Integrating Omics data : a new software tool and its use in implicating therapeutic targets in Huntington's disease
Name
1057726996-MIT.pdf
Description
Full printable version
Size
23.56 MB
Format
Adobe PDF
Checksum (MD5)
704d9687247c0633f902d615795f6f8a
Author(s)
Kedaigle, Amanda Joy
Advisor(s)
Ernest Fraenkel.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
High-throughput "omics" data are becoming commonplace in biological research and can provide important translational insights, but there is a need for well-crafted user-friendly tools for integrating and analyzing these data. In this thesis, I present versions 1 and 2 of Omics Integrator, a software tool designed to take advantage of the Prize-Collecting Steiner Forest algorithm from graph theory to provide users with high-confidence biological networks informed by their omics results. I show the results of using this flexible tool in several studies of Huntington's disease (HD), a fatal neurodegenerative disorder with no cure. By leveraging Omics Integrator on omics datasets from induced pluripotent stem cell (iPSC) derived models of HD, I discovered and highlighted several pathways that are altered in these cell line models, including neurodevelopment and glycolytic metabolism, which may lead to important therapeutic targets in the disease. Finally, I compare omics data derived from three iPSC-derived models differentiated towards a striatal neuron cell type using different protocols, and show that by performing this large comparative analysis I can implicate functions and pathways common to several models of HD. Future integrative and comparative studies like these will be made easier by the Omics Integrator tool.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references.
Subjects
Computational and Systems Biology Program.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
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