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Open Access Methodology article

New methods to analyse microarray data that partially lack a reference signal

Neeltje Carpaij1*, Ad C Fluit1, Jodi A Lindsay2, Marc JM Bonten1 and Rob JL Willems1

Author Affiliations

1 Department of Medical Microbiology, University Medical Centre Utrecht, room G04.614 PO BOX 85500, 3508 GA Utrecht, The Netherlands

2 Department of Cellular and Molecular Medicine, St. George's, University of London, London, UK

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BMC Genomics 2009, 10:522  doi:10.1186/1471-2164-10-522

Published: 13 November 2009

Abstract

Background

Microarray-based Comparative Genomic Hybridisation (CGH) has been used to assess genetic variability between bacterial strains. Crucial for interpretation of microarray data is the availability of a reference to compare signal intensities to reliably determine presence or divergence each DNA fragment. However, the production of a good reference becomes unfeasible when microarrays are based on pan-genomes.

When only a single strain is used as a reference for a multistrain array, the accessory gene pool will be partially represented by reference DNA, although these genes represent the genomic repertoire that can explain differences in virulence, pathogenicity or transmissibility between strains. The lack of a reference makes interpretation of the data for these genes difficult and, if the test signal is low, they are often deleted from the analysis. We aimed to develop novel methods to determine the presence or divergence of genes in a Staphylococcus aureus multistrain PCR product microarray-based CGH approach for which reference DNA was not available for some probes.

Results

In this study we have developed 6 new methods to predict divergence and presence of all genes spotted on a multistrain Staphylococcus aureus DNA microarray, published previously, including those gene spots that lack reference signals. When considering specificity and PPV (i.e. the false-positive rate) as the most important criteria for evaluating these methods, the method that defined gene presence based on a signal at least twice as high as the background and higher than the reference signal (method 4) had the best test characteristics. For this method specificity was 100% and 82% for MRSA252 (compared to the GACK method) and all spots (compared to sequence data), respectively, and PPV were 100% and 76% for MRSA252 (compared to the GACK method) and all spots (compared to sequence data), respectively.

Conclusion

A definition of gene presence based on signal at least twice as high as the background and higher than the reference signal (method 4) had the best test characteristics, allowing the analysis of 6-17% more of the genes not present in the reference strain. This method is recommended to analyse microarray data that partially lack a reference signal.