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Nucleic Acids Res. 2017 Apr 7;45(6):e44. doi: 10.1093/nar/gkw1193.

QSEA-modelling of genome-wide DNA methylation from sequencing enrichment experiments.

Author information

1
Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Berlin 14195, Germany.
2
Functional Epigenomics, University Hospital Cologne, Cologne 50937, Germany.
3
Experimental Pharmacology & Oncology Berlin-Buch GmbH, Berlin 13125, Germany.
4
Department of Thoracic Surgery, ELK Berlin Chest Hospital, Berlin 13125, Germany.
5
Cancer Genome Research Group, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg 69120, Germany.
6
Sequencing Core Facility, Max-Planck-Institute for Molecular Genetics, Berlin 14195, Germany.
7
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
8
Department of Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics, Berlin 14195, Germany.

Abstract

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).

PMID:
27913729
PMCID:
PMC5389680
DOI:
10.1093/nar/gkw1193
[Indexed for MEDLINE]
Free PMC Article

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