Literature DB >> 23148905

Recognizing uncertainty increases robustness and reproducibility of mass spectrometry-based protein inferences.

Oliver Serang1, Luminita Moruz, Michael R Hoopmann, Lukas Käll.   

Abstract

Parsimony and protein grouping are widely employed to enforce economy in the number of identified proteins, with the goal of increasing the quality and reliability of protein identifications; however, in a counterintuitive manner, parsimony and protein grouping may actually decrease the reproducibility and interpretability of protein identifications. We present a simple illustration demonstrating ways in which parsimony and protein grouping may lower the reproducibility or interpretability of results. We then provide an example of a data set where a probabilistic method increases the reproducibility and interpretability of identifications made on replicate analyses of Human Du145 prostate cancer cell lines.

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Year:  2012        PMID: 23148905      PMCID: PMC3534833          DOI: 10.1021/pr300426s

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


  14 in total

1.  The need for guidelines in publication of peptide and protein identification data: Working Group on Publication Guidelines for Peptide and Protein Identification Data.

Authors:  Steven Carr; Ruedi Aebersold; Michael Baldwin; Al Burlingame; Karl Clauser; Alexey Nesvizhskii
Journal:  Mol Cell Proteomics       Date:  2004-04-09       Impact factor: 5.911

2.  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

3.  Proteomic parsimony through bipartite graph analysis improves accuracy and transparency.

Authors:  Bing Zhang; Matthew C Chambers; David L Tabb
Journal:  J Proteome Res       Date:  2007-08-04       Impact factor: 4.466

4.  Two-dimensional target decoy strategy for shotgun proteomics.

Authors:  Marshall W Bern; Yong J Kil
Journal:  J Proteome Res       Date:  2011-11-07       Impact factor: 4.466

5.  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

6.  Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry.

Authors:  Lukas Reiter; Manfred Claassen; Sabine P Schrimpf; Marko Jovanovic; Alexander Schmidt; Joachim M Buhmann; Michael O Hengartner; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2009-07-16       Impact factor: 5.911

7.  Quality assessments of peptide-spectrum matches in shotgun proteomics.

Authors:  Viktor Granholm; Lukas Käll
Journal:  Proteomics       Date:  2011-02-07       Impact factor: 3.984

Review 8.  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

9.  Efficient marginalization to compute protein posterior probabilities from shotgun mass spectrometry data.

Authors:  Oliver Serang; Michael J MacCoss; William Stafford Noble
Journal:  J Proteome Res       Date:  2010-10-01       Impact factor: 4.466

10.  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

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  19 in total

1.  Nonparametric Bayesian evaluation of differential protein quantification.

Authors:  Oliver Serang; A Ertugrul Cansizoglu; Lukas Käll; Hanno Steen; Judith A Steen
Journal:  J Proteome Res       Date:  2013-09-11       Impact factor: 4.466

2.  Correlation of actin crosslinker and capper expression levels with stereocilia growth phases.

Authors:  Matthew R Avenarius; Katherine W Saylor; Megan R Lundeberg; Phillip A Wilmarth; Jung-Bum Shin; Kateri J Spinelli; James M Pagana; Leonardo Andrade; Bechara Kachar; Dongseok Choi; Larry L David; Peter G Barr-Gillespie
Journal:  Mol Cell Proteomics       Date:  2013-12-07       Impact factor: 5.911

3.  Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics.

Authors:  Matthew The; Lukas Käll
Journal:  Mol Cell Proteomics       Date:  2018-11-27       Impact factor: 5.911

4.  A non-parametric cutout index for robust evaluation of identified proteins.

Authors:  Oliver Serang; Joao Paulo; Hanno Steen; Judith A Steen
Journal:  Mol Cell Proteomics       Date:  2013-01-04       Impact factor: 5.911

5.  Human Spermatozoa Quantitative Proteomic Signature Classifies Normo- and Asthenozoospermia.

Authors:  Mayank Saraswat; Sakari Joenväärä; Tushar Jain; Anil Kumar Tomar; Ashima Sinha; Sarman Singh; Savita Yadav; Risto Renkonen
Journal:  Mol Cell Proteomics       Date:  2016-11-28       Impact factor: 5.911

Review 6.  Challenges and Opportunities for Bayesian Statistics in Proteomics.

Authors:  Oliver M Crook; Chun-Wa Chung; Charlotte M Deane
Journal:  J Proteome Res       Date:  2022-03-08       Impact factor: 4.466

7.  SweetSEQer, simple de novo filtering and annotation of glycoconjugate mass spectra.

Authors:  Oliver Serang; John W Froehlich; Jan Muntel; Gary McDowell; Hanno Steen; Richard S Lee; Judith A Steen
Journal:  Mol Cell Proteomics       Date:  2013-02-26       Impact factor: 5.911

8.  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

9.  EPIFANY: A Method for Efficient High-Confidence Protein Inference.

Authors:  Julianus Pfeuffer; Timo Sachsenberg; Tjeerd M H Dijkstra; Oliver Serang; Knut Reinert; Oliver Kohlbacher
Journal:  J Proteome Res       Date:  2020-02-13       Impact factor: 4.466

10.  A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms.

Authors:  Matthew The; Fredrik Edfors; Yasset Perez-Riverol; Samuel H Payne; Michael R Hoopmann; Magnus Palmblad; Björn Forsström; Lukas Käll
Journal:  J Proteome Res       Date:  2018-04-16       Impact factor: 4.466

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