Clustering Millions of Tandem Mass Spectra
| Autor: | Richard D. Smith, Steven P. Briggs, Nuno Bandeira, Pavel A. Pevzner, Zhouxin Shen, Stephen Tanner, Ari Frank |
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| Jazyk: | angličtina |
| Rok vydání: | 2007 |
| Předmět: |
Proteomics
Proteomics methods Chemistry Extramural business.industry Molecular Sequence Data Analytical chemistry Computational Biology Pattern recognition General Chemistry Open source software Tandem mass spectrometry Biochemistry Tandem mass spectrum Article ComputingMethodologies_PATTERNRECOGNITION Tandem Mass Spectrometry Redundancy (engineering) Cluster Analysis Database search engine Artificial intelligence Amino Acid Sequence Cluster analysis business Peptides |
| Popis: | Tandem mass spectrometry (MS/MS) experiments often generate redundant data sets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/MS database searches. We present an efficient clustering approach for analyzing large MS/MS data sets (over 10 million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular nonclustered searches. Our open source software MS-Clustering is available for download at http://peptide.ucsd.edu or can be run online at http://proteomics.bioprojects.org/MassSpec. |
| Databáze: | OpenAIRE |
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