TY - JOUR
T1 - Efficient detection of differentially methylated regions using DiMmeR
AU - Almeida, Diogo
AU - Skov, Ida
AU - Silva, Artur
AU - Vandin, Fabio
AU - Tan, Qihua
AU - Röttger, Richard
AU - Baumbach, Jan
N1 - Publisher Copyright:
© 2017 The Author.
PY - 2017/2/15
Y1 - 2017/2/15
N2 - Motivation: Epigenome-wide association studies (EWAS) generate big epidemiological datasets. 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 datasets 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 datasets. 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 datasets 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.
UR - http://www.scopus.com/inward/record.url?scp=85028302737&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btw657
DO - 10.1093/bioinformatics/btw657
M3 - Article
C2 - 27794558
AN - SCOPUS:85028302737
SN - 1367-4803
VL - 33
SP - 549
EP - 551
JO - Bioinformatics
JF - Bioinformatics
IS - 4
ER -