MS-GeneratingFunction
Download Publications Documentation
Contact: Sangtae Kim [sak008 (at) ucsd.edu]
Summary
A key problem in computational proteomics is distinguishing between correct and false peptide identifications. We argue that evaluating the error rates of peptide identifications is not unlike computing generating functions in combinatorics. We show that the generating functions and their derivatives (like spectral energy) represent new features of tandem mass spectra that, similarly to Delta-scores, significantly improve peptide identifications. We further describe how to efficiently compute the generating function of a tandem mass spectrum and to use it for rigorous computing of error rates in MS/MS searches. This improves the sensitivity-specificity trade-off of existing MS/MS search tools, addresses the notoriously difficult problem of 'one-hit-wonders' in mass spectrometry, and often eliminates the need for decoy database searches. We therefore argue that the generating function approach has the potential to increase the number of peptide identifications in typical MS/MS searches.
Documentation
- Installation
To install MS-GF, download MSGF.jar program and place it on any folder. If Java Runtime Environment is not installed in your computer, you should install it before running MS-GF.
- Run
Type 'java -jar MSGF.jar -i specFile' in command line. Currently, MS-GF supports mgf, dta and zip of dta files.
Downloads
Additional scripts will become available after the paper is accepted.
Publications
The Generating Function of Tandem Mass Spectra: a Database Independent Approach to Evaluating Statistical Significance of Peptide Identifications
Sangtae Kim, Nitin Gupta and Pavel Pevzner
Submitted.
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Media Coverage
A powerful tool for PTM discovery (Jan 2008, Journal of Proteome research, Vol 7. Issue 1)
From spectral networks to shotgun sequencing (June 2007, Nature Methods, Vol. 4 No. 6)
Identifying peptides without a database (May 2007, Journal of Proteome Research)
UCSD Computer Scientist Wins Young Investigator Award, Research on Snake Venom Proteins Highlighted (Nov 2006, UCSD)
