Introduction

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).

Publications

  1. QSEA-modelling of genome-wide DNA methylation from sequencing enrichment experiments.
    Cite this
    Lienhard M, Grasse S, Rolff J, Frese S, Schirmer U, Becker M, Börno S, Timmermann B, Chavez L, Sültmann H, Leschber G, Fichtner I, Schweiger MR, Herwig R, 2017-04-01 - Nucleic acids research

Credits

  1. Matthias Lienhard
    Developer

    Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Germany

  2. Sabrina Grasse
    Developer

    Functional Epigenomics, University Hospital Cologne, Germany

  3. Jana Rolff
    Developer

    Experimental Pharmacology & Oncology Berlin-Buch GmbH, Berlin 13125, Germany

  4. Steffen Frese
    Developer

    Department of Thoracic Surgery, ELK Berlin Chest Hospital, Germany

  5. Uwe Schirmer
    Developer

    Cancer Genome Research Group, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Germany

  6. Michael Becker
    Developer

    Experimental Pharmacology & Oncology Berlin-Buch GmbH, Berlin 13125, Germany

  7. Stefan Börno
    Developer

    Sequencing Core Facility, Max-Planck-Institute for Molecular Genetics, Germany

  8. Bernd Timmermann
    Developer

    Sequencing Core Facility, Max-Planck-Institute for Molecular Genetics, Germany

  9. Lukas Chavez
    Developer

    Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Germany

  10. Holger Sültmann
    Developer

    Cancer Genome Research Group, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Germany

  11. Gunda Leschber
    Developer

    Department of Thoracic Surgery, ELK Berlin Chest Hospital, Germany

  12. Iduna Fichtner
    Developer

    Experimental Pharmacology & Oncology Berlin-Buch GmbH, Berlin 13125, Germany

  13. Michal R Schweiger
    Developer

    Department of Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics, Germany

  14. Ralf Herwig
    Investigator

    Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Germany

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Summary
AccessionBT000340
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Country/RegionGermany
Submitted ByRalf Herwig