Algorithmic Study on Mass Spectrometry and Proteomics: Methods for Proteomic Mass Spectrometry Data Analysis

Tandem mass spectrometry has emerged to be one of the most powerful techniques for proteomics study. We aimed to improve existing algorithms and develop new algorithms for proteomic mass spectrometry data analysis. Three studies were presented in this book. (1) De novo peptide sequencing via tandem mass spectrometry is of interest in various situations. We developed a dynamic-programming-based suboptimal algorithm for de novo peptide sequencing. (2) A major known problem for protein and peptide identification using mass spectrometry database search is that the speed of database search is too slow, especially when searching against a large sequence database. To cope with this situation, we designed speedup algorithms for the searching process. We employed an approach combining suffix tree data structure and spectrum graph. (3) The biological inference from proteomics data generated by mass spectrometers is a challenging problem. In this study, we first introduce some difficult issues in proteomics data analysis and then we show how we managed and visualized proteomics data by using both in-house and publicly available software tools.

Dr. Bingwen Lu earned his Ph.D. from University of Southern California located at Los Angeles, California, USA. He is currently (2008) working with Professor John R. Yates III at The Scripps Research Institute located at La Jolla, California, USA. His research focuses on computational method developments for proteomics and mass spectrometry.