Literature DB >> 21055489

Assigning statistical significance to proteotypic peptides via database searches.

Gelio Alves1, Aleksey Y Ogurtsov, Yi-Kuo Yu.   

Abstract

Querying MS/MS spectra against a database containing only proteotypic peptides reduces data analysis time due to reduction of database size. Despite the speed advantage, this search strategy is challenged by issues of statistical significance and coverage. The former requires separating systematically significant identifications from less confident identifications, while the latter arises when the underlying peptide is not present, due to single amino acid polymorphisms (SAPs) or post-translational modifications (PTMs), in the proteotypic peptide libraries searched. To address both issues simultaneously, we have extended RAId's knowledge database to include proteotypic information, utilized RAId's statistical strategy to assign statistical significance to proteotypic peptides, and modified RAId's programs to allow for consideration of proteotypic information during database searches. The extended database alleviates the coverage problem since all annotated modifications, even those that occurred within proteotypic peptides, may be considered. Taking into account the likelihoods of observation, the statistical strategy of RAId provides accurate E-value assignments regardless whether a candidate peptide is proteotypic or not. The advantage of including proteotypic information is evidenced by its superior retrieval performance when compared to regular database searches. Published by Elsevier B.V.

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Year:  2010        PMID: 21055489      PMCID: PMC3186061          DOI: 10.1016/j.jprot.2010.10.005

Source DB:  PubMed          Journal:  J Proteomics        ISSN: 1874-3919            Impact factor:   4.044


  23 in total

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Review 2.  Scoring proteomes with proteotypic peptide probes.

Authors:  Bernhard Kuster; Markus Schirle; Parag Mallick; Ruedi Aebersold
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3.  General framework for developing and evaluating database scoring algorithms using the TANDEM search engine.

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4.  Using annotated peptide mass spectrum libraries for protein identification.

Authors:  R Craig; J C Cortens; D Fenyo; R C Beavis
Journal:  J Proteome Res       Date:  2006-08       Impact factor: 4.466

5.  Computational prediction of proteotypic peptides for quantitative proteomics.

Authors:  Parag Mallick; Markus Schirle; Sharon S Chen; Mark R Flory; Hookeun Lee; Daniel Martin; Jeffrey Ranish; Brian Raught; Robert Schmitt; Thilo Werner; Bernhard Kuster; Ruedi Aebersold
Journal:  Nat Biotechnol       Date:  2006-12-31       Impact factor: 54.908

6.  Development and validation of a spectral library searching method for peptide identification from MS/MS.

Authors:  Henry Lam; Eric W Deutsch; James S Eddes; Jimmy K Eng; Nichole King; Stephen E Stein; Ruedi Aebersold
Journal:  Proteomics       Date:  2007-03       Impact factor: 3.984

Review 7.  PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows.

Authors:  Eric W Deutsch; Henry Lam; Ruedi Aebersold
Journal:  EMBO Rep       Date:  2008-05       Impact factor: 8.807

8.  The use of proteotypic peptide libraries for protein identification.

Authors:  Robertson Craig; John P Cortens; Ronald C Beavis
Journal:  Rapid Commun Mass Spectrom       Date:  2005       Impact factor: 2.419

9.  Statistical Characterization of a 1D Random Potential Problem - with applications in score statistics of MS-based peptide sequencing.

Authors:  Gelio Alves; Yi-Kuo Yu
Journal:  Physica A       Date:  2008-11-15       Impact factor: 3.263

10.  RAId_DbS: mass-spectrometry based peptide identification web server with knowledge integration.

Authors:  Gelio Alves; Aleksey Y Ogurtsov; Yi-Kuo Yu
Journal:  BMC Genomics       Date:  2008-10-27       Impact factor: 3.969

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  2 in total

1.  Improving peptide identification sensitivity in shotgun proteomics by stratification of search space.

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Journal:  J Proteome Res       Date:  2013-05-29       Impact factor: 4.466

2.  PhosphoHunter: An Efficient Software Tool for Phosphopeptide Identification.

Authors:  Alessandra Tiengo; Lorenzo Pasotti; Nicola Barbarini; Paolo Magni
Journal:  Adv Bioinformatics       Date:  2015-01-12
  2 in total

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