DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingBidrag til bog/antologiForskningpeer review

Resumé

DNA-methylation has a strong influence on gene expression such that differences in methylation are associated with a wide range of diseases. Array-based approaches like the Illumina 450 K or 850 K EPIC chips have been used in a wide range of studies mostly comparing a disease group with healthy control, but also to correlate with survival times, for instance. Processing, normalization, and analysis of raw data require extensive knowledge in statistics and programming languages such as R. Here we introduce DiMmer, an easy-to-use Java tool for the analysis of EWAS. A graphical user interface guides the user through preprocessing, normalization, testing for differentially methylated CpGs, and finally the discovery of differentially methylated regions (DMRs). The software performs randomization tests to compute empirical P-values, corrects for multiple testing, and requires no prior knowledge in programming. All computed results are provided as plots or tables and can be easily exported. DiMmer is thus a powerful one-stop-shop for EWAS data analysis.
OriginalsprogEngelsk
TitelData Mining for Systems Biology : Methods and Protocols
RedaktørerHiroshi Mamitsuka
ForlagHumana Press
Publikationsdato2018
Sider51-62
ISBN (Trykt)9781493985609
ISBN (Elektronisk)9781493985616
DOI
StatusUdgivet - 2018
NavnMethods in Molecular Biology
Vol/bind1807
ISSN1064-3745

Fingeraftryk

Programming Languages
DNA Methylation
Random Allocation

Citer dette

Frisch, T., Møller Gøttcke, J., Röttger, R., Tan, Q., & Baumbach, J. (2018). DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data. I H. Mamitsuka (red.), Data Mining for Systems Biology: Methods and Protocols (s. 51-62). Humana Press. Methods in Molecular Biology, Bind. 1807 https://doi.org/10.1007/978-1-4939-8561-6_5
Frisch, Tobias ; Møller Gøttcke, Jonatan ; Röttger, Richard ; Tan, Qihua ; Baumbach, Jan. / DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data. Data Mining for Systems Biology: Methods and Protocols. red. / Hiroshi Mamitsuka. Humana Press, 2018. s. 51-62 (Methods in Molecular Biology, Bind 1807).
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abstract = "DNA-methylation has a strong influence on gene expression such that differences in methylation are associated with a wide range of diseases. Array-based approaches like the Illumina 450 K or 850 K EPIC chips have been used in a wide range of studies mostly comparing a disease group with healthy control, but also to correlate with survival times, for instance. Processing, normalization, and analysis of raw data require extensive knowledge in statistics and programming languages such as R. Here we introduce DiMmer, an easy-to-use Java tool for the analysis of EWAS. A graphical user interface guides the user through preprocessing, normalization, testing for differentially methylated CpGs, and finally the discovery of differentially methylated regions (DMRs). The software performs randomization tests to compute empirical P-values, corrects for multiple testing, and requires no prior knowledge in programming. All computed results are provided as plots or tables and can be easily exported. DiMmer is thus a powerful one-stop-shop for EWAS data analysis.",
author = "Tobias Frisch and {M{\o}ller G{\o}ttcke}, Jonatan and Richard R{\"o}ttger and Qihua Tan and Jan Baumbach",
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Frisch, T, Møller Gøttcke, J, Röttger, R, Tan, Q & Baumbach, J 2018, DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data. i H Mamitsuka (red.), Data Mining for Systems Biology: Methods and Protocols. Humana Press, Methods in Molecular Biology, bind 1807, s. 51-62. https://doi.org/10.1007/978-1-4939-8561-6_5

DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data. / Frisch, Tobias; Møller Gøttcke, Jonatan; Röttger, Richard; Tan, Qihua; Baumbach, Jan.

Data Mining for Systems Biology: Methods and Protocols. red. / Hiroshi Mamitsuka. Humana Press, 2018. s. 51-62 (Methods in Molecular Biology, Bind 1807).

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingBidrag til bog/antologiForskningpeer review

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AU - Møller Gøttcke, Jonatan

AU - Röttger, Richard

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AB - DNA-methylation has a strong influence on gene expression such that differences in methylation are associated with a wide range of diseases. Array-based approaches like the Illumina 450 K or 850 K EPIC chips have been used in a wide range of studies mostly comparing a disease group with healthy control, but also to correlate with survival times, for instance. Processing, normalization, and analysis of raw data require extensive knowledge in statistics and programming languages such as R. Here we introduce DiMmer, an easy-to-use Java tool for the analysis of EWAS. A graphical user interface guides the user through preprocessing, normalization, testing for differentially methylated CpGs, and finally the discovery of differentially methylated regions (DMRs). The software performs randomization tests to compute empirical P-values, corrects for multiple testing, and requires no prior knowledge in programming. All computed results are provided as plots or tables and can be easily exported. DiMmer is thus a powerful one-stop-shop for EWAS data analysis.

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Frisch T, Møller Gøttcke J, Röttger R, Tan Q, Baumbach J. DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data. I Mamitsuka H, red., Data Mining for Systems Biology: Methods and Protocols. Humana Press. 2018. s. 51-62. (Methods in Molecular Biology, Bind 1807). https://doi.org/10.1007/978-1-4939-8561-6_5