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Author Notes:

Correspondence: Rafael A Irizarry. Email: rafa@jhu.edu

SL and RAI conceived the study, developed the software, and drafted the manuscript.

AC conceived the study, provided the data, and revised the manuscript.

BC conceived the study, developed the software, and revised the manuscript.

DEA conceived the design and revised the manuscript. DJC revised the manuscript.

We would like to thank the Broad Institute and Affymetix for providing genotype data; Simon Cawley, Terry Speed, Earl Hubbell and Chuck Sugnet for helpful discussions; and finally, Ben Bolstad, James MacDonald, Seth Falcon, Marvin Newhouse, Robert Gentleman, Jiong Yang, and Fernando Pineda for help with hardware and software.

Note: Aravinda Chakravarti is a member of the Scientific Advisory Board of Affymetrix, Inc., a potential conflict of interest managed by the policies of Johns Hopkins University.

Subjects:

Research Funding:

The work of Rafael A Irizarry was partially funded by NIH grants 1R01GM083084-01, 1P41HG004059 and P50 HL73994 (Core E); Benilton Carvalho was funded by NIH grant 1R01RR021967-01A2 and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES/Brazil); Shin Lin, David J Cutler, Dan E Arking, and Aravinda Chakravarti were supported by NIH grants HG02757, MH60007, and the DW Reynolds Foundation.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Biotechnology & Applied Microbiology
  • Genetics & Heredity
  • GENOME-WIDE ASSOCIATION
  • OLIGONUCLEOTIDE ARRAYS
  • DNA
  • NORMALIZATION
  • POLYMORPHISM
  • EXPLORATION
  • ALGORITHMS
  • LOCI

Validation and extension of an empirical Bayes method for SNP calling on Affymetrix microarrays

Journal Title:

Genome Biology

Volume:

Volume 9, Number 4

Publisher:

, Pages R63-R63

Type of Work:

Article | Final Publisher PDF

Abstract:

Multiple algorithms have been developed for the purpose of calling single nucleotide polymorphisms (SNPs) from Affymetrix microarrays. We extend and validate the algorithm CRLMM, which incorporates HapMap information within an empirical Bayes framework. We find CRLMM to be more accurate than the Affymetrix default programs (BRLMM and Birdseed). Also, we tie our call confidence metric to percent accuracy. We intend that our validation datasets and methods, refered to as SNPaffycomp, serve as standard benchmarks for future SNP calling algorithms.

Copyright information:

© 2008 Lin et al.; licensee BioMed Central Ltd.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 2.0 Generic License (http://creativecommons.org/licenses/by/2.0/).

Creative Commons License

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