This article is part of the supplement: Selected Proceedings of the First Summit on Translational Bioinformatics 2008

Proceedings

Robust methods for accurate diagnosis using pan-microbiological oligonucleotide microarrays

Yang Liu^1†, Lee Sam¹, Jianrong Li¹ and Yves A Lussier^1,2,3,4*

• * Corresponding author: Yves A Lussier lussier@uchicago.edu

• † Equal contributors

Author Affiliations

¹ Center for Biomedical Informatics and Section of Genetic Medicine, Dept. of Medicine, The University of Chicago, Chicago, 5841 South Maryland Ave, Ill, USA

² UC Cancer Research Center, The University of Chicago, Chicago, 5841 South Maryland Ave, Ill, USA

³ Institute for Genomics and Systems Biology, The University of Chicago, Chicago, 5841 South Maryland Ave, Ill, USA

⁴ Computation Institute, The University of Chicago, Chicago, 5841 South Maryland Ave, Ill, USA

For all author emails, please log on.

BMC Bioinformatics 2009, 10(Suppl 2):S11 doi:10.1186/1471-2105-10-S2-S11

Published: 5 February 2009

Abstract

Background

To address the limitations of traditional virus and pathogen detection methodologies in clinical diagnosis, scientists have developed high-throughput oligonucleotide microarrays to rapidly identify infectious agents. However, objectively identifying pathogens from the complex hybridization patterns of these massively multiplexed arrays remains challenging.

Methods

In this study, we conceived an automated method based on the hypergeometric distribution for identifying pathogens in multiplexed arrays and compared it to five other methods. We evaluated these metrics: 1) accurate prediction, whether the top ranked prediction(s) match the real virus(es); 2) four accuracy scores.

Results

Though accurate prediction and high specificity and sensitivity can be achieved with several methods, the method based on hypergeometric distribution provides a significant advantage in term of positive predicting value with two to sixty folds the positive predicting values of other methods.

Conclusion

The proposed multi-specie array analysis based on the hypergeometric distribution addresses shortcomings of previous methods by enhancing signals of positively hybridized probes.