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Title page for ETD etd-07092014-143347

Type of Document Master's Thesis
Author Harrell, Morgan Rachel
Author's Email Address morgan.harrell@vanderbilt.edu
URN etd-07092014-143347
Title A Computational Analysis on Gene Fusions in Human Cancer
Degree Master of Science
Department Biomedical Informatics
Advisory Committee
Advisor Name Title
Zhongming Zhao Committee Chair
Bing Zhang Committee Member
William Bush Committee Member
  • gene fusion
  • bioinformatics
  • cancer
Date of Defense 2014-06-26
Availability unrestricted
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.
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