Literature DB >> 21953092

Target-decoy approach and false discovery rate: when things may go wrong.

Nitin Gupta1, Nuno Bandeira, Uri Keich, Pavel A Pevzner.   

Abstract

The target-decoy approach (TDA) has done the field of proteomics a great service by filling in the need to estimate the false discovery rates (FDR) of peptide identifications. While TDA is often viewed as a universal solution to the problem of FDR evaluation, we argue that the time has come to critically re-examine TDA and to acknowledge not only its merits but also its demerits. We demonstrate that some popular MS/MS search tools are not TDA-compliant and that it is easy to develop a non-TDA compliant tool that outperforms all TDA-compliant tools. Since the distinction between TDA-compliant and non-TDA compliant tools remains elusive, we are concerned about a possible proliferation of non-TDA-compliant tools in the future (developed with the best intentions). We are also concerned that estimation of the FDR by TDA awkwardly depends on a virtual coin toss and argue that it is important to take the coin toss factor out of our estimation of the FDR. Since computing FDR via TDA suffers from various restrictions, we argue that TDA is not needed when accurate p-values of individual Peptide-Spectrum Matches are available.

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Year:  2011        PMID: 21953092      PMCID: PMC3220955          DOI: 10.1007/s13361-011-0139-3

Source DB:  PubMed          Journal:  J Am Soc Mass Spectrom        ISSN: 1044-0305            Impact factor:   3.109


  31 in total

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Journal:  Mol Cell Proteomics       Date:  2010-09-09       Impact factor: 5.911

2.  Prediction of error associated with false-positive rate determination for peptide identification in large-scale proteomics experiments using a combined reverse and forward peptide sequence database strategy.

Authors:  Edward L Huttlin; Adrian D Hegeman; Amy C Harms; Michael R Sussman
Journal:  J Proteome Res       Date:  2007-01       Impact factor: 4.466

3.  Comment on "Unbiased statistical analysis for multi-stage proteomic search strategies".

Authors:  Marshall Bern; Yong J Kil
Journal:  J Proteome Res       Date:  2011-02-21       Impact factor: 4.466

4.  Comparative evaluation of tandem MS search algorithms using a target-decoy search strategy.

Authors:  Brian M Balgley; Tom Laudeman; Li Yang; Tao Song; Cheng S Lee
Journal:  Mol Cell Proteomics       Date:  2007-05-28       Impact factor: 5.911

5.  Local alignment statistics.

Authors:  S F Altschul; W Gish
Journal:  Methods Enzymol       Date:  1996       Impact factor: 1.600

6.  Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes.

Authors:  S Karlin; S F Altschul
Journal:  Proc Natl Acad Sci U S A       Date:  1990-03       Impact factor: 11.205

Review 7.  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

8.  False discovery rates of protein identifications: a strike against the two-peptide rule.

Authors:  Nitin Gupta; Pavel A Pevzner
Journal:  J Proteome Res       Date:  2009-09       Impact factor: 4.466

9.  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

10.  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

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  61 in total

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Review 3.  Peptide identification by tandem mass spectrometry with alternate fragmentation modes.

Authors:  Adrian Guthals; Nuno Bandeira
Journal:  Mol Cell Proteomics       Date:  2012-05-17       Impact factor: 5.911

Review 4.  A face in the crowd: recognizing peptides through database search.

Authors:  Jimmy K Eng; Brian C Searle; Karl R Clauser; David L Tabb
Journal:  Mol Cell Proteomics       Date:  2011-08-29       Impact factor: 5.911

5.  A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets.

Authors:  Mikhail M Savitski; Mathias Wilhelm; Hannes Hahne; Bernhard Kuster; Marcus Bantscheff
Journal:  Mol Cell Proteomics       Date:  2015-05-17       Impact factor: 5.911

6.  Mass spectrometry-based protein identification with accurate statistical significance assignment.

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Journal:  Bioinformatics       Date:  2014-10-31       Impact factor: 6.937

7.  The generating function approach for Peptide identification in spectral networks.

Authors:  Adrian Guthals; Christina Boucher; Nuno Bandeira
Journal:  J Comput Biol       Date:  2014-11-25       Impact factor: 1.479

8.  Typing and Characterization of Bacteria Using Bottom-up Tandem Mass Spectrometry Proteomics.

Authors:  Fredrik Boulund; Roger Karlsson; Lucia Gonzales-Siles; Anna Johnning; Nahid Karami; Omar Al-Bayati; Christina Åhrén; Edward R B Moore; Erik Kristiansson
Journal:  Mol Cell Proteomics       Date:  2017-04-18       Impact factor: 5.911

Review 9.  Inference and validation of protein identifications.

Authors:  Manfred Claassen
Journal:  Mol Cell Proteomics       Date:  2012-08-03       Impact factor: 5.911

Review 10.  A review of methods for interpretation of glycopeptide tandem mass spectral data.

Authors:  Han Hu; Kshitij Khatri; Joshua Klein; Nancy Leymarie; Joseph Zaia
Journal:  Glycoconj J       Date:  2015-11-26       Impact factor: 2.916

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