QSEA—modelling of genome-wide DNA methylation from sequencing enrichment experiments. Issue 6 (29th November 2016)

• Record Type: Journal Article
• Authors: Lienhard, Matthias; Grasse, Sabrina; Rolff, Jana; Frese, Steffen; Schirmer, Uwe; Becker, Michael; Börno, Stefan; Timmermann, Bernd; Chavez, Lukas; Sültmann, Holger; Leschber, Gunda; Fichtner, Iduna; Schweiger, Michal R; Herwig, Ralf
• Is Part Of: Nucleic acids research. Volume 45:Issue 6(2017)
• Page Start: e44
• Page End: e44
• Publication Date: 2016-11-29
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkw1193
• Languages: English
• ISSNs: 0305-1048

Abstract

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 ).