2026-09-18T20:55:58Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/367892022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
David Gifford.
Kumar, Rasika S
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2007-03-12T17:53:53Z
2007-03-12T17:53:53Z
2005
2005
http://hdl.handle.net/1721.1/36789
79624636
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (leaves 75-78).
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.
by Rasika S. Kumar.
M.Eng.
78 leaves
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Multi-syllabic DNA motif discovery
Multi-syllabic deoxyribonucleic acid motif discovery
Thesis
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<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>
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