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Related Experiment Videos

SNPchip: R classes and methods for SNP array data.

Robert B Scharpf1, Jason C Ting, Jonathan Pevsner

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.

Bioinformatics (Oxford, England)
|January 6, 2007
PubMed
Summary
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High-density single nucleotide polymorphism (SNP) chips offer high-resolution genomic mapping for detecting chromosomal abnormalities linked to diseases. The R package SNPchip facilitates the analysis of this SNP data for new disease insights and potential therapeutic targets.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-density single nucleotide polymorphism (SNP) microarrays provide detailed genomic information, including copy number and genotype.
  • SNP chips offer higher resolution than traditional methods like fluorescence in situ hybridization and karyotyping for detecting chromosomal abnormalities.
  • These abnormalities, such as aneuploidies, microdeletions, microduplications, and loss of heterozygosity (LOH), are linked to various diseases.

Purpose of the Study:

  • To introduce the R package SNPchip for analyzing high-density SNP data.
  • To provide tools for storing, visualizing, and analyzing genomic data from SNP chips.
  • To leverage SNP chip data for discovering disease-associated genomic regions and potential intervention targets.

Main Methods:

Related Experiment Videos

  • The R package SNPchip utilizes S4 classes and integrates with Bioconductor open-source tools.
  • It extends the functionality of the original SNPscan web-tool.
  • The package enables the construction of statistical models for SNP-level data and interoperability with other R packages.

Main Results:

  • SNPchip provides a robust framework for the analysis of high-density SNP microarray data.
  • The package facilitates the identification of chromosomal abnormalities.
  • It aids in discovering novel genomic regions associated with various diseases.

Conclusions:

  • The SNPchip R package offers valuable tools for genomic research, particularly in identifying disease-linked chromosomal abnormalities.
  • Its integration with existing R and Bioconductor resources enhances its utility for statistical modeling and data analysis.
  • SNPchip promises to advance the discovery of disease-associated genomic regions and potential therapeutic targets.