VIPR: A probabilistic algorithm for analysis of microbial detection microarrays

Viral pathogen discovery

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Pathogen discovery is critically important to infectious diseases and public health.

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Nearly all new outbreaks are caused by the emergence of novel viruses.

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Genomic tools for pathogen discovery include consensus PCR, microarrays, and deep sequencing.

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Downstream studies are often necessary to link a candidate novel virus to a disease.

Viral pathogen discovery is of critical importance to clinical microbiology, infectious diseases, and public health. Genomic approaches for pathogen discovery, including consensus polymerase chain reaction (PCR), microarrays, and unbiased next-generation sequencing (NGS), have the capacity to comprehensively identify novel microbes present in clinical samples. Although numerous challenges remain to be addressed, including the bioinformatics analysis and interpretation of large datasets, these technologies have been successful in rapidly identifying emerging outbreak threats, screening vaccines and other biological products for microbial contamination, and discovering novel viruses associated with both acute and chronic illnesses. Downstream studies such as genome assembly, epidemiologic screening, and a culture system or animal model of infection are necessary to establish an association of a candidate pathogen with disease.

VIPR HMM: a hidden Markov model for detecting recombination with microbial detection microarrays

MOTIVATION

Current methods in diagnostic microbiology typically focus on the detection of a single genomic locus or protein in a candidate agent. The presence of the entire microbe is then inferred from this isolated result. Problematically, the presence of recombination in microbial genomes would go undetected unless other genomic loci or protein components were specifically assayed. Microarrays lend themselves well to the detection of multiple loci from a given microbe; furthermore, the inherent nature of microarrays facilitates highly parallel interrogation of multiple microbes. However, none of the existing methods for analyzing diagnostic microarray data has the capacity to specifically identify recombinant microbes. In previous work, we developed a novel algorithm, VIPR, for analyzing diagnostic microarray data.

RESULTS

We have expanded upon our previous implementation of VIPR by incorporating a hidden Markov model (HMM) to detect recombinant genomes. We trained our HMM on a set of non-recombinant parental viruses and applied our method to 11 recombinant alphaviruses and 4 recombinant flaviviruses hybridized to a diagnostic microarray in order to evaluate performance of the HMM. VIPR HMM correctly identified 95% of the 62 inter-species recombination breakpoints in the validation set and only two false-positive breakpoints were predicted. This study represents the first description and validation of an algorithm capable of detecting recombinant viruses based on diagnostic microarray hybridization patterns.

AVAILABILITY

VIPR HMM is freely available for academic use and can be downloaded from http://ibridgenetwork.org/wustl/vipr.

CONTACT

davewang@borcim.wustl.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Microarrays for pathogen detection and analysis

DNA microarrays have emerged as a viable platform for detection of pathogenic organisms in clinical and environmental samples. These microbial detection arrays occupy a middle ground between low cost, narrowly focused assays such as multiplex PCR and more expensive, broad-spectrum technologies like high-throughput sequencing. While pathogen detection arrays have been used primarily in a research context, several groups are aggressively working to develop arrays for clinical diagnostics, food safety testing, environmental monitoring and biodefense. Statistical algorithms that can analyze data from microbial detection arrays and provide easily interpretable results are absolutely required in order for these efforts to succeed. In this article, we will review the most promising array designs and analysis algorithms that have been developed to date, comparing their strengths and weaknesses for pathogen detection and discovery.