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dc.contributor.author Singhal, Sandeep K.
dc.contributor.author Usmani, Nawaid
dc.contributor.author Michiels, Stefan
dc.contributor.author Metzger-Filho, Otto
dc.contributor.author Saini, Kamal S.
dc.contributor.author Kovalchuk, Olga
dc.contributor.author Parliament, Matthew
dc.date.accessioned 2019-01-23T19:07:21Z
dc.date.available 2019-01-23T19:07:21Z
dc.date.issued 2016
dc.identifier.citation Singhal, 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.6503 en_US
dc.identifier.uri https://hdl.handle.net/10133/5270
dc.description Sherpa Romeo blue journal. Open access article. Creative Commons Attribution 3.0 LIcense (CC BY 3.0) applies. en_US
dc.description.abstract Until 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.language.iso en_US en_US
dc.publisher Impact Journals en_US
dc.subject DNA methylation en_US
dc.subject Breast cancer en_US
dc.subject Epigenetics en_US
dc.subject Expression en_US
dc.subject Microarray en_US
dc.subject.lcsh Breast--Cancer
dc.subject.lcsh Gene expression
dc.subject.lcsh Genetic regulation
dc.subject.lcsh DNA microarrays
dc.subject.lcsh Breast--Cancer--Research
dc.title Towards understanding the breast cancer epigenome: a comparison of genome-wide DNA methylation and gene expression data en_US
dc.type Article en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Biological Sciences en_US
dc.description.peer-review Yes en_US
dc.publisher.institution University of Alberta en_US
dc.publisher.institution Service de Biostatistique et d'Epidemiologie en_US
dc.publisher.institution Université Paris-Sud en_US
dc.publisher.institution Harvard Medical School en_US
dc.publisher.institution Quantum Health Analytics SPRL en_US
dc.publisher.institution University of Lethbridge en_US
dc.publisher.institution Canada Cancer and Aging Research Laboratories Ltd. en_US


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