Defense Date

2013

Document Type

Thesis

Degree Name

Master of Bioinformatics

Department

Bioinformatics

First Advisor

Arodz Tomasz

Abstract

Nowadays we have tremendous amount of genetic data needing to be interpreted. Somatic mutations, copy number variations and methylation are example of the genetics data we are dealing with. Discovering driver mutations from these combined data types is challenging. Mutations are unpredictable and have broad heterogeneity, which makes our goal hard to accomplish. Many methods have been proposed to solve the mystery of genetics of cancer. In this project we manipulate those above mentioned genetics data types and choose to use and modified an existing method utilizing Markov Chain Monte Carlo (MCMC). The method introduced two properties, coverage and exclusivity. We obtained the data from The Cancer Genome Atlas (TCGA). We used MCMC method with three cancer types: Glioblastoma Multiform (GBM) with 214 patients, Breast Invasive Carcinoma (BRCA) with 474 patients and Colon Adenocarcinoma (COAD) with 233 patients.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

December 2013

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