Methods for identifying regulatory grammars
Name
845324064-MIT.pdf
Description
Full printable version
Size
2.36 MB
Format
Adobe PDF
Checksum (MD5)
120a59971d2c46cb99b2fdcda59b5704
Author(s)
Syed, Tahin Fahmid
Advisor(s)
David K. Gifford.
Date Issued
2013
Publisher
Massachusetts Institute of Technology
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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reproduction or distribution in any format is prohibited without written
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