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Bioinformatics Advance Access originally published online on March 18, 2008
Bioinformatics 2008 24(9):1161-1167; doi:10.1093/bioinformatics/btn096
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

An analytical pipeline for genomic representations used for cytosine methylation studies

Reid F. Thompson 1, Mark Reimers 2, Batbayar Khulan 1, Mathieu Gissot 3,4, Todd A. Richmond 5, Quan Chen 6,7, Xin Zheng 6,7, Kami Kim 3,4 and John M. Greally 1,8,*

1Department of Molecular Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, 2Department of Biostatistics, Virginia Commonwealth University, 730 East Broad Street, Richmond, VA 23298, 3Department of Medicine (Infectious Diseases), 4Department of Microbiology & Immunology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, 5Roche NimbleGen, 1 Science Court, Madison, WI 53711, 6Department of Pathology, 7Bioinformatics Shared Resource and 8Department of Medicine (Hematology), Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Representations of the genome can be generated by the selection of a subpopulation of restriction fragments using ligation-mediated PCR. Such representations form the basis for a number of high-throughput assays, including the HELP assay to study cytosine methylation. We find that HELP data analysis is complicated not only by PCR amplification heterogeneity but also by a complex and variable distribution of cytosine methylation. To address this, we created an analytical pipeline and novel normalization approach that improves concordance between microarray-derived data and single locus validation results, demonstrating the value of the analytical approach. A major influence on the PCR amplification is the size of the restriction fragment, requiring a quantile normalization approach that reduces the influence of fragment length on signal intensity. Here we describe all of the components of the pipeline, which can also be applied to data derived from other assays based on genomic representations.

Contact: jgreally{at}aecom.yu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Joaquin Dopazo

Received on November 7, 2007; revised on January 24, 2008; accepted on March 7, 2008

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