Bioinformatics Analysis for Cancer Gene Mutations

Ming-Ying Leung, University of Texas at El Paso

Usage Details

Advances of NGS technologies in the past few years have greatly facilitated research studies on many human diseases at the genomic level. While the exome, consisting of all the exons (i.e., protein-coding regions), represents less than 3% of the entire genome, whole exome sequence analyses in many recent studies have proven to be an efficient way of identifying novel genetic alterations associated with various types of cancer. At the cancer bio-repository housed at The University of Texas at El Paso, an increasing number of tissue samples from cancer patients in the hospitals of the local region have been collected. We have recently initiated a research project to develop a bioinformatics pipeline that uses a combination of scoring functions, visualization tools, and statistical methods to filter through multiple cancer mutations and identify a short list of exonic variants for focused experimental investigation in the wet lab. However, the computations involved in our current evolution-based scoring function generally requires over a week to complete the analysis of the data from a single individual. The work in this internship will involve exploring the use of parallel programming and Blue Waters resources to speed up the computations and complete the analysis much more efficiently.