Type of Document Dissertation Author Shallom, Shamira J URN etd-05162012-174753 Title A Species Independent Universal Bio-detection Microarray for Pathogen Forensics Degree PhD Department Genetics, Bioinformatics, and Computational Biology Advisory Committee
Advisor Name Title Garner, Harold Ray Committee Chair Bevan, David R. Committee Member Laubenbacher, Reinhard C. Committee Member Lawrence, Christopher B. Committee Member Keywords
Date of Defense 2012-05-04 Availability restricted AbstractThe detection and identification of bio-threat agents and study of host-pathogen interactions require a high-resolution detection platform capable of discerning closely related species. This dissertation addresses the completion of the development of an array based platform and provides a robust pipeline for the discovery of unique bio-signatures for pathogens and their host. Our collection (library) of host and pathogen signatures has been greatly expanded to improve robustness and identification accuracy of an 'unknown' sample. The library containing measured bio-signatures for each species/isolate is complemented with computational methodologies to resolve the identity of the unknown sample as well as a mixture of organisms or a pathogen in a host background.
Current approaches for pathogen detection rely on prior genomic sequence information. This research targets use of a broad based platform for identification of pathogens from field or laboratory samples on a high density Universal Bio-signature Detection Array (UBDA). This array is genome independent and contains all possible (49 combinations) 9-mer probes which are mathematically computed and genome independent. It works by comparing signal intensity readout to a library of readouts established by interrogating a wide spectrum of organisms. Each genome has a unique pattern of signal intensities corresponding to each of these probes. These signal intensities were used to generate un-biased cluster analysis patterns that can easily distinguish organisms into accepted and known phylogenomic relationships.
Classification methods such as hierarchical clustering, Pearson’s correlation matrix, principal component analysis and curve fitting regression methods were tested for pathogen specific use cases. Hierarchical clustering and Pearson’s correlation matrix methods can establish phylogenomic relationships between highly diverse genomes. However, in order to assign a given sample to one or more groups, such as a pure isolate of a single species or composite mixture of multiple species, principal component analysis (PCA) was used. The test cases included identification of mixed samples, case study of field samples from state diagnostic labs and finally a surveillance method for viral and parasite carrying insect host vectors. Completion of these application challenges is meant to demonstrate the power and confirm confidence in the Universal Bio-signature Detection Array.
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