Literature DB >> 10582570

De novo peptide sequencing via tandem mass spectrometry.

V Dancík1, T A Addona, K R Clauser, J E Vath, P A Pevzner.   

Abstract

Peptide sequencing via tandem mass spectrometry (MS/MS) is one of the most powerful tools in proteomics for identifying proteins. Because complete genome sequences are accumulating rapidly, the recent trend in interpretation of MS/MS spectra has been database search. However, de novo MS/MS spectral interpretation remains an open problem typically involving manual interpretation by expert mass spectrometrists. We have developed a new algorithm, SHERENGA, for de novo interpretation that automatically learns fragment ion types and intensity thresholds from a collection of test spectra generated from any type of mass spectrometer. The test data are used to construct optimal path scoring in the graph representations of MS/MS spectra. A ranked list of high scoring paths corresponds to potential peptide sequences. SHERENGA is most useful for interpreting sequences of peptides resulting from unknown proteins and for validating the results of database search algorithms in fully automated, high-throughput peptide sequencing.

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Year:  1999        PMID: 10582570     DOI: 10.1089/106652799318300

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  116 in total

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6.  De novo sequencing, peptide composition analysis, and composition-based sequencing: a new strategy employing accurate mass determination by fourier transform ion cyclotron resonance mass spectrometry.

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8.  The generating function of CID, ETD, and CID/ETD pairs of tandem mass spectra: applications to database search.

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9.  A ranking-based scoring function for peptide-spectrum matches.

Authors:  Ari M Frank
Journal:  J Proteome Res       Date:  2009-05       Impact factor: 4.466

10.  Statistical characterization of ion trap tandem mass spectra from doubly charged tryptic peptides.

Authors:  David L Tabb; Lori L Smith; Linda A Breci; Vicki H Wysocki; Dayin Lin; John R Yates
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