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Bioinformatics Advance Access first published online on July 9, 2004
This version published online on September 3, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth346
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received March 24, 2004
Revised May 6, 2004
Accepted May 11, 2004

Article

Quantitative DNA methylation analysis based on four-dye trace data from direct sequencing of PCR amplificates

Jörn Lewin 1*, Armin O. Schmitt 2, Péter Adorján 1, Thomas Hildmann 1, and Christian Piepenbrock 1

1 Epigenomics AG, Kleine Präsidentenstr. 1, 10178 Berlin, Germany
2 Institut für Nutztierwissenschaften, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany

* To whom correspondence should be addressed. E-mail: joern.lewin{at}epigenomics.com.


  Abstract

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 bisulphite treated DNA with high throughput sequencing machines. This technology is a prerequisite for success of the http://www.epigenome.org/Human, http://www.epigenome.org/Epigenome, http://www.epigenome.org/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 bisulphite 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 bisulphite 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 bisulphite 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.


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