TY  - JOUR
AU  - Lienhard, Matthias
AU  - Grasse, Sabrina
AU  - Rolff, Jana
AU  - Frese, Steffen
AU  - Schirmer, Uwe
AU  - Becker, Michael
AU  - Börno, Stefan
AU  - Timmermann, Bernd
AU  - Chavez, Lukas
AU  - Sültmann, Holger
AU  - Leschber, Gunda
AU  - Fichtner, Iduna
AU  - Schweiger, Michal R
AU  - Herwig, Ralf
TI  - QSEA-modelling of genome-wide DNA methylation from sequencing enrichment experiments.
JO  - Nucleic acids symposium series
VL  - 45
IS  - 6
SN  - 0261-3166
CY  - Oxford
PB  - Oxford Univ. Press
M1  - DKFZ-2017-01255
SP  - e44 - e44
PY  - 2017
AB  - Genome-wide enrichment of methylated DNA followed by sequencing (MeDIP-seq) offers a reasonable compromise between experimental costs and genomic coverage. However, the computational analysis of these experiments is complex, and quantification of the enrichment signals in terms of absolute levels of methylation requires specific transformation. In this work, we present QSEA, Quantitative Sequence Enrichment Analysis, a comprehensive workflow for the modelling and subsequent quantification of MeDIP-seq data. As the central part of the workflow we have developed a Bayesian statistical model that transforms the enrichment read counts to absolute levels of methylation and, thus, enhances interpretability and facilitates comparison with other methylation assays. We suggest several calibration strategies for the critical parameters of the model, either using additional data or fairly general assumptions. By comparing the results with bisulfite sequencing (BS) validation data, we show the improvement of QSEA over existing methods. Additionally, we generated a clinically relevant benchmark data set consisting of methylation enrichment experiments (MeDIP-seq), BS-based validation experiments (Methyl-seq) as well as gene expression experiments (RNA-seq) derived from non-small cell lung cancer patients, and show that the workflow retrieves well-known lung tumour methylation markers that are causative for gene expression changes, demonstrating the applicability of QSEA for clinical studies. QSEA is implemented in R and available from the Bioconductor repository 3.4 (www.bioconductor.org/packages/qsea).
LB  - PUB:(DE-HGF)16
C6  - pmid:27913729
C2  - pmc:PMC5389680
DO  - DOI:10.1093/nar/gkw1193
UR  - https://inrepo02.dkfz.de/record/124376
ER  -