc.subjectDeviation coefficient
dc.subjectGC contents
dc.subjectNucleotide composition
dc.subjectEstimation
dc.subjectGene expression
dc.subjectNucleotides
dc.subjectStatistics
dc.subjectamino acid
dc.subjectArabidopsis
dc.subjectcodon
dc.subjectcomputer simulation
dc.subjectDNA base composition
dc.subjectEscherichia coli
dc.subjectevolution
dc.subjectgenetic selection
dc.subjectgenetics
dc.subjectgenome
dc.subjectmethodology
dc.subjectmutation
dc.subjectSaccharomyces cerevisiae
dc.subjectstatistics
dc.subjectAmino Acids
dc.subjectArabidopsis
dc.subjectBase Composition
dc.subjectBiological Evolution
dc.subjectCodon
dc.subjectComputer Simulation
dc.subjectEscherichia coli
dc.subjectGenome
dc.subjectMutation
dc.subjectSaccharomyces cerevisiae
dc.subjectSelection, Genetic
dc.subjectStatistics as Topic
dc.titleCodon Deviation Coefficient: A novel measure for estimating codon usage bias and its statistical significance
dc.typeArticle
dc.contributor.departmentBiological and Environmental Science and Engineering (BESE) Division
dc.contributor.departmentCenter for Desert Agriculture
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.identifier.journalBMC Bioinformatics
dc.identifier.pmcidPMC3368730
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China
dc.contributor.institutionSchool of Biological Sciences, The University of Hong Kong, Hong Kong, China
dc.contributor.institutionDepartment of Pharmacology and Toxicology and the Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, United States
dc.contributor.institutionDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, United States
dc.contributor.institutionProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, United States
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personZhang, Zhang
kaust.personCui, Peng
kaust.personDing, Feng
kaust.personLi, Ang
refterms.dateFOA2018-06-14T03:47:53Z
dc.date.published-online2012-03-23
dc.date.published-print2012


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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.