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

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

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.
Original languageEnglish
Title of host publicationData Mining for Systems Biology : Methods and Protocols
EditorsHiroshi Mamitsuka
PublisherHumana Press
Publication date2018
Pages51-62
ISBN (Print)9781493985609
ISBN (Electronic)9781493985616
DOIs
Publication statusPublished - 2018
SeriesMethods in Molecular Biology
Volume1807
ISSN1064-3745

Fingerprint

Programming Languages
DNA Methylation
Random Allocation

Cite this

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. In H. Mamitsuka (Ed.), Data Mining for Systems Biology: Methods and Protocols (pp. 51-62). Humana Press. Methods in Molecular Biology, Vol.. 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. editor / Hiroshi Mamitsuka. Humana Press, 2018. pp. 51-62 (Methods in Molecular Biology, Vol. 1807).
@inbook{d76adc1083144fe291011923abcbd0e0,
title = "DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data",
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",
year = "2018",
doi = "10.1007/978-1-4939-8561-6_5",
language = "English",
isbn = "9781493985609",
pages = "51--62",
editor = "Hiroshi Mamitsuka",
booktitle = "Data Mining for Systems Biology",
publisher = "Humana Press",
address = "United States",

}

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. in H Mamitsuka (ed.), Data Mining for Systems Biology: Methods and Protocols. Humana Press, Methods in Molecular Biology, vol. 1807, pp. 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. ed. / Hiroshi Mamitsuka. Humana Press, 2018. p. 51-62.

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

TY - CHAP

T1 - DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data

AU - Frisch, Tobias

AU - Møller Gøttcke, Jonatan

AU - Röttger, Richard

AU - Tan, Qihua

AU - Baumbach, Jan

PY - 2018

Y1 - 2018

N2 - 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.

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.

U2 - 10.1007/978-1-4939-8561-6_5

DO - 10.1007/978-1-4939-8561-6_5

M3 - Book chapter

SN - 9781493985609

SP - 51

EP - 62

BT - Data Mining for Systems Biology

A2 - Mamitsuka, Hiroshi

PB - Humana Press

ER -

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