Life Sciences Commons^™

Application Of Novel And Existing Methods To Identify Genes With Evidence Of Epigenetic Association: Results From Gaw20, Angga M. Fuady, Samantha Lent, Chloé Sarnowski, Nathan L. Tintle

Faculty Work Comprehensive List

Background: The rise in popularity and accessibility of DNA methylation data to evaluate epigenetic associations with disease has led to numerous methodological questions. As part of GAW20, our working group of 8 research groups focused on gene searching methods.

Results: Although the methods were varied, we identified 3 main themes within our group. First, many groups tackled the question of how best to use pedigree information in downstream analyses, finding that (a) the use of kinship matrices is common practice, (b) ascertainment corrections may be necessary, and (c) pedigree information may be useful for identifying parent-of-origin effects. Second, many groups …

A Bioinformatics Analysis Of Contributors To False Discovery For A Mouse Genotyping Array, Nisha Patel

Electronic Thesis and Dissertation Repository

Microarray experiments employing massively-parallel hybridization are valuable for the study of genetic variation, however, errors during hybridization and limitations of single-species design must be considered for use within and across species. The Mouse Diversity Genotyping Array (MDGA) is a low cost, high-resolution microarray with probes that bind to target DNA for variant detection. Errors associated with probe design and incomplete protein removal from target DNA lead to false discovery and thus necessitate examination of probe suitability and target DNA availability. Bioinformatics methods were used to carry out confirmation of probe annotations, assessment of DNA accessibility for hybridization to probes, and …

Efficient Reduced Bias Genetic Algorithm For Generic Community Detection Objectives, Aditya Karnam Gururaj Rao

Theses

The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by …