Biothreat Detection by Random Oligomer-Based Microarray

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dc.contributor.authorDiggans, James C.
dc.creatorDiggans, James C.
dc.date2008-12-03
dc.date.accessioned2009-08-24T18:40:34Z
dc.date.availableNO_RESTRICTION
dc.date.available2009-08-24T18:40:34Z
dc.date.issued2009-08-24T18:40:34Z
dc.description.abstractCurrent biosensors are primarily based upon previous observations: they detect organisms known to be pathogenic. Future biowarfare agents, however, are likely to contain completely novel or re-engineered proteins and nucleic acid sequences intended either to make previously harmless organisms pathogenic, to increase the pathenogenicity of existing agents or expressly to render the agent undetectable by conventional serotype-or PCR-based methods. The present work describes the creation and validation of a nucleic-acid microarray-based biosensor for the detection of putative biohazards present in environmental air samples. The prototype array consists of 15,200 pseudo-random 25bp oligonucleotide probes whose sequences were generated using variable-length Markov chain models trained on sequence from pathogenic prokaryotic genomes. Classifiers constructed on organism-specific patterns of hybridization were then applied to unknown or mixed samples to determine a likelihood of detection. With this approach, the ability to estimate the presence of a novel or engineered threat then requires only the characterization of the binding pattern of the agent’s amplified genomic DNA to the array.
dc.description.noteSupplemental material is available in Special Collections and Archives upon request. This material includes VLMC Training, Reference Arrays, WGA Replicate Study, and Classification.
dc.identifier.urihttps://hdl.handle.net/1920/5579
dc.language.isoen_US
dc.subjectBioinformatics
dc.subjectBiosensor
dc.subjectClassification
dc.subjectMicroarray
dc.subjectBiodefense
dc.titleBiothreat Detection by Random Oligomer-Based Microarray
dc.typeDissertation
thesis.degree.disciplineBioinformatics and Computational Biology
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy in Bioinformatics and Computational Biology

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