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  • Thesis

  • Authors: Vinar, Tomas (2006)

  • In this thesis, we present enhancements of hidden Markov models for the problem of finding genes in DNA sequences. Genes are the parts of DNA that serve as a template for synthesis of proteins. Thus. gene finding is a crucial step in the analysis of DNA sequencing data. Hidden Markov models are a key tool used in gene finding. Yhis thesis presents three methods for extending the capabilities of hidden Markov models to better capture the statistical properties of DNA sequences. In all three, we encounter limiting factors that lead to trade-offs between the model accuracy and those limiting factors. First. we build better models for recognizing biological signals in DNA sequences. Our new models capture non-adjacent dependencies within these signals. In this case. the main limiting ...

  • Article

  • Authors: Allen, Jonathan Edward (2006)

  • Obtaining the complete set of proteins for each eukaryotic organism is an important step in the quest to understand how life evolves and functions. The complex physiology of eukaryotic cells, however, makes direct observation of proteins and their parent genes difficult to achieve. An organism's genome provides the raw data that contains the set of instructions for generating the complete set of proteins, providing the potential to obtain a complete list of proteins without having to rely exclusively on direct observations in the cell. Computational gene prediction systems, therefore, play an important role in compiling sets of putative proteins for each sequenced genome. This dissertation addresses the problem of computational gene prediction in eukaryotic genomes, presenting a fr...