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      ChAMP: updated methylation analysis pipeline for Illumina BeadChips

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          Abstract

          Summary

          The Illumina Infinium HumanMethylationEPIC BeadChip is the new platform for high-throughput DNA methylation analysis, effectively doubling the coverage compared to the older 450 K array. Here we present a significantly updated and improved version of the Bioconductor package ChAMP, which can be used to analyze EPIC and 450k data. Many enhanced functionalities have been added, including correction for cell-type heterogeneity, network analysis and a series of interactive graphical user interfaces.

          Availability and implementation

          ChAMP is a BioC package available from https://bioconductor.org/packages/release/bioc/html/ChAMP.html.

          Contact

          a.teschendorff@ 123456ucl.ac.uk or s.beck@ 123456ucl.ac.uk or a.feber@ 123456ucl.ac.uk

          Supplementary information

          Supplementary data are available at Bioinformatics online.

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          De novo identification of differentially methylated regions in the human genome

          Timothy J. Peters, Michael Buckley, Aaron L Statham … (2015)
          Background The identification and characterisation of differentially methylated regions (DMRs) between phenotypes in the human genome is of prime interest in epigenetics. We present a novel method, DMRcate, that fits replicated methylation measurements from the Illumina HM450K BeadChip (or 450K array) spatially across the genome using a Gaussian kernel. DMRcate identifies and ranks the most differentially methylated regions across the genome based on tunable kernel smoothing of the differential methylation (DM) signal. The method is agnostic to both genomic annotation and local change in the direction of the DM signal, removes the bias incurred from irregularly spaced methylation sites, and assigns significance to each DMR called via comparison to a null model. Results We show that, for both simulated and real data, the predictive performance of DMRcate is superior to those of Bumphunter and Probe Lasso, and commensurate with that of comb-p. For the real data, we validate all array-derived DMRs from the candidate methods on a suite of DMRs derived from whole-genome bisulfite sequencing called from the same DNA samples, using two separate phenotype comparisons. Conclusions The agglomeration of genomically localised individual methylation sites into discrete DMRs is currently best served by a combination of DM-signal smoothing and subsequent threshold specification. The findings also suggest the design of the 450K array shows preference for CpG sites that are more likely to be differentially methylated, but its overall coverage does not adequately reflect the depth and complexity of methylation signatures afforded by sequencing. For the convenience of the research community we have created a user-friendly R software package called DMRcate, downloadable from Bioconductor and compatible with existing preprocessing packages, which allows others to apply the same DMR-finding method on 450K array data. Electronic supplementary material The online version of this article (doi:10.1186/1756-8935-8-6) contains supplementary material, which is available to authorized users.
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            Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes

            Wanding Zhou, Peter Laird, Hui Shen (2016)
            Abstract Illumina Infinium DNA Methylation BeadChips represent the most widely used genome-scale DNA methylation assays. Existing strategies for masking Infinium probes overlapping repeats or single nucleotide polymorphisms (SNPs) are based largely on ad hoc assumptions and subjective criteria. In addition, the recently introduced MethylationEPIC (EPIC) array expands on the utility of this platform, but has not yet been well characterized. We present in this paper an extensive characterization of probes on the EPIC and HM450 microarrays, including mappability to the latest genome build, genomic copy number of the 3΄ nested subsequence and influence of polymorphisms including a previously unrecognized color channel switch for Type I probes. We show empirical evidence for exclusion criteria for underperforming probes, providing a sounder basis than current ad hoc criteria for exclusion. In addition, we describe novel probe uses, exemplified by the addition of a total of 1052 SNP probes to the existing 59 explicit SNP probes on the EPIC array and the use of these probes to predict ethnicity. Finally, we present an innovative out-of-band color channel application for the dual use of 62 371 probes as internal bisulfite conversion controls.
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              Using high-density DNA methylation arrays to profile copy number alterations

              Andrew Feber, Paul Guilhamon, Matthias Lechner … (2014)
              The integration of genomic and epigenomic data is an increasingly popular approach for studying the complex mechanisms driving cancer development. We have developed a method for evaluating both methylation and copy number from high-density DNA methylation arrays. Comparing copy number data from Infinium HumanMethylation450 BeadChips and SNP arrays, we demonstrate that Infinium arrays detect copy number alterations with the sensitivity of SNP platforms. These results show that high-density methylation arrays provide a robust and economic platform for detecting copy number and methylation changes in a single experiment. Our method is available in the ChAMP Bioconductor package: http://www.bioconductor.org/packages/2.13/bioc/html/ChAMP.html.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 December 2017
                Publication date (Electronic): 14 August 2017
                Publication date PMC-release: 14 August 2017
                Volume: 33
                Issue: 24
                Pages: 3982-3984
                Affiliations
                [1 ]CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, University of Chinese Academy of Science, Chinese Academy of Sciences, Shanghai 200031, China
                [2 ]Medical Genomics Group, Paul O’Gorman Building, UCL Cancer Institute, University College London, London WC1E 6BT, UK
                [3 ]Cambridge Epigenetix, Jonas Webb Building, Babraham Campus, Cambridge CB22 3AT, UK
                [4 ]Statistical Genomics, UCL Cancer Institute, University College London, London WC1E 6BT, UK
                [5 ]Department of Women’s Cancer, University College London, London WC1E 6AU, UK
                Author notes

                Associate Editor: Prof. Alfonso Valencia

                To whom correspondence should be addressed.
                Article
                Publisher ID: btx513
                DOI: 10.1093/bioinformatics/btx513
                PMC ID: 5860089
                PubMed ID: 28961746
                SO-VID: 94e9336a-d6dd-4ea7-8a93-9adff0f3e7d0
                Copyright © © The Author 2017. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 06 November 2016
                Date revision received : 14 July 2017
                Date accepted : 11 August 2017
                Page count
                Pages: 3
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Funded by: CSC 10.13039/501100000867
                Funded by: MRC 10.13039/501100000265
                Award ID: MR/M025411/1
                Funded by: UCLH 10.13039/501100008721
                Funded by: UCL 10.13039/501100000765
                Funded by: National Institute for Health Research 10.13039/501100000272
                Funded by: NIHR 10.13039/100006662
                Categories
                Subject: Applications Notes
                Subject: Genome Analysis

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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