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• W2149665870 abstract "Scientists are only beginning to develop methods for whole genome comparisons. Computational methods which do not incorporate evolutionary relationships in their models are inadequate when there is high correlation between the DNA sequence data from closely related species. New genomes from various species are often sequenced specifically because of their relationships to other species as researchers wish to learn how species are differentiated at the DNA sequence level. Current methods do not provide a computational platform for efficiently analyzing data from many genomes simultaneously. Genomic scale analysis tends to result in hundreds of thousands of files across gigabytes of disk space with much redundancy. As the library of complete genomes grows, researchers will analyze larger sets of more complex genomes. A software system which scales to facilitate these multiple genome analyses will be necessary. This thesis aims to provide a computational system for identifying cis-regulatory elements in closely related genomes. As a demonstration, an analysis of cis-regulatory elements in E. coli and related bacterial genomes is provided. Previous computational methods for such an analysis are somewhat ineffective because the species are closely related. This closeness causes statistical problems due to the high correlation between the sequence data in the genomes. Methods for discovering potential cis-regulatory elements from DNA sequence data are reviewed, and a new Gibbs Sampling method is provided to address the correlation problem by accounting for evolutionary distances between species. A new software system is also shown to provide a computational platform necessary for more complex, collaborative analyses in the future. The software framework provides an object-oriented representation for the Gibbs Phylogenetic Sampler and generically lays the groundwork to accommodate other statistical bioinformatics applications. Many sampling strategies, biological data structures, and mathematical models may be shared between various analysis applications. On synthetic data, the new Gibbs Sampling method demonstrates greater positive predictive value and significantly greater specificity than other, comparable methods. On real data, the new method also produces encouraging results. A comparison of features between this and other methods is also provided." @default.
• W2149665870 created "2016-06-24" @default.
• W2149665870 creator A5044471951 @default.
• W2149665870 creator A5057953740 @default.
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• W2149665870 date "2006-01-01" @default.
• W2149665870 modified "2023-09-27" @default.
• W2149665870 title "A computational system for identifyingcis-regulatory elements in closely related genomes" @default.
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