articleTop 1% cited
Open Mass Spectrometry Search Algorithm
Journal of Proteome Research · 2004 · Vol. 3(5) · pp. 958–964
Lewis Y. Geer✉(National Institute of Mental Health)Sanford P. Markey(National Center for Biotechnology Information)Jeffrey A. Kowalak(National Institutes of Health)Lukas Wagner(National Institutes of Health)Ming Xu(National Institutes of Health)Dawn Maynard(National Institutes of Health)Xiaoyu Yang(National Institutes of Health)Wenyao Shi(National Center for Biotechnology Information)Stephen H. Bryant(National Institutes of Health)
Abstract
Large numbers of MS/MS peptide spectra generated in proteomics experiments require efficient, sensitive and specific algorithms for peptide identification. In the Open Mass Spectrometry Search Algorithm (OMSSA), specificity is calculated by a classic probability score using an explicit model for matching experimental spectra to sequences. At default thresholds, OMSSA matches more spectra from a standard protein cocktail than a comparable algorithm. OMSSA is designed to be faster than published algorithms in searching large MS/MS datasets.
Advanced Proteomics Techniques and ApplicationsMass Spectrometry Techniques and ApplicationsMetabolomics and Mass Spectrometry StudiesMass spectrometryDatabase search engineMatching (statistics)Computer scienceAlgorithmProteomicsMass spectrumIdentification (biology)ChemistrySearch engine
MeSH terms
AlgorithmsFalse Positive ReactionsPeptide FragmentsProteinsROC CurveMass SpectrometryReproducibility of ResultsPoisson DistributionComputational BiologyDatabases, ProteinProteomics
Funding
- National Institutes of Health
Citations
1,428
FWCI
18.17
field-weighted impact
References
20
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100%
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Citations per year
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Nucleic Acids Research · 1997 · 74,154 citations
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