Optimization and clinical validation of a pathogen detection microarray

doi:10.1186/gb-2007-8-5-r93

Genome Biology

Volume 8
Issue 5

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Wong CW
Heng CL
Wan Yee L
Soh SW
Kartasasmita CB
Simoes EA
Hibberd ML
Sung WK
Miller LD

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Heng CL
Wan Yee L
Soh SW
Kartasasmita CB
Simoes EA
Hibberd ML
Sung WK
Miller LD
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Optimization and clinical validation of a pathogen detection microarray

Christopher W Wong¹ , Charlie Lee Wah Heng² , Leong Wan Yee¹ , Shirlena WL Soh³ , Cissy B Kartasasmita⁴ , Eric AF Simoes⁵ , Martin L Hibberd³ , Wing-Kin Sung² and Lance D Miller¹

¹Genomic Technologies, Genome Institute of Singapore, Republic of Singapore

²Computational and Mathematical Biology, Genome Institute of Singapore, Republic of Singapore

³Infectious Diseases, Genome Institute of Singapore, Republic of Singapore

⁴Hasan Sadikin Hospital, Department of Pediatrics, Faculty of Medicine Universitas Padjadjaran, Indonesia

⁵Section of Infectious Diseases, The University of Colorado at Denver and Health Sciences Center and The Children's Hospital, Denver, CO 80262, USA

author email corresponding author email

Genome Biology 2007, 8:R93doi:10.1186/gb-2007-8-5-r93


Published:	28 May 2007

Subject areas: Bioinformatics, Genetics, Microbiology and parasitology, Virology

Abstract

DNA microarrays used as 'genomic sensors' have great potential in clinical diagnostics. Biases inherent in random PCR-amplification, cross-hybridization effects, and inadequate microarray analysis, however, limit detection sensitivity and specificity. Here, we have studied the relationships between viral amplification efficiency, hybridization signal, and target-probe annealing specificity using a customized microarray platform. Novel features of this platform include the development of a robust algorithm that accurately predicts PCR bias during DNA amplification and can be used to improve PCR primer design, as well as a powerful statistical concept for inferring pathogen identity from probe recognition signatures. Compared to real-time PCR, the microarray platform identified pathogens with 94% accuracy (76% sensitivity and 100% specificity) in a panel of 36 patient specimens. Our findings show that microarrays can be used for the robust and accurate diagnosis of pathogens, and further substantiate the use of microarray technology in clinical diagnostics.