Open mass spectrometry search algorithm.

By Lewis Y. Geer, S. Markey, J. Kowalak, L. Wagner, Ming Xu, D. Maynard, Xiaoyu Yang, Wenyao Shi, Stephen H. Bryant. Journal of Proteome Research, 2004

1,451 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/0a8dcad119ed6b8e2958841547fda8d432f43a60

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Citations in the corpus

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics (2010, background): “[163] Geer LY, Markey SP, Kowalak JA, Wagner L, Xu M, Maynard DM, et al.”
Spectral Probabilities and Generating Functions of Tandem Mass Spectra: a Strike Against Decoy Databases (2008, background): “The major difference here is that MS/MS searches are currently based on empirical database-dependent estimates of error rates (often represented by Poisson, Gaussian, hypergeometric, or other approximations of tails of score distributions [5,6,7]) as opposed to the analytically derived and database-”
ProLuCID: an improved SEQUEST-like algorithm with enhanced sensitivity and specificity (2015, methodology): “Thisisdonebymultiplealgo-rithms, including the hypergeometric probability based PEP_PROBE [29], OMSSA [31] and the central limit theorem based PEP_PROBE [32].”
Comet: An open‐source MS/MS sequence database search tool (2013, methodology): “Tandem version 2011.12.01.1 [13] and OMSSA version 2.1.9 [14], two of the most widely used open-source MS/MS search tools.”
Universal database search tool for proteomics (2014, neutral): “This simple ‘dot-product’ scoring model contrasts with many other database search 2,4,27,28 and re-scoring 9,10 tools, using sophisticated scoring functions that often make it difﬁcult to satisfy the third condition.”
High Sensitivity Crosslink Detection Coupled With Integrative Structure Modeling in the Mass Spec Studio * (2016, methodology): “A search of the full set of possible linear peptides is conducted against each MS 2 spectrum using OMSSA (38), to generate a set of peptide-spectrum matches (PSM’s) for each MS 2 spectrum.”
Scaffold: A bioinformatic tool for validating MS/MS‐based proteomic studies (2010, methodology): “Tandem [12] and OMSSA [13]).”
Deciphering Protein–Protein Interactions. Part I. Experimental Techniques and Databases (2007, neutral): “to identify proteins by their sequence [57–60].”
PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification (2011, methodology): “Representative de novo sequencing software packages include PEAKS (1), PepNovo (2), NovoHMM (3), and Lutefisk (4), and representative database search software packages include Mascot (5), SEQUEST (6), X!Tandem (7), OMSSA (8), ProteinProspector (9), MaxQuant”
Less label, more free: Approaches in label‐free quantitative mass spectrometry (2011, methodology): “Database searching involves the use of scoring algorithms including those that employ spectral correlation functions, such as SEQUEST [28], and those which use probabilitybased scoring-related concepts such as MASCOT [26], X!TANDEM [29], and OMSSA [76].”