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      An evaluation of processing methods for HumanMethylation450 BeadChip data

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          Abstract

          Background

          Illumina’s HumanMethylation450 arrays provide the most cost-effective means of high-throughput DNA methylation analysis. As with other types of microarray platforms, technical artifacts are a concern, including background fluorescence, dye-bias from the use of two color channels, bias caused by type I/II probe design, and batch effects. Several approaches and pipelines have been developed, either targeting a single issue or designed to address multiple biases through a combination of methods. We evaluate the effect of combining separate approaches to improve signal processing.

          Results

          In this study nine processing methods, including both within- and between- array methods, are applied and compared in four datasets. For technical replicates, we found both within- and between-array methods did a comparable job in reducing variance across replicates. For evaluating biological differences, within-array processing always improved differential DNA methylation signal detection over no processing, and always benefitted from performing background correction first. Combinations of within-array procedures were always among the best performing methods, with a slight advantage appearing for the between-array method Funnorm when batch effects explained more variation in the data than the methylation alterations between cases and controls. However, when this occurred, RUVm, a new batch correction method noticeably improved reproducibility of differential methylation results over any of the signal-processing methods alone.

          Conclusions

          The comparisons in our study provide valuable insights in preprocessing HumanMethylation450 BeadChip data. We found the within-array combination of Noob + BMIQ always improved signal sensitivity, and when combined with the RUVm batch-correction method, outperformed all other approaches in performing differential DNA methylation analysis. The effect of the data processing method, in any given data set, was a function of both the signal and noise.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-016-2819-7) contains supplementary material, which is available to authorized users.

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          Most cited references9

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          DNA methylation profiling of human chromosomes 6, 20 and 22

          DNA methylation constitutes the most stable type of epigenetic modifications modulating the transcriptional plasticity of mammalian genomes. Using bisulfite DNA sequencing, we report high-resolution methylation reference profiles of human chromosomes 6, 20 and 22, providing a resource of about 1.9 million CpG methylation values derived from 12 different tissues. Analysis of 6 annotation categories, revealed evolutionary conserved regions to be the predominant sites for differential DNA methylation and a core region surrounding the transcriptional start site as informative surrogate for promoter methylation. We find 17% of the 873 analyzed genes differentially methylated in their 5′-untranslated regions (5′-UTR) and about one third of the differentially methylated 5′-UTRs to be inversely correlated with transcription. While our study was controlled for factors reported to affect DNA methylation such as sex and age, we did not find any significant attributable effects. Our data suggest DNA methylation to be ontogenetically more stable than previously thought.
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            Complete pipeline for Infinium(®) Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation.

            Huge progress has been made in the development of array- or sequencing-based technologies for DNA methylation analysis. The Illumina Infinium(®) Human Methylation 450K BeadChip (Illumina Inc., CA, USA) allows the simultaneous quantitative monitoring of more than 480,000 CpG positions, enabling large-scale epigenotyping studies. However, the assay combines two different assay chemistries, which may cause a bias in the analysis if all signals are merged as a unique source of methylation measurement. We confirm in three 450K data sets that Infinium I signals are more stable and cover a wider dynamic range of methylation values than Infinium II signals. We evaluated the methylation profile of Infinium I and II probes obtained with different normalization protocols and compared these results with the methylation values of a subset of CpGs analyzed by pyrosequencing. We developed a subset quantile normalization approach for the processing of 450K BeadChips. The Infinium I signals were used as 'anchors' to normalize Infinium II signals at the level of probe coverage categories. Our normalization approach outperformed alternative normalization or correction approaches in terms of bias correction and methylation signal estimation. We further implemented a complete preprocessing protocol that solves most of the issues currently raised by 450K array users. We developed a complete preprocessing pipeline for 450K BeadChip data using an original subset quantile normalization approach that performs both sample normalization and efficient Infinium I/II shift correction. The scripts, being freely available from the authors, will allow researchers to concentrate on the biological analysis of data, such as the identification of DNA methylation signatures.
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              Cigarette smoking and DNA methylation

              DNA methylation is the most studied epigenetic modification, capable of controlling gene expression in the contexts of normal traits or diseases. It is highly dynamic during early embryogenesis and remains relatively stable throughout life, and such patterns are intricately related to human development. DNA methylation is a quantitative trait determined by a complex interplay of genetic and environmental factors. Genetic variants at a specific locus can influence both regional and distant DNA methylation. The environment can have varying effects on DNA methylation depending on when the exposure occurs, such as during prenatal life or during adulthood. In particular, cigarette smoking in the context of both current smoking and prenatal exposure is a strong modifier of DNA methylation. Epigenome-wide association studies have uncovered candidate genes associated with cigarette smoking that have biologically relevant functions in the etiology of smoking-related diseases. As such, DNA methylation is a potential mechanistic link between current smoking and cancer, as well as prenatal cigarette-smoke exposure and the development of adult chronic diseases.
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                Author and article information

                Contributors
                liu485@usc.edu
                kims@usc.edu
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                22 June 2016
                22 June 2016
                2016
                : 17
                : 469
                Affiliations
                [ ]Department of Preventive Medicine, USC Keck School of Medicine, University of Southern California, Los Angeles, USA
                [ ]Department of Preventive Medicine, USC Keck School of Medicine, 2001 N. Soto Street, Suite 202 W, Los Angeles, CA 90089 USA
                Article
                2819
                10.1186/s12864-016-2819-7
                4918139
                27334613
                1ff48bfe-4676-4bb4-9858-d63dd4302d13
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 9 October 2015
                : 8 June 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000051, National Human Genome Research Institute;
                Award ID: HG006705
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: 5P30 CA014089
                Funded by: FundRef http://dx.doi.org/10.13039/100000066, National Institute of Environmental Health Sciences;
                Award ID: P30 ES07048
                Categories
                Methodology Article
                Custom metadata
                © The Author(s) 2016

                Genetics
                humanmethylation450 beadchip,preprocessing,normalization,batch correction,concordance plot

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