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This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM): Genomics

Open Access Research

Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes

Michael P Trimarchi1, Mark Murphy1, David Frankhouser1, Benjamin AT Rodriguez1, John Curfman1, Guido Marcucci1, Pearlly Yan1* and Ralf Bundschuh2*

Author affiliations

1 Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA

2 Departments of Physics and Biochemistry, The Ohio State University, Columbus, Ohio, USA

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Citation and License

BMC Genomics 2012, 13(Suppl 8):S6  doi:10.1186/1471-2164-13-S8-S6

Published: 17 December 2012

Abstract

Background

DNA methylation is an important epigenetic mark and dysregulation of DNA methylation is associated with many diseases including cancer. Advances in next-generation sequencing now allow unbiased methylome profiling of entire patient cohorts, greatly facilitating biomarker discovery and presenting new opportunities to understand the biological mechanisms by which changes in methylation contribute to disease. Enrichment-based sequencing assays such as MethylCap-seq are a cost effective solution for genome-wide determination of methylation status, but the technical reliability of methylation reconstruction from raw sequencing data has not been well characterized.

Methods

We analyze three MethylCap-seq data sets and perform two different analyses to assess data quality. First, we investigate how data quality is affected by excluding samples that do not meet quality control cutoff requirements. Second, we consider the effect of additional reads on enrichment score, saturation, and coverage. Lastly, we verify a method for the determination of the global amount of methylation from MethylCap-seq data by comparing to a spiked-in control DNA of known methylation status.

Results

We show that rejection of samples based on our quality control parameters leads to a significant improvement of methylation calling. Additional reads beyond ~13 million unique aligned reads improved coverage, modestly improved saturation, and did not impact enrichment score. Lastly, we find that a global methylation indicator calculated from MethylCap-seq data correlates well with the global methylation level of a sample as obtained from a spike-in DNA of known methylation level.

Conclusions

We show that with appropriate quality control MethylCap-seq is a reliable tool, suitable for cohorts of hundreds of patients, that provides reproducible methylation information on a feature by feature basis as well as information about the global level of methylation.