Efficient detection of differentially methylated regions using DiMmeR

Diogo Marinho Almeida, Ida Uhrenfeldt Skov, Artur Silva, Fabio Vandin, Qihua Tan, Richard Röttger, Jan Baumbach

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

MOTIVATION:

Epigenome-wide association studies (EWAS) generate big epidemiological data sets. They aim for detecting differentially methylated DNA regions that are likely to influence transcriptional gene activity and, thus, the regulation of metabolic processes. The by far most widely used technology is the Illumina Methylation BeadChip, which measures the methylation levels of 450 (850) thousand cytosines, in the CpG dinucleotide context in a set of patients compared to a control group. Many bioinformatics tools exist for raw data analysis. However, most of them require some knowledge in the programming language R, have no user interface, and do not offer all necessary steps to guide users from raw data all the way down to statistically significant differentially methylated regions (DMRs) and the associated genes.

RESULTS:

Here, we present DiMmeR (Discovery of Multiple Differentially Methylated Regions), the first free standalone software that interactively guides with a user-friendly graphical user interface (GUI) scientists the whole way through EWAS data analysis. It offers parallelized statistical methods for efficiently identifying DMRs in both Illumina 450K and 850K EPIC chip data. DiMmeR computes empirical p-values through randomization tests, even for big data sets of hundreds of patients and thousands of permutations within a few minutes on a standard desktop PC. It is independent of any third-party libraries, computes regression coefficients, p-values and empirical p-values, and it corrects for multiple testing.
Original languageEnglish
JournalBioinformatics
Volume33
Issue number4
Pages (from-to)549-551
ISSN1367-4803
DOIs
Publication statusPublished - 2017

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Programming Languages
Cytosine
Random Allocation
Computational Biology
Libraries
Control Groups
DNA
Datasets

Cite this

Almeida, Diogo Marinho ; Uhrenfeldt Skov, Ida ; Silva, Artur ; Vandin, Fabio ; Tan, Qihua ; Röttger, Richard ; Baumbach, Jan. / Efficient detection of differentially methylated regions using DiMmeR. In: Bioinformatics. 2017 ; Vol. 33, No. 4. pp. 549-551.
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abstract = "MOTIVATION: Epigenome-wide association studies (EWAS) generate big epidemiological data sets. They aim for detecting differentially methylated DNA regions that are likely to influence transcriptional gene activity and, thus, the regulation of metabolic processes. The by far most widely used technology is the Illumina Methylation BeadChip, which measures the methylation levels of 450 (850) thousand cytosines, in the CpG dinucleotide context in a set of patients compared to a control group. Many bioinformatics tools exist for raw data analysis. However, most of them require some knowledge in the programming language R, have no user interface, and do not offer all necessary steps to guide users from raw data all the way down to statistically significant differentially methylated regions (DMRs) and the associated genes.RESULTS: Here, we present DiMmeR (Discovery of Multiple Differentially Methylated Regions), the first free standalone software that interactively guides with a user-friendly graphical user interface (GUI) scientists the whole way through EWAS data analysis. It offers parallelized statistical methods for efficiently identifying DMRs in both Illumina 450K and 850K EPIC chip data. DiMmeR computes empirical p-values through randomization tests, even for big data sets of hundreds of patients and thousands of permutations within a few minutes on a standard desktop PC. It is independent of any third-party libraries, computes regression coefficients, p-values and empirical p-values, and it corrects for multiple testing.",
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Efficient detection of differentially methylated regions using DiMmeR. / Almeida, Diogo Marinho; Uhrenfeldt Skov, Ida; Silva, Artur; Vandin, Fabio; Tan, Qihua; Röttger, Richard; Baumbach, Jan.

In: Bioinformatics, Vol. 33, No. 4, 2017, p. 549-551.

Research output: Contribution to journalJournal articleResearchpeer-review

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T1 - Efficient detection of differentially methylated regions using DiMmeR

AU - Almeida, Diogo Marinho

AU - Uhrenfeldt Skov, Ida

AU - Silva, Artur

AU - Vandin, Fabio

AU - Tan, Qihua

AU - Röttger, Richard

AU - Baumbach, Jan

PY - 2017

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N2 - MOTIVATION: Epigenome-wide association studies (EWAS) generate big epidemiological data sets. They aim for detecting differentially methylated DNA regions that are likely to influence transcriptional gene activity and, thus, the regulation of metabolic processes. The by far most widely used technology is the Illumina Methylation BeadChip, which measures the methylation levels of 450 (850) thousand cytosines, in the CpG dinucleotide context in a set of patients compared to a control group. Many bioinformatics tools exist for raw data analysis. However, most of them require some knowledge in the programming language R, have no user interface, and do not offer all necessary steps to guide users from raw data all the way down to statistically significant differentially methylated regions (DMRs) and the associated genes.RESULTS: Here, we present DiMmeR (Discovery of Multiple Differentially Methylated Regions), the first free standalone software that interactively guides with a user-friendly graphical user interface (GUI) scientists the whole way through EWAS data analysis. It offers parallelized statistical methods for efficiently identifying DMRs in both Illumina 450K and 850K EPIC chip data. DiMmeR computes empirical p-values through randomization tests, even for big data sets of hundreds of patients and thousands of permutations within a few minutes on a standard desktop PC. It is independent of any third-party libraries, computes regression coefficients, p-values and empirical p-values, and it corrects for multiple testing.

AB - MOTIVATION: Epigenome-wide association studies (EWAS) generate big epidemiological data sets. They aim for detecting differentially methylated DNA regions that are likely to influence transcriptional gene activity and, thus, the regulation of metabolic processes. The by far most widely used technology is the Illumina Methylation BeadChip, which measures the methylation levels of 450 (850) thousand cytosines, in the CpG dinucleotide context in a set of patients compared to a control group. Many bioinformatics tools exist for raw data analysis. However, most of them require some knowledge in the programming language R, have no user interface, and do not offer all necessary steps to guide users from raw data all the way down to statistically significant differentially methylated regions (DMRs) and the associated genes.RESULTS: Here, we present DiMmeR (Discovery of Multiple Differentially Methylated Regions), the first free standalone software that interactively guides with a user-friendly graphical user interface (GUI) scientists the whole way through EWAS data analysis. It offers parallelized statistical methods for efficiently identifying DMRs in both Illumina 450K and 850K EPIC chip data. DiMmeR computes empirical p-values through randomization tests, even for big data sets of hundreds of patients and thousands of permutations within a few minutes on a standard desktop PC. It is independent of any third-party libraries, computes regression coefficients, p-values and empirical p-values, and it corrects for multiple testing.

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