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A Computational Analysis on Gene Fusions in Human Cancer

Gene fusions are instances where two discrete genes incorrectly join together. They are common mutations in cancer, and, since the advent of next generation sequencing technology, many gene fusions in cancer tissues have been discovered and cataloged. We utilized the rapidly growing pool of information on gene fusions in human cancer to form projections on gene fusion mutations. We test two hypotheses: 1) identifiable motifs and entropy patterns exist at breakpoints that form fusions, and 2) gene fusions are more connected than randomly generated mutations in the biological networks. This thesis project has three related computational analyses: 1) motif discovery to examine common sequence patterns at and around breakpoints that form fusions, 2) entropy sliding-window analysis to determine structural characteristics at and around breakpoints that form fusions, and 3) gene-fusion network analysis to visualize and compare cancer-associated gene fusion metrics versus controls. We found no over-represented motifs at breakpoints that form gene fusions. We characterized a common entropy change at breakpoints. This feature may help us to predict gene fusions as part of prediction algorithms. Finally, we found that network metrics may be useful toward understanding the role gene fusions have in cancers.

Identiferoai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-07092014-143347
Date22 July 2014
CreatorsHarrell, Morgan Rachel
ContributorsZhongming Zhao, William Bush, Bing Zhang
PublisherVANDERBILT
Source SetsVanderbilt University Theses
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.library.vanderbilt.edu/available/etd-07092014-143347/
Rightsunrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.

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