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Nucleic Acids Research Advance Access published online on March 11, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn110
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Methods online

wuHMM: a robust algorithm to detect DNA copy number variation using long oligonucleotide microarray data

Patrick Cahan1, Laura E. Godfrey1, Peggy S. Eis2, Todd A. Richmond2, Rebecca R. Selzer2, Michael Brent1, Howard L. McLeod3, Timothy J. Ley1 and Timothy A. Graubert1,*

1Department of Internal Medicine and Department of Genetics, Division of Oncology, Stem Cell Biology Section, Washington University, St Louis, MO, 2Roche NimbleGen, Inc., Madison, WI and 3Institute for Pharmacogenomics and Individualized Therapy, University of North Carolina, Chapel Hill, NC, USA

*To whom correspondence should be addressed. Tel: 314 747 4437; Fax: 314 362 9333; Email: graubert{at}medicine.wustl.edu

Received September 18, 2007. Revised February 26, 2008. Accepted February 27, 2008.

Copy number variants (CNVs) are currently defined as genomic sequences that are polymorphic in copy number and range in length from 1000 to several million base pairs. Among current array-based CNV detection platforms, long-oligonucleotide arrays promise the highest resolution. However, the performance of currently available analytical tools suffers when applied to these data because of the lower signal:noise ratio inherent in oligonucleotide-based hybridization assays. We have developed wuHMM, an algorithm for mapping CNVs from array comparative genomic hybridization (aCGH) platforms comprised of 385 000 to more than 3 million probes. wuHMM is unique in that it can utilize sequence divergence information to reduce the false positive rate (FPR). We apply wuHMM to 385K-aCGH, 2.1M-aCGH and 3.1M-aCGH experiments comparing the 129X1/SvJ and C57BL/6J inbred mouse genomes. We assess wuHMM's performance on the 385K platform by comparison to the higher resolution platforms and we independently validate 10 CNVs. The method requires no training data and is robust with respect to changes in algorithm parameters. At a FPR of <10%, the algorithm can detect CNVs with five probes on the 385K platform and three on the 2.1M and 3.1M platforms, resulting in effective resolutions of 24 kb, 2–5 kb and 1 kb, respectively.


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