Bioinformatics Advance Access originally published online on July 9, 2004
Bioinformatics 2004 20(17):3005-3012; doi:10.1093/bioinformatics/bth346
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Bioinformatics vol. 20 issue 17 © Oxford University Press 2004; all rights reserved.
Quantitative DNA methylation analysis based on four-dye trace data from direct sequencing of PCR amplificates
1 Epigenomics AG, Kleine Präsidentenstrasse 1, 10178 Berlin, Germany and 2 Institut für Nutztierwissenschaften, Humboldt-Universität zu Berlin, Invalidenstrasse 42, 10115 Berlin, Germany
Received on March 24, 2004; revised on May 6, 2004; accepted on May 11, 2004
Advance Access Publication July 9, 2004
Motivation: Methylation of cytosines in DNA plays an important role in the regulation of gene expression, and the analysis of methylation patterns is fundamental for the understanding of cell differentiation, aging processes, diseases and cancer development. Such analysis has been limited, because technologies for detailed and efficient high-throughput studies have not been available. We have developed a novel quantitative methylation analysis algorithm and workflow based on direct DNA sequencing of PCR products from bisulfite-treated DNA with high-throughput sequencing machines. This technology is a prerequisite for success of the Human Epigenome Project, the first large genome-wide sequencing study for DNA methylation in many different tissues. Methylation in tissue samples which are compositions of different cells is a quantitative information represented by cytosine/thymine proportions after bisulfite conversion of unmethylated cytosines to uracil and PCR. Calculation of quantitative methylation information from base proportions represented by different dye signals in four-dye sequencing trace files needs a specific algorithm handling imbalanced and overscaled signals, incomplete conversion, quality problems and basecaller artifacts.
Results: The algorithm we developed has several key properties: it analyzes trace files from PCR products of bisulfite-treated DNA sequenced directly on ABI machines; it yields quantitative methylation measurements for individual cytosine positions after alignment with genomic reference sequences, signal normalization and estimation of effectiveness of bisulfite treatment; it works in a fully automated pipeline including data quality monitoring; it is efficient and avoids the usual cost of multiple sequencing runs on subclones to estimate DNA methylation. The power of our new algorithm is demonstrated with data from two test systems based on mixtures with known base compositions and defined methylation. In addition, the applicability is proven by identifying CpGs that are differentially methylated in real tissue samples.
Contact: joern.lewin{at}epigenomics.com
* To whom correspondence should be addressed.
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