{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/36789"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/36789","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Multi-syllabic DNA motif discovery","abstract":"This paper describes a method for finding multi-syllabic motifs in a genome. It expands on the algorithm developed by Takusagawa, et al[1, 2] that uses data from Chromatin Immuno-Precipitation (ChIP) experiments to isolate regions that have a given motif. The Takusagawa method uses an enumeration method to search for motifs in both positive and negative intergenic regions in order to determine the statistical significance of the results. Our algorithm also uses enumeration to find motifs that have gaps between the different sub-motifs, or syllables. This thesis also describes a method to calculate the significance of each motif and tests this method via Monte Carlo simulations on random test sets. The significant motifs found using this algorithm are verified against consensus motifs found in the literature.","abstract_html":"This paper describes a method for finding multi-syllabic motifs in a genome. It expands on the algorithm developed by Takusagawa, et al[1, 2] that uses data from Chromatin Immuno-Precipitation (ChIP) experiments to isolate regions that have a given motif. The Takusagawa method uses an enumeration method to search for motifs in both positive and negative intergenic regions in order to determine the statistical significance of the results. Our algorithm also uses enumeration to find motifs that have gaps between the different sub-motifs, or syllables. This thesis also describes a method to calculate the significance of each motif and tests this method via Monte Carlo simulations on random test sets. 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