Literature DB >> 23298186

When target-decoy false discovery rate estimations are inaccurate and how to spot instances.

Robert J Chalkley1.   

Abstract

To address problems with estimating the reliability of proteomic search engine results from mass spectrometry fragmentation data, the use of target-decoy database searching has become the de facto approach for estimating a false discovery rate. Several articles have been written about the effects of different ways of creating the decoy database, effects of the search engine scoring, or effects of search parameters on whether this approach provides an accurate estimate, not all agreeing with each other's conclusions. Hence, there may be some confusion about how effective this approach is and how broadly it can be applied. Although it is generally very effective, in this article I will try to emphasize some of the pitfalls and dangers of using the target-decoy approach and will indicate tell-tale signs that something may be amiss. This information will hopefully help researchers become more astute in their assessment of search results.

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Year:  2013        PMID: 23298186      PMCID: PMC4751987          DOI: 10.1021/pr301063v

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  9 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.  Bromenshenk et al (PLoS One, 2011, 5(10):e13181) have claimed to have found peptides from an invertebrate iridovirus in bees.

Authors:  Leonard J Foster
Journal:  Mol Cell Proteomics       Date:  2012-01       Impact factor: 5.911

3.  Improving software performance for peptide electron transfer dissociation data analysis by implementation of charge state- and sequence-dependent scoring.

Authors:  Peter R Baker; Katalin F Medzihradszky; Robert J Chalkley
Journal:  Mol Cell Proteomics       Date:  2010-05-31       Impact factor: 5.911

4.  Reporting protein identification data: the next generation of guidelines.

Authors:  Ralph A Bradshaw; Alma L Burlingame; Steven Carr; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2006-05       Impact factor: 5.911

5.  The problem with peptide presumption and the downfall of target-decoy false discovery rates.

Authors:  Bret Cooper
Journal:  Anal Chem       Date:  2012-11-01       Impact factor: 6.986

Review 6.  A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics.

Authors:  Alexey I Nesvizhskii
Journal:  J Proteomics       Date:  2010-09-08       Impact factor: 4.044

7.  Improvements to the percolator algorithm for Peptide identification from shotgun proteomics data sets.

Authors:  Marina Spivak; Jason Weston; Léon Bottou; Lukas Käll; William Stafford Noble
Journal:  J Proteome Res       Date:  2009-07       Impact factor: 4.466

8.  Interpretation of data underlying the link between colony collapse disorder (CCD) and an invertebrate iridescent virus.

Authors:  Leonard J Foster
Journal:  Mol Cell Proteomics       Date:  2011-03       Impact factor: 5.911

9.  The effect of using an inappropriate protein database for proteomic data analysis.

Authors:  Giselle M Knudsen; Robert J Chalkley
Journal:  PLoS One       Date:  2011-06-14       Impact factor: 3.240

  9 in total
  7 in total

Review 1.  Lessons in de novo peptide sequencing by tandem mass spectrometry.

Authors:  Katalin F Medzihradszky; Robert J Chalkley
Journal:  Mass Spectrom Rev       Date:  2015 Jan-Feb       Impact factor: 10.946

2.  Matching cross-linked peptide spectra: only as good as the worse identification.

Authors:  Michael J Trnka; Peter R Baker; Philip J J Robinson; A L Burlingame; Robert J Chalkley
Journal:  Mol Cell Proteomics       Date:  2013-12-12       Impact factor: 5.911

3.  MS-viewer: a web-based spectral viewer for proteomics results.

Authors:  Peter R Baker; Robert J Chalkley
Journal:  Mol Cell Proteomics       Date:  2014-03-03       Impact factor: 5.911

4.  Systematic Errors in Peptide and Protein Identification and Quantification by Modified Peptides.

Authors:  Boris Bogdanow; Henrik Zauber; Matthias Selbach
Journal:  Mol Cell Proteomics       Date:  2016-05-23       Impact factor: 5.911

5.  New glycoproteomics software, GlycoPep Evaluator, generates decoy glycopeptides de novo and enables accurate false discovery rate analysis for small data sets.

Authors:  Zhikai Zhu; Xiaomeng Su; Eden P Go; Heather Desaire
Journal:  Anal Chem       Date:  2014-08-28       Impact factor: 6.986

Review 6.  Proteomics for systems toxicology.

Authors:  Bjoern Titz; Ashraf Elamin; Florian Martin; Thomas Schneider; Sophie Dijon; Nikolai V Ivanov; Julia Hoeng; Manuel C Peitsch
Journal:  Comput Struct Biotechnol J       Date:  2014-08-27       Impact factor: 7.271

Review 7.  MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts.

Authors:  Xue Wang; Shichen Shen; Sailee Suryakant Rasam; Jun Qu
Journal:  Mass Spectrom Rev       Date:  2019-03-28       Impact factor: 10.946

  7 in total

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