Electrical and Computer Engineering graduate student wins award

A graduate student in the Department of Electrical and Computer Engineering recently won an award for "Highest achievement -  poster presentation" at the 2010 TGen/VARI scientific retreat.Youting Sunwon the award for her poster titled "BPDA - A Bayesian peptide detection algorithm for mass spectrometry." Her poster on a novel Bayesian method for peptide detection in mass spectrometry data (which outperforms many standard software currently in use) was one of four selected for this award. Sun competed against more than 100 posters by researchers from all over, including The University of Michigan, The University of Virginia, M.D. Anderson, The Broad Institute of MIT and Harvard and Johns Hopkins medical school, as well as several from Europe, Canada, Australia, New Zealand, Egypt and Uganda. A summary of her work is as follows: We present BPDA, a Bayesian approach for peptide detection in high-resolution mass spectrometry (MS) data. BPDA is based on a rigorous statistical framework and avoids problems, such as voting and ad-hoc thresholding, generally encountered in existing algorithms. BPDA systematically evaluates all possible combinations of possible peptide candidates to interpret a given spectrum, and iteratively finds the best fitting peptide signals. Since BPDA looks for the optimal among all possible interpretations, the procedure is thus systematic. In contrast, most of the existing methods utilize greedy approaches, and are neither systematic nor optimal. As opposed to previous detection methods, BPDA performs deisotoping and deconvolution of mass spectra simultaneously, which enables better identification of weak peptide signals and produces higher sensitivities and more robust results. Unlike template-matching algorithms, BPDA can handle complex data where features overlap. Our experimental results indicate that BPDA performs well on simulated data and real MS data sets, for various resolutions and signal to noise ratios, and compares very favorably with commonly used commercial and open-source software. Sun received her B.E. degree in Department of Automation in 2007 from Tsinghua University, Beijing, China and is currently pursuing her Ph.D degree in electrical engineering at Texas A&M University. Her research interests include proteomics and genomic signal processing. She is co-advised by Edward R. Dougherty and Ulisses Braga-Neto. Sun is co-advised by Drs. Edward R. Dougherty and Ulisses Braga-Neto, co-authors of the poster.