Integrating Omics data : a new software tool and its use in implicating therapeutic targets in Huntington's disease

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.