Towards understanding the breast cancer epigenome: a comparison of genome-wide DNA methylation and gene expression data

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dc.contributor.authorSinghal, Sandeep K.
dc.contributor.authorUsmani, Nawaid
dc.contributor.authorMichiels, Stefan
dc.contributor.authorMetzger-Filho, Otto
dc.contributor.authorSaini, Kamal S.
dc.contributor.authorKovalchuk, Olga
dc.contributor.authorParliament, Matthew
dc.date.accessioned2019-01-23T19:07:21Z
dc.date.available2019-01-23T19:07:21Z
dc.date.issued2016
dc.descriptionSherpa Romeo blue journal. Open access article. Creative Commons Attribution 3.0 License (CC BY 3.0) applies.en_US
dc.description.abstractUntil recently, an elevated disease risk has been ascribed to a genetic predisposition, however, exciting progress over the past years has discovered alternate elements of inheritance that involve epigenetic regulation. Epigenetic changes are heritably stable alterations that include DNA methylation, histone modifications and RNA-mediated silencing. Aberrant DNA methylation is a common molecular basis for a number of important human diseases, including breast cancer. Changes in DNA methylation profoundly affect global gene expression patterns. What is emerging is a more dynamic and complex association between DNA methylation and gene expression than previously believed. Although many tools have already been developed for analyzing genome-wide gene expression data, tools for analyzing genome-wide DNA methylation have not yet reached the same level of refinement. Here we provide an in-depth analysis of DNA methylation in parallel with gene expression data characteristics and describe the particularities of low-level and highlevel analyses of DNA methylation data. Low-level analysis refers to pre-processing of methylation data (i.e. normalization, transformation and filtering), whereas high-level analysis is focused on illustrating the application of the widely used class comparison, class prediction and class discovery methods to DNA methylation data. Furthermore, we investigate the influence of DNA methylation on gene expression by measuring the correlation between the degree of CpG methylation and the level of expression and to explore the pattern of methylation as a function of the promoter region.en_US
dc.description.peer-reviewYesen_US
dc.identifier.citationSinghal, S. K., Usmani, N., Michiels, S., Metzger-Filho, O., Saini, K. S., Kovalchuck, O., & Parliament, M. (2016). Towards understanding the breast cancer epigenome: A comparison of genome-wide DNA methylation and gene expression data. Oncotarget, 7(3), 3002-3017. https://doi.org/10.18632/oncotarget.6503en_US
dc.identifier.urihttps://hdl.handle.net/10133/5270
dc.language.isoen_USen_US
dc.publisherImpact Journalsen_US
dc.publisher.departmentDepartment of Biological Sciencesen_US
dc.publisher.facultyArts and Scienceen_US
dc.publisher.institutionUniversity of Albertaen_US
dc.publisher.institutionService de Biostatistique et d'Epidemiologieen_US
dc.publisher.institutionUniversité Paris-Suden_US
dc.publisher.institutionHarvard Medical Schoolen_US
dc.publisher.institutionQuantum Health Analytics SPRLen_US
dc.publisher.institutionUniversity of Lethbridgeen_US
dc.publisher.institutionCanada Cancer and Aging Research Laboratories Ltd.en_US
dc.publisher.urlhttps://doi.org/10.18632/oncotarget.6503
dc.subjectDNA methylationen_US
dc.subjectBreast canceren_US
dc.subjectEpigeneticsen_US
dc.subjectExpressionen_US
dc.subjectMicroarrayen_US
dc.subject.lcshBreast--Cancer
dc.subject.lcshGene expression
dc.subject.lcshGenetic regulation
dc.subject.lcshDNA microarrays
dc.subject.lcshBreast--Cancer--Research
dc.titleTowards understanding the breast cancer epigenome: a comparison of genome-wide DNA methylation and gene expression dataen_US
dc.typeArticleen_US
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