GEM package : methodologies to discovery associations among DNA methylation (M), genetic variation (G) and environmental influences (E)

• Main Author: Heng, Edmund Kai Yang
• Other Authors: Kwoh Chee Keong
• Format: Final Year Project
• Language: English
• Published: 2015
• Subjects: DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences; DRNTU::Engineering::Computer science and engineering::Software::Software engineering
• Online Access: http://hdl.handle.net/10356/62833
• Institution: Nanyang Technological University

Epigenetics is the process of heritable change in an organism’s phenotype that is not caused by heritable genetic factors. DNA methylation and Histone modifications are some of the most widely studied epigenetic changes. It has long been known that epigenetic changes are caused by environmental factors. These changes can regulate genes and may result in diseases such as cancer. GEM is an R software suite that seeks to associate genetic and environmental factors with the epigenetic changes in an effort to contribute to Epigenetic Wide Association Studies (EWAS). Part of that goal includes the assembly of DNA methylation processing and analysis functions, providing researchers with the computational methodologies to further explore the relationship between DNA methylation and genetic and environmental interactions. This project aims to contribute to the EWAS cause, as well as researchers in the epigenetics research community, by packaging the existing R methylation association methodologies into an R package that is GEM. This involved the analysis of existing scripts for restructure and documentation into required the package format as well as possible optimization and error correction. Due to the specificity of existing GEM methodologies, restructuring for scalability and extensibility could not be achieved. Additionally, the issue of efficiency has to be taken into consideration due to the long runtime of some scripts. In order to improve runtime of the GEM analysis, it is recommended that the use of parallel processing or external C language scripts be considered. Also, greater insight into the domain of biology and epigenetics would likely be helpful in the process of restructuring for scalability and extensibility.