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Structure-based realignment of non-coding RNAs in multiple whole genome alignments

Author(s)
Yu, Michael Ku
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Alternative title
Alignment and prediction on non-coding RNAs
Structure-based realignment of non-coding ribonucleic acids in multiple whole genome alignments
Alignment and prediction on non-coding ribonucleic acids
Other Contributors
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Advisor
Bonnie Berger.
Terms of use
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
Whole genome alignments have become a central tool in biological sequence analysis. A major application is the de novo prediction of non-coding RNAs (ncRNAs) from structural conservation visible in the alignment. However, current methods for constructing genome alignments do so by explicitly optimizing for sequence similarity but not structural similarity. Therefore, de novo prediction of ncRNAs with high structural but low sequence conservation is intrinsically challenging in a genome alignment because the conservation signal is typically hidden. This study addresses this problem with a method for genome-wide realignment of potential ncRNAs according to structural similarity. Doing so reveals thousands of new high-confidence ncRNA predictions with particularly low sequence conservation from an alignment of 12 Drosophila genomes and hundreds from an alignment of 28 vertebrate genomes in the Encode project.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (p. 62-65).
 
Date issued
2011
URI
http://hdl.handle.net/1721.1/66817
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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