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

Lienhard, M., Grasse, S., Rolff, J., Frese, S., Schirmer, U., Becker, M., et al. (2017). QSEA – modelling of genome-wide DNA methylation from sequencing enrichment experiments. Nucleic Acids Research (London), 45(6): e44. doi:10.1093/nar/gkw1193.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-CDAC-1 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-CDAD-0
Genre: Journal Article

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© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

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 Creators:
Lienhard, Matthias1, Author
Grasse, Sabrina, Author
Rolff, Jana, Author
Frese, Steffen, Author
Schirmer, Uwe, Author
Becker, Michael, Author
Börno, Stefan T.2, Author              
Timmermann, Bernd2, Author              
Chavez, Lukas, Author
Sültmann, Holger, Author
Leschber, Gunda, Author
Fichtner, Iduna, Author
Schweiger, Michal R., Author
Herwig, Ralf1, Author              
Affiliations:
1Bioinformatics (Ralf Herwig), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_2385701              
2Sequencing (Head: Bernd Timmermann), Scientific Service (Head: Christoph Krukenkamp), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479670              

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Free keywords: calibration, dna, dna methylation, genome, methylation, rna workflow
 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).

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Language(s): eng - English
 Dates: 2017-04-07
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1093/nar/gkw1193
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Title: Nucleic Acids Research (London)
  Other : Nucleic Acids Res
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 45 (6) Sequence Number: e44 Start / End Page: - Identifier: ISSN: 0305-1048
CoNE: https://pure.mpg.de/cone/journals/resource/110992357379342