Shotgun proteomics using mass spectrometry is a powerful method for protein identification but suffers limited sensitivity in complex samples. Integrating peptide identifications from multiple database search engines is a promising strategy to increase the number of peptide identifications and reduce the volume of unassigned tandem mass spectra. Existing methods pool statistical significance scores such as p-values or posterior probabilities of peptide-spectrum matches (PSMs) from multiple search engines after high scoring peptides have been assigned to spectra, but these methods lack reliable control of identification error rates as data are integrated from different search engines. We developed a statistically coherent method for integrative analysis, termed MSblender. MSblender converts raw search scores from search engines into a probability score for all possible PSMs and properly accounts for the correlation between search scores. The method reliably estimates false discovery rates and identifies more PSMs than any single search engine at the same false discovery rate. Increased identifications increment spectral counts for all detected proteins and allow quantification of proteins that would not have been quantified by individual search engines. We also demonstrate that enhanced quantification contributes to improve sensitivity in differential expression analyses.
Dr. Nesvizhskii received an M.S. (with honors) from St. Petersburg State Technical University, Department of Physics and Technology, St. Petersburg, Russia in 1995 and a Ph.D. in Physics from the University of Washington, Seattle in 2001. He completed postdoctoral training in Ruedi Aebersold Lab at the Institute for Systems Biology in Seattle, Washington from 2001-2003 and joined the staff as a Research Scientist upon completion of training.
Dr. Nesvizhskii was the recipient of a medal for "Best Student Scientific Work" awarded by the Russion Federation State Committee of Higher Education and was named Russian Presidential Fellow for the period 1994-1995 and Soros Fellow for the period 1995-1996. He is a member of the Human Proteome Organization (HUPO), International Society for Computational Biology, and the American Society for Mass Spectrometry.In November 2005, Dr. Nesvizhskii joined the faculty of the Department of Pathology as an Assistant Professor.
Dr. Nesvizhskii’s research interest is in the field of quantitative proteomics, with a focus on the development of computational methods for processing and extracting biological information from complex proteomic datasets. Similar to other global high throughput technologies such as microarray gene expression analysis, proteomics is extremely dependent on the ability to quickly and reliably analyze large amounts of experimental data. One of the aims of Dr. Nesvizhskii’s research is to close the critical gap between the development of high throughput quantitative proteomics methods and the ability to deal with the resulting data deluge and to convert it into new biological knowledge or to develop new disease biomarkers. The efforts in his lab range from the development of computational tools and statistical methods for mass spectrometry-based peptide and protein identification and quantification, to the establishment of guidelines and standards for proteomic data analysis and publication, to the creation of public databases and proteomic data repositories and integration of proteomic with genomic and other types of biological data.