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Bioinformatics Advance Access originally published online on January 4, 2007
Bioinformatics 2007 23(5):627-628; doi:10.1093/bioinformatics/btl638
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

SNPchip: R classes and methods for SNP array data

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

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

*To whom correspondence should be addressed.


  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, e.g., 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

Contact: ingo{at}jhu.edu

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/

Received on February 10, 2006; revised on November 15, 2006; accepted on December 14, 2006

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