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Methods for identifying regulatory grammars

Author(s)
Syed, Tahin Fahmid
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Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
David K. Gifford.
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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
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Abstract
Recent advancements in sequencing technology have made it possible to study the mechanisms of gene regulation, such as protein-DNA binding, at greater resolution and on a greater scale than was previously possible. We present an expectation-maximization learning algorithm that identifies enriched spatial relationships between motifs in sets of DNA sequences. For example, the method will identify spatially constrained motifs colocated in the same regulatory region. We apply our method to biological sequence data and recover previously known prokaryotic promoter spacing constraints demonstrating that joint learning of motifs and spacing constraints is superior to other methods for this task.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (p. [37]-40).
 
Date issued
2013
URI
http://hdl.handle.net/1721.1/79240
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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