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Bioinformatics Advance Access originally published online on February 23, 2009
Bioinformatics 2009 25(8):1056-1062; doi:10.1093/bioinformatics/btp104
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© 2009 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.

Array-based genotyping in S.cerevisiae using semi-supervised clustering

Richard Bourgon 1,*, Eugenio Mancera 2, Alessandro Brozzi 1, Lars M. Steinmetz 2 and Wolfgang Huber 1

1EMBL - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and 2European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany

*To whom correspondence should be addressed.


  Abstract

Motivation: Microarrays provide an accurate and cost-effective method for genotyping large numbers of individuals at high resolution. The resulting data permit the identification of loci at which genetic variation is associated with quantitative traits, or fine mapping of meiotic recombination, which is a key determinant of genetic diversity among individuals. Several issues inherent to short oligonucleotide arrays—cross-hybridization, or variability in probe response to target—have the potential to produce genotyping errors. There is a need for improved statistical methods for array-based genotyping.

Results: We developed ssGenotyping (ssG), a multivariate, semi-supervised approach for using microarrays to genotype haploid individuals at thousands of polymorphic sites. Using a meiotic recombination dataset, we show that ssG is more accurate than existing supervised classification methods, and that it produces denser marker coverage. The ssG algorithm is able to fit probe-specific affinity differences and to detect and filter spurious signal, permitting high-confidence genotyping at nucleotide resolution. We also demonstrate that oligonucleotide probe response depends significantly on genomic background, even when the probe's specific target sequence is unchanged. As a result, supervised classifiers trained on reference strains may not generalize well to diverged strains; ssG's semi-supervised approach, on the other hand, adapts automatically.

Availability: The ssGenotyping software is implemented in R. It is currently available for download (www.ebi.ac.uk/~bourgon/yeast_genotyping/ssG) and is being submitted to Bioconductor.

Contact: bourgon{at}ebi.ac.uk

Supplementary information: Supplementary data and a version including color figures are available at Bioinformatics online.

Associate Editor: Martin Bishop

Received on December 28, 2008; revised on February 1, 2009; accepted on February 17, 2009

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