Title

Comparing Statistical Methods for Analyzing DNA Methylation Data

Presenter Information

Arielle Bodine

Department

Mathematics and Statistics

Major

Applied Mathematics and Economics

Research Advisor

Olbricht, Gayla R.

Advisor's Department

Mathematics and Statistics

Funding Source

Opportunities for Undergraduate Research Experiences (OURE)

Abstract

DNA Methylation occurs when a methyl group attaches to a cytosine base in the DNA strand. This methylation of cytosine bases plays a significant role in gene expression. It has also been shown that differences in DNA methylation patterns exist between healthy and diseased individuals. Because methylation patterns vary from person to person, this creates statistical challenges when trying to quantify differences between groups of individuals. In this project, we compare statistical methods for analyzing DNA Methylation microarray data with the goal of detecting methylation differences between low-grade and high-grade HIV patients.

Biography

Arielle Bodine is a senior in applied mathematics and economics. Active on campus, she is the recruitment chair for Delta Omicron Lambda service sorority, vice-president of S&T’s chapter of Kappa Mu Epsilon math honor society and a Sue Shear fellow. She is also a student writer for the Missouri S&T marketing and communications department and an undergraduate researcher for the department of economics.

Research Category

Research Proposals

Presentation Type

Poster Presentation

Document Type

Poster

Location

Upper Atrium/Hallway

Presentation Date

11 Apr 2016, 9:00 am - 11:45 am

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Apr 11th, 9:00 AM Apr 11th, 11:45 AM

Comparing Statistical Methods for Analyzing DNA Methylation Data

Upper Atrium/Hallway

DNA Methylation occurs when a methyl group attaches to a cytosine base in the DNA strand. This methylation of cytosine bases plays a significant role in gene expression. It has also been shown that differences in DNA methylation patterns exist between healthy and diseased individuals. Because methylation patterns vary from person to person, this creates statistical challenges when trying to quantify differences between groups of individuals. In this project, we compare statistical methods for analyzing DNA Methylation microarray data with the goal of detecting methylation differences between low-grade and high-grade HIV patients.