Genome-wide DNA methylation detection by MethylCap-seq and Infinium HumanMethylation450 BeadChips: an independent large-scale comparison.
Two cost-efficient genome-scale methodologies to assess DNA-methylation are MethylCap-seq and Illumina's Infinium HumanMethylation450 BeadChips (HM450). Objective information regarding the best-suited methodology for a specific research question is scant. Therefore, we performed a large-scale evaluation on a set of 70 brain tissue samples, i.e. 65 glioblastoma and 5 non-tumoral tissues. As MethylCap-seq coverages were limited, we focused on the inherent capacity of the methodology to detect methylated loci rather than a quantitative analysis. MethylCap-seq and HM450 data were dichotomized and performances were compared using a gold standard free Bayesian modelling procedure. While conditional specificity was adequate for both approaches, conditional sensitivity was systematically higher for HM450. In addition, genome-wide characteristics were compared, revealing that HM450 probes identified substantially fewer regions compared to MethylCap-seq. Although results indicated that the latter method can detect more potentially relevant DNA-methylation, this did not translate into the discovery of more differentially methylated loci between tumours and controls compared to HM450. Our results therefore indicate that both methodologies are complementary, with a higher sensitivity for HM450 and a far larger genome-wide coverage for MethylCap-seq, but also that a more comprehensive character does not automatically imply more significant results in biomarker studies.
Dept. of Mathematical Modelling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.
Tim
T
De Meyer
Lab. of Brain Tumor Biology and Genetics, Department of Clinical Neurosciences, University Hospital Lausanne, Lausanne, Switzerland.|Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Département de Formation et Recherche, University Hospital/University of Lausanne, Lausanne, Switzerland.
Pierre
P
Bady
Dept. of Mathematical Modelling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.
Geert
G
Trooskens
Lab. of Brain Tumor Biology and Genetics, Department of Clinical Neurosciences, University Hospital Lausanne, Lausanne, Switzerland.|Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Neurosurgery, Department of Clinical Neurosciences, University Hospital Lausanne, Lausanne, Switzerland.
Sebastian
S
Kurscheid
Neurosurgery, Department of Clinical Neurosciences, University Hospital Lausanne, Lausanne, Switzerland.
Jocelyne
J
Bloch
Division of Neuropathology, University Hospital Rotterdam, Rotterdam, The Netherlands.
Johan M
JM
Kros
Institute of Neurology, Medical University of Vienna, Vienna, Austria.
Johannes A
JA
Hainfellner
Neurosurgery, Department of Clinical Neurosciences, University Hospital Lausanne, Lausanne, Switzerland.|Department of Oncology, University Hospital Zurich, Zurich, Switzerland.
Roger
R
Stupp
Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Département de Formation et Recherche, University Hospital/University of Lausanne, Lausanne, Switzerland.
Mauro
M
Delorenzi
Lab. of Brain Tumor Biology and Genetics, Department of Clinical Neurosciences, University Hospital Lausanne, Lausanne, Switzerland.|Neurosurgery, Department of Clinical Neurosciences, University Hospital Lausanne, Lausanne, Switzerland.
Monika E
ME
Hegi
Dept. of Mathematical Modelling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.
Wim
W
Van Criekinge
D000483:Alleles; D016022:Case-Control Studies; D019295:Computational Biology / Q000379:methods; D018899:CpG Islands; D019175:DNA Methylation; D044127:Epigenesis, Genetic; D057890:Epigenomics / Q000379:methods / Q000592:standards; D055106:Genome-Wide Association Study / Q000379:methods / Q000592:standards; D005909:Glioblastoma / Q000235:genetics; D006801:Humans; D058977:Molecular Sequence Annotation; D012680:Sensitivity and Specificity