Date of Award
5-2023
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Genetics
Committee Chair/Advisor
Alex Feltus
Committee Member
Hong Luo
Committee Member
Meredith Morris
Abstract
The field of genetics is constantly evolving. New advances in bioinformatics and computational approaches are leading to exciting new developments in our ability to treat and prevent diseases. Computational genetics provides valuable insights into the complex mechanisms and layers of biological communication that shape an organism's phenotype. Understanding these mechanisms is critical to advancing human health.
The study of diseases in genetics requires a comprehensive understanding of the interactions between various biological processes, including gene expression, protein synthesis, RNA, metabolism, and cell-cell communication. To effectively address the root causes of such diseases, multi-disciplinary approaches that integrate information from different levels of biological organization are increasingly needed. Network analysis, also known as graph theory analysis, provides a solution by allowing the visualization and quantification of these relationships.
This paper focuses on the features and functions of sPyderMIM, a program I designed to automate the construction and analysis of genotype, phenotype, and protein interaction networks constructed from clinical and genetic disease data retrieved from the OMIM (Online Mendelian Inheritance in Man) database and protein interaction data retrieved from the IntAct database. I aim to demonstrate the potential utility of sPyderMIM in facilitating the exploration of genetic and protein relationships underlying disease progression.
Recommended Citation
Keane, Devin, "Integrating OMIM and IntAct Data for the Analysis of Gene-Phenotype Interactions in Complex Diseases: a Linux-based Computational Tool for Network Analysis" (2023). All Theses. 4009.
https://open.clemson.edu/all_theses/4009
Included in
Computational Biology Commons, Genetics Commons, Genomics Commons