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Bioinformatics Advance Access published online on January 4, 2007
Bioinformatics, doi:10.1093/bioinformatics/btl638
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received February 10, 2006
Revised November 15, 2006
Accepted December 14, 2006

Applications note

SNPchip : R classes and methods for SNP array data

Robert B. Scharpf 1, Jason C. Ting 2, Jonathan Pevsner 2, and Ingo Ruczinski 1 *

1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205
2 Department of Neurology, Kennedy Krieger Institute, Baltimore, Maryland 21205

* To whom correspondence should be addressed.
Ingo Ruczinski, E-mail: ingo{at}jhu.edu


  Abstract

Summary: High density single nucleotide polymorphism microarrays (SNP chips) provide information on a subject's genome, such as copy number and genotype (heterozygosity/homozygosity) at a SNP. While fluorescence in situ hybridization and karyotyping reveal many abnormalities, SNP chips provide a higher resolution map of the human genome that can be used to detect, for example, aneuploidies, microdeletions, microduplications, and loss of heterozygosity (LOH). As a variety of diseases are linked to such chromosomal abnormalities, SNP chips promise new insights for these diseases by aiding in the discovery of such regions, and may suggest targets for intervention. The R package SNPchip contains classes and methods useful for storing, visualizing, and analyzing high density SNP data. Originally developed from the SNPscan web-tool, SNPchip utilizes S4 classes and extends other open source R tools available at Bioconductor. This has numerous advantages, including the ability to build statistical models for SNP-level data that operate on instances of the class, and to communicate with other R packages that add additional functionality.

Availability: The package is available from the Bioconductor web page at www.bioconductor.org.

Supplementary information: The supplementary material as described in this article (case studies, installation guidelines, and R code) is available from http://biostat.jhsph.edu/~iruczins/publications/sm/.

Associate Editor: Chris Stoeckert
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