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dc.contributor.advisorXu, Dong, 1965-eng
dc.contributor.authorSrivastava, Gyan Prakash, 1979-eng
dc.date.issued2009eng
dc.date.submitted2009 Falleng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on April 5, 2010).eng
dc.descriptionThe entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.eng
dc.descriptionDissertation advisor: Dr. Dong Xu.eng
dc.descriptionVita.eng
dc.descriptionPh. D. University of Missouri--Columbia 2009.eng
dc.description.abstractIn this present era of high-throughput technologies, meta-analysis is being widely used to integrate multiple similar high throughput studies. Here we propose a novel framework for applying meta-analysis techniques on expression data for gene function characterizations and biological networks construction like the gene regulatory network. In particular, we developed a prototype for gene function annotation using multiple microarray datasets and tested the performance of our model using yeast and human microarray datasets. Our results show significant improvement in functional annotation in general. We further applied the same metaanalysis method on the Arabidopsis plant in a collaborative project with Monsanto Company to construct regulatory network for Arabidopsis. Our method shows significant improvement than any other existing methods for inferring gene regulatory network. Beside meta-analysis, I have invested a great deal of efforts in developing PRIMEGENS, an open source software, which could be used for large-scale primers and probe design for PCR, DNA synthesis, qRT-PCR (gene expression), and targeted next-generation sequencing (454, Solexa, Agilent sure-select technology etc.) for normal or bisulfite-treated genome. We recently extended its functionality including microarray probe design to cover genome-wide CpG islands in human, Taqman probes and discriminating transcripts from its multiple homologs or splice variants based on gene-specific unique fragment in soybean genome.eng
dc.description.bibrefIncludes bibliographical referenceseng
dc.format.extentviii, 123 pageseng
dc.identifier.oclc605910441eng
dc.identifier.urihttps://hdl.handle.net/10355/6841
dc.identifier.urihttps://doi.org/10.32469/10355/6841eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subject.lcshGenomics -- Data processingeng
dc.subject.lcshGene expression -- Data processingeng
dc.subject.lcshDNA microarrays -- Data processingeng
dc.subject.lcshArabidopsis -- Genetics -- Data processingeng
dc.titleGenome scale meta analysis of microarrays for biological inferenceseng
dc.typeThesiseng
thesis.degree.disciplineComputer science (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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