Codon Deviation Coefficient: A novel measure for estimating codon usage bias and its statistical significance

Handle URI:
http://hdl.handle.net/10754/325470
Title:
Codon Deviation Coefficient: A novel measure for estimating codon usage bias and its statistical significance
Authors:
Zhang, Zhang; Li, Jun; Cui, Peng ( 0000-0003-3076-0070 ) ; Ding, Feng ( 0000-0001-8237-4062 ) ; Li, Ang; Townsend, Jeffrey P; Yu, Jun
Abstract:
Background: 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.
KAUST Department:
Computational Bioscience Research Center (CBRC)
Citation:
Zhang 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.
Publisher:
Springer Nature
Journal:
BMC Bioinformatics
Issue Date:
22-Mar-2012
DOI:
10.1186/1471-2105-13-43
PubMed ID:
22435713
PubMed Central ID:
PMC3368730
Type:
Article
ISSN:
14712105
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorZhang, Zhangen
dc.contributor.authorLi, Junen
dc.contributor.authorCui, Pengen
dc.contributor.authorDing, Fengen
dc.contributor.authorLi, Angen
dc.contributor.authorTownsend, Jeffrey Pen
dc.contributor.authorYu, Junen
dc.date.accessioned2014-08-27T09:52:45Z-
dc.date.available2014-08-27T09:52:45Z-
dc.date.issued2012-03-22en
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.en
dc.identifier.issn14712105en
dc.identifier.pmid22435713en
dc.identifier.doi10.1186/1471-2105-13-43en
dc.identifier.urihttp://hdl.handle.net/10754/325470en
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.en
dc.language.isoenen
dc.publisherSpringer Natureen
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.en
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en
dc.subjectBackground nucleotide compositionen
dc.subjectBootstrappingen
dc.subjectCdcen
dc.subjectCodon deviation coefficienten
dc.subjectCodon usage biasen
dc.subjectCuben
dc.subjectGC contenten
dc.subjectPurine contenten
dc.subjectStatistical significanceen
dc.subjectDeviation coefficienten
dc.subjectGC contentsen
dc.subjectNucleotide compositionen
dc.subjectEstimationen
dc.subjectGene expressionen
dc.subjectNucleotidesen
dc.subjectStatisticsen
dc.subjectamino aciden
dc.subjectArabidopsisen
dc.subjectcodonen
dc.subjectcomputer simulationen
dc.subjectDNA base compositionen
dc.subjectEscherichia colien
dc.subjectevolutionen
dc.subjectgenetic selectionen
dc.subjectgeneticsen
dc.subjectgenomeen
dc.subjectmethodologyen
dc.subjectmutationen
dc.subjectSaccharomyces cerevisiaeen
dc.subjectstatisticsen
dc.subjectAmino Acidsen
dc.subjectArabidopsisen
dc.subjectBase Compositionen
dc.subjectBiological Evolutionen
dc.subjectCodonen
dc.subjectComputer Simulationen
dc.subjectEscherichia colien
dc.subjectGenomeen
dc.subjectMutationen
dc.subjectSaccharomyces cerevisiaeen
dc.subjectSelection, Geneticen
dc.subjectStatistics as Topicen
dc.titleCodon Deviation Coefficient: A novel measure for estimating codon usage bias and its statistical significanceen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalBMC Bioinformaticsen
dc.identifier.pmcidPMC3368730en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionCAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, Chinaen
dc.contributor.institutionSchool of Biological Sciences, The University of Hong Kong, Hong Kong, Chinaen
dc.contributor.institutionDepartment of Pharmacology and Toxicology and the Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, United Statesen
dc.contributor.institutionDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, United Statesen
dc.contributor.institutionProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, United Statesen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorZhang, Zhangen
kaust.authorCui, Pengen
kaust.authorDing, Fengen
kaust.authorLi, Angen

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