Invited Talks

DE NOVO SHOTGUN PROTEIN SEQUENCING VIA SPECTRAL NETWORKS ANALYSIS

Abstract
Advances in tandem mass-spectrometry steadily increase the rate of generation of tandem (MS/MS) spectra. As a result, the existing approaches that compare spectra against databases are already facing a bottleneck, particularly when interpreting spectra of modified peptides. We introduce a new idea that allows one to perform MS/MS search without ever comparing a spectrum against a database. Our approach utilizes spectral pairs - pairs of spectra from overlapping peptides or from unmodified and modified versions of the same peptide. While seemingly redundant, spectral pairs open up computational avenues that were never explored before. Spectral networks (formed by spectral pairs) allow one to greatly reduce the number of noise peaks, and to generate a small number of peptide reconstructions that are likely to contain the correct one. The MS/MS database search is thus reduced to fast pattern matching (rather than time-consuming matching of spectra against databases). In addition to speed, our approach provides a new paradigm for identifying post-translational modifications and de novo shotgun protein sequencing. We illustrate the applications of spectral networks to shotgun protein sequencing of snake venoms.

This is a joint work with Nuno Bandeira (UCSD) and Karl Clauser (Broad)

Biography
Pavel A. Pevzner is the Ronald R. Taylor Professor of Computer Science in the Department of Computer Science and Engineering at the University of California, San Diego, where he is also Adjunct Professor of Mathematics. Prior to that, he was a Professor in the Departments of Mathematics, Computer Science, and Molecular Biology at the University of Southern California. He was also Associate Professor in the Department of Computer Science at Pennsylvania State University. Professor Pevzner received his Ph.D. in Mathematics and Physics from the Moscow Institute of Physics and Technology in 1988.

In 2006 Pevzner was named a Howard Hughes Medical Institute (HHMI) Professor. His research focuses on combinatorial algorithms in computational molecular biology. His interdisciplinary HHMI project involves three programs: an introductory bioinformatics course suitable for all biology students; a research course in bioinformatics in which undergraduates, graduate students, and faculty will collaborate on the same research project; and a residential summer program for gifted high school students.

Pevzner's research interests include:
Fragment Assembly in DNA Sequencing
• Pattern Discovery and Regulatory Genomics
• Computational Mass-Spectrometry
• Genome Rearrangements
• Optimization of DNA Array Manufacturing