1. Discovery of Multiple Differentially Methylated Regions
DiMmeR
Discovery of Multiple Differentially Methylated Regions
November 2016
by Jan Baumbach

2. vs.
Jan Baumbach
Computational Biology group
University of Southern Denmark
Odense, DK

3. Epigenome-wide association studies
(EWAS)

4. EWAS: overview

5. EWAS: overview
DNA methylation analysis: Discovery of differentially methylated:
CpG sites
Regions
Are the differentially methylated regions related to an observed phenotype?
Different exposures affect the maternal milk composition and lead to different methylation patterns in mice, and consequently to differences in the regulation of gene expression.
Feil, R & Fraga, M. Epigenetics and the environment: emerging patterns and implications. Nature Rev.|Genetics 2012 February;13:97.

6. EWAS: overview Data: Sequence - Whole Genome Bisulfite seq
- Reduced Representation Bisulfite seq - Methylcap-seq
Microarray
Illumina Infinium Bead Chips 450K
Illumina Epic Chips 850K
Sequence technologies naturally can cover more CpG positions than array technologies. However, array technologies such as Illumina 450K and 850K are very popular due its low cost and high-throughput.
Stirzaker, et. Al., Mining cancer methylomes: prospects and challenges. Trends in genetics 2014 February;30:2.

7. DNA methylation analysis: Infinium technologies
Veen diagram for the overlap (yellow) and exclusive CpG probes: green for 850K and red 450K
Genomic and functional context of the newly added 413,759 CpG sites in the MethylationEPIC BeadChip microarray.
The 850K microarray covers >90% of the 450K sites, but adds 333,265 CpGs located in enhancer regions
Moran, ., Mining cancer methylomes: prospects and challenges. Trends in genetics 2014 February;30:2.

8. DNA methylation analysis: tools available
OmicTools.com: ~52 tools available for DNA methylation analysis using 450K or 850K data
Minfi
WaterMelon
RNBeads
ChAMP
DMRCate
FastDMA
IDAT I/O ✔ ✗
Probe Filtering
Background Correction
Probe rescaling
Cell composition estimation
Batch effect analysis
Single CpG statistics
DMR search
All of them are R-packages.

9. DNA methylation analysis: DiMmeR
Get rid of R-scripting, and perform your DNA methylation analysis right away and fully parallelized with DiMmeR.
Input
Output
It uses as input raw methylation array data and outputs de-methylated positions and regions from the genome, associated to a specific phenotype/trait
DNA methylation data (*.idat files)
Identification of differentially methylated positions or regions, associated to a specific trait/disease.

10. DiMmeR: workflow
Pre-processing and downstream analysis in 9 steps with a Run-Wizard guide

11. Case study: PCOS Polycystic ovarian syndrome: 24 samples
Case control study (12 health) (12 sick)
We want to find differentially methylated regions, which can be associated with this disease.
Let’s see how it works with DiMmeR…
healthy
Sick

12. DiMmeR: starting

13. DiMmeR: choosing model

14. DiMmeR: input file

15. DiMmeR: setting parameters

16. DiMmeR: starting permutation test

17. DiMmeR: evaluating p-values

18. DiMmeR: preparing DMR search

19. DiMmeR: DMR search

20. DiMmeR: evaluating DMRs

21. DiMmeR
More downstream analysis can be performed outside DiMmeR using links to GREAT and the UCSC Genome Browser…

22. DiMmeR: summary GUI Fast Easy to use Full IDAT pre-processing steps
Parallelized permutations
Empirical p-values & multiple test correction
Twins and singletons cohorts
Whole independent of external packages or libraries

23. DiMmeR: download

24. Thanks! OR
 Jan Baumbach