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

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

Epigenome-wide association studies (EWAS)

EWAS: overview

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.

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.

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.

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.

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.

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

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

DiMmeR: starting

DiMmeR: choosing model

DiMmeR: input file

DiMmeR: setting parameters

DiMmeR: starting permutation test

DiMmeR: evaluating p-values

DiMmeR: preparing DMR search

DiMmeR: DMR search

DiMmeR: evaluating DMRs

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

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

DiMmeR: download http://dimmer.compbio.sdu.dk

Thanks! http://www.baumbachlab.net OR  Jan Baumbach