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      Comparison of multiplexed reduced representation bisulfite sequencing (mRRBS) with the 450K Illumina Human BeadChip: from concordance to practical applications for methylomic profiling in epigenetic epidemiologic studies

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          Abstract

          Background Reduced representation bisulfite sequencing (RRBS) is an efficient approach for large-scale base-pair resolution DNA methylation analysis. RRBS utilizes Mspl digestion to enrich for CpG dinucleotides prior to sequencing. RRBS works with considerably lower amounts of DNA compared to the human 450K BeadChip Illumina microarray. Previous studies have compared these two methodologies with good concordance on a relatively small set of overlapping sites. Boyle and colleagues recently proposed a variant of RRBS which is gel-free and high-throughput, allowing for the simultaneous processing of multiple samples [1]. Given the potential of mRRBS to characterize the methylome in larger epidemiology studies, there is a need to compare the performance of the multiplexed RRBS (mRRBS) with the 450K BeadChip, especially in terms of reproducibility of the results and genomic coverage. Materials and methods We have compared mRRBS with the 450K BeadChip using buffy coat genomic DNA extracted from 24 samples from 12 males in an existing cohort. Additionally, 6 of the 24 samples were replicated in the mRRBS study as analytic duplicates. A further 12 samples were sequenced again at a higher cluster density. Sequencing with 75bp single-end reads used both 6 or 12 sample pools on the Illumina HiSeq 2000. Post-processing included read trimming with Trim Galore, alignment using Bismark, and merging of informative reads from both strands for CpGs. Data from the 450k beadchip were normalized using a recent comprehensive pipeline [2]. Results Among 42 samples sequenced, 28 had more than 5M reads and after alignment of >71% of trimmed reads, these samples had a median of 1.3M CpGs at ≥10x depth (300K to 2.5M CpGs). Samples <5 million reads were related to particular Illumina sequencing adapters and position on the library preparation plate. Sequencing at a higher cluster density yielded ~300K extra CpGs at >10x depth. To represent a population, we took the best passing sample (>5M reads, n=11) from each individual. There were 160K shared sites among all 11 samples at ≥10x depth with 1M found in 8 of 11. Between 24K and 124K sites per sample overlapped with Illumina 450K sites. Pearson correlation coefficients between RRBS %methylation (≥10x depth) and quantile normalized 450K beta values for these 11 samples ranged from 0.92 to 0.95. Conclusions Given the observed differences in reads by library position and adapter ligation efficiency, a more even distribution of reads per sample may be achieved by screening adapters and concentration matching prior to pooling samples. These results support the use of mRRBS for methylomics in epigenetic epidemiologic studies, and further investigation of sample quality and measurement variability is ongoing.

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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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            Gel-free multiplexed reduced representation bisulfite sequencing for large-scale DNA methylation profiling

            Sequencing-based approaches have led to new insights about DNA methylation. While many different techniques for genome-scale mapping of DNA methylation have been employed, throughput has been a key limitation for most. To further facilitate the mapping of DNA methylation, we describe a protocol for gel-free multiplexed reduced representation bisulfite sequencing (mRRBS) that reduces the workload dramatically and enables processing of 96 or more samples per week. mRRBS achieves similar CpG coverage to the original RRBS protocol, while the higher throughput and lower cost make it better suited for large-scale DNA methylation mapping studies, including cohorts of cancer samples.
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              Author and article information

              Conference
              Epigenetics Chromatin
              Epigenetics Chromatin
              Epigenetics & Chromatin
              BioMed Central
              1756-8935
              2013
              18 March 2013
              : 6
              : Suppl 1
              : P36
              Affiliations
              [1 ]Laboratory of Environmental Epigenetics, Department of Environmental Health, Harvard School of Public Health, Boston, MA, USA
              [2 ]Exposure, Epidemiology, & Risk Program, Department of Environmental Health, Harvard School of Public Health, 401 Park Drive, Landmark Center West, Boston, MA, USA
              [3 ]Harvard School of Public Health, Department of Epidemiology, Boston, MA, USA
              [4 ]Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics and Gynecology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
              [5 ]Division of Cancer Epidemiology, Comprehensive Cancer Center Freiburg, Freiburg University, Freiburg, Germany
              [6 ]Center for Health Bioinformatics, Harvard School of Public Health, Department of Biostatistics & Program in Quantitative Genomics, Boston, MA, USA
              [7 ]Dana-Farber/Harvard Cancer Center, Boston, MA, USA
              [8 ]Harvard/Massachusetts General Hospital Center on Genomics, Vulnerable Populations, and Health Disparities, Boston, MA, USA
              Article
              1756-8935-6-S1-P36
              10.1186/1756-8935-6-S1-P36
              3600781
              8f918ad8-8206-431f-a457-61aabcfd8f33
              Copyright ©2013 Carmona et al; licensee BioMed Central Ltd.

              This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

              Epigenetics and Chromatin: Interactions and processes
              Boston, MA, USA
              11-13 March 2013
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              Genetics
              Genetics

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