Literature DB >> 24895379

Computing exact p-values for a cross-correlation shotgun proteomics score function.

J Jeffry Howbert1, William Stafford Noble2.   

Abstract

The core of every protein mass spectrometry analysis pipeline is a function that assesses the quality of a match between an observed spectrum and a candidate peptide. We describe a procedure for computing exact p-values for the oldest and still widely used score function, SEQUEST XCorr. The procedure uses dynamic programming to enumerate efficiently the full distribution of scores for all possible peptides whose masses are close to that of the spectrum precursor mass. Ranking identified spectra by p-value rather than XCorr significantly reduces variance because of spectrum-specific effects on the score. In combination with the Percolator postprocessor, the XCorr p-value yields more spectrum and peptide identifications at a fixed false discovery rate than Mascot, X!Tandem, Comet, and MS-GF+ across a variety of data sets.
© 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

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Year:  2014        PMID: 24895379      PMCID: PMC4159662          DOI: 10.1074/mcp.O113.036327

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  22 in total

1.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

Authors:  Andrew Keller; Alexey I Nesvizhskii; Eugene Kolker; Ruedi Aebersold
Journal:  Anal Chem       Date:  2002-10-15       Impact factor: 6.986

2.  A method for reducing the time required to match protein sequences with tandem mass spectra.

Authors:  Robertson Craig; Ronald C Beavis
Journal:  Rapid Commun Mass Spectrom       Date:  2003       Impact factor: 2.419

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

Authors:  Sangtae Kim; Nikolai Mischerikow; Nuno Bandeira; J Daniel Navarro; Louis Wich; Shabaz Mohammed; Albert J R Heck; Pavel A Pevzner
Journal:  Mol Cell Proteomics       Date:  2010-09-09       Impact factor: 5.911

4.  Peptide identification from mixture tandem mass spectra.

Authors:  Jian Wang; Josué Pérez-Santiago; Jonathan E Katz; Parag Mallick; Nuno Bandeira
Journal:  Mol Cell Proteomics       Date:  2010-03-27       Impact factor: 5.911

5.  A fast SEQUEST cross correlation algorithm.

Authors:  Jimmy K Eng; Bernd Fischer; Jonas Grossmann; Michael J Maccoss
Journal:  J Proteome Res       Date:  2008-09-06       Impact factor: 4.466

6.  Rapid and accurate peptide identification from tandem mass spectra.

Authors:  Christopher Y Park; Aaron A Klammer; Lukas Käll; Michael J MacCoss; William S Noble
Journal:  J Proteome Res       Date:  2008-05-28       Impact factor: 4.466

7.  Comparison of database search strategies for high precursor mass accuracy MS/MS data.

Authors:  Edward J Hsieh; Michael R Hoopmann; Brendan MacLean; Michael J MacCoss
Journal:  J Proteome Res       Date:  2010-02-05       Impact factor: 4.466

8.  Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases.

Authors:  Sangtae Kim; Nitin Gupta; Pavel A Pevzner
Journal:  J Proteome Res       Date:  2008-07-03       Impact factor: 4.466

9.  Fast and accurate database searches with MS-GF+Percolator.

Authors:  Viktor Granholm; Sangtae Kim; José C F Navarro; Erik Sjölund; Richard D Smith; Lukas Käll
Journal:  J Proteome Res       Date:  2013-12-23       Impact factor: 4.466

10.  False discovery rates in spectral identification.

Authors:  Kyowon Jeong; Sangtae Kim; Nuno Bandeira
Journal:  BMC Bioinformatics       Date:  2012-11-05       Impact factor: 3.169

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  19 in total

1.  Averaging Strategy To Reduce Variability in Target-Decoy Estimates of False Discovery Rate.

Authors:  Uri Keich; Kaipo Tamura; William Stafford Noble
Journal:  J Proteome Res       Date:  2019-01-03       Impact factor: 4.466

2.  Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra.

Authors:  John T Halloran; David M Rocke
Journal:  Adv Neural Inf Process Syst       Date:  2017-12

3.  Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra.

Authors:  John T Halloran; David M Rocke
Journal:  Adv Neural Inf Process Syst       Date:  2018-12

4.  Progressive calibration and averaging for tandem mass spectrometry statistical confidence estimation: Why settle for a single decoy?

Authors:  Uri Keich; William Stafford Noble
Journal:  Res Comput Mol Biol       Date:  2017-04-12

5.  The SEQUEST family tree.

Authors:  David L Tabb
Journal:  J Am Soc Mass Spectrom       Date:  2015-06-30       Impact factor: 3.109

6.  Mass spectrometrists should search only for peptides they care about.

Authors:  William Stafford Noble
Journal:  Nat Methods       Date:  2015-07       Impact factor: 28.547

7.  Controlling the FDR in imperfect matches to an incomplete database.

Authors:  Uri Keich; William Stafford Noble
Journal:  J Am Stat Assoc       Date:  2018-06-28       Impact factor: 5.033

8.  A deeper look into Comet--implementation and features.

Authors:  Jimmy K Eng; Michael R Hoopmann; Tahmina A Jahan; Jarrett D Egertson; William S Noble; Michael J MacCoss
Journal:  J Am Soc Mass Spectrom       Date:  2015-06-27       Impact factor: 3.109

9.  Multiplexed Post-Experimental Monoisotopic Mass Refinement (mPE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation.

Authors:  Inamul Hasan Madar; Seung-Ik Ko; Hokeun Kim; Dong-Gi Mun; Sangtae Kim; Richard D Smith; Sang-Won Lee
Journal:  Anal Chem       Date:  2016-12-22       Impact factor: 6.986

10.  Effective Leveraging of Targeted Search Spaces for Improving Peptide Identification in Tandem Mass Spectrometry Based Proteomics.

Authors:  Avinash K Shanmugam; Alexey I Nesvizhskii
Journal:  J Proteome Res       Date:  2015-11-24       Impact factor: 4.466

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