Literature DB >> 22052993

Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam Principles).

Christopher R Kinsinger1, James Apffel, Mark Baker, Xiaopeng Bian, Christoph H Borchers, Ralph Bradshaw, Mi-Youn Brusniak, Daniel W Chan, Eric W Deutsch, Bruno Domon, Jeff Gorman, Rudolf Grimm, William Hancock, Henning Hermjakob, David Horn, Christie Hunter, Patrik Kolar, Hans-Joachim Kraus, Hanno Langen, Rune Linding, Robert L Moritz, Gilbert S Omenn, Ron Orlando, Akhilesh Pandey, Peipei Ping, Amir Rahbar, Robert Rivers, Sean L Seymour, Richard J Simpson, Douglas Slotta, Richard D Smith, Stephen E Stein, David L Tabb, Danilo Tagle, John R Yates, Henry Rodriguez.   

Abstract

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the United States National Cancer Institute convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed up on two primary needs for the wide use of quality metrics: 1) an evolving list of comprehensive quality metrics and 2) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in the Journal of Proteome Research, Molecular and Cellular Proteomics, Proteomics, and Proteomics Clinical Applications as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.

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Year:  2011        PMID: 22052993      PMCID: PMC3237091          DOI: 10.1074/mcp.O111.015446

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


  44 in total

1.  Minimum information about a microarray experiment (MIAME)-toward standards for microarray data.

Authors:  A Brazma; P Hingamp; J Quackenbush; G Sherlock; P Spellman; C Stoeckert; J Aach; W Ansorge; C A Ball; H C Causton; T Gaasterland; P Glenisson; F C Holstege; I F Kim; V Markowitz; J C Matese; H Parkinson; A Robinson; U Sarkans; S Schulze-Kremer; J Stewart; R Taylor; J Vilo; M Vingron
Journal:  Nat Genet       Date:  2001-12       Impact factor: 38.330

2.  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

3.  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

4.  Open source system for analyzing, validating, and storing protein identification data.

Authors:  Robertson Craig; John P Cortens; Ronald C Beavis
Journal:  J Proteome Res       Date:  2004 Nov-Dec       Impact factor: 4.466

5.  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

Review 6.  The minimum information about a proteomics experiment (MIAPE).

Authors:  Chris F Taylor; Norman W Paton; Kathryn S Lilley; Pierre-Alain Binz; Randall K Julian; Andrew R Jones; Weimin Zhu; Rolf Apweiler; Ruedi Aebersold; Eric W Deutsch; Michael J Dunn; Albert J R Heck; Alexander Leitner; Marcus Macht; Matthias Mann; Lennart Martens; Thomas A Neubert; Scott D Patterson; Peipei Ping; Sean L Seymour; Puneet Souda; Akira Tsugita; Joel Vandekerckhove; Thomas M Vondriska; Julian P Whitelegge; Marc R Wilkins; Ioannnis Xenarios; John R Yates; Henning Hermjakob
Journal:  Nat Biotechnol       Date:  2007-08       Impact factor: 54.908

7.  Colander: a probability-based support vector machine algorithm for automatic screening for CID spectra of phosphopeptides prior to database search.

Authors:  Bingwen Lu; Cristian I Ruse; John R Yates
Journal:  J Proteome Res       Date:  2008-06-19       Impact factor: 4.466

8.  Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma.

Authors:  Terri A Addona; Susan E Abbatiello; Birgit Schilling; Steven J Skates; D R Mani; David M Bunk; Clifford H Spiegelman; Lisa J Zimmerman; Amy-Joan L Ham; Hasmik Keshishian; Steven C Hall; Simon Allen; Ronald K Blackman; Christoph H Borchers; Charles Buck; Helene L Cardasis; Michael P Cusack; Nathan G Dodder; Bradford W Gibson; Jason M Held; Tara Hiltke; Angela Jackson; Eric B Johansen; Christopher R Kinsinger; Jing Li; Mehdi Mesri; Thomas A Neubert; Richard K Niles; Trenton C Pulsipher; David Ransohoff; Henry Rodriguez; Paul A Rudnick; Derek Smith; David L Tabb; Tony J Tegeler; Asokan M Variyath; Lorenzo J Vega-Montoto; Asa Wahlander; Sofia Waldemarson; Mu Wang; Jeffrey R Whiteaker; Lei Zhao; N Leigh Anderson; Susan J Fisher; Daniel C Liebler; Amanda G Paulovich; Fred E Regnier; Paul Tempst; Steven A Carr
Journal:  Nat Biotechnol       Date:  2009-06-28       Impact factor: 54.908

9.  Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry.

Authors:  Susan E Abbatiello; D R Mani; Hasmik Keshishian; Steven A Carr
Journal:  Clin Chem       Date:  2009-12-18       Impact factor: 8.327

10.  The Proteomics Identifications database: 2010 update.

Authors:  Juan Antonio Vizcaíno; Richard Côté; Florian Reisinger; Harald Barsnes; Joseph M Foster; Jonathan Rameseder; Henning Hermjakob; Lennart Martens
Journal:  Nucleic Acids Res       Date:  2009-11-11       Impact factor: 16.971

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  10 in total

1.  Processing shotgun proteomics data on the Amazon cloud with the trans-proteomic pipeline.

Authors:  Joseph Slagel; Luis Mendoza; David Shteynberg; Eric W Deutsch; Robert L Moritz
Journal:  Mol Cell Proteomics       Date:  2014-11-23       Impact factor: 5.911

2.  Revolutionizing Precision Oncology through Collaborative Proteogenomics and Data Sharing.

Authors:  Henry Rodriguez; Stephen R Pennington
Journal:  Cell       Date:  2018-04-19       Impact factor: 41.582

Review 3.  Quality assessment for clinical proteomics.

Authors:  David L Tabb
Journal:  Clin Biochem       Date:  2012-12-12       Impact factor: 3.281

4.  Quantitative profiling of major neutral lipid classes in human meibum by direct infusion electrospray ionization mass spectrometry.

Authors:  Jianzhong Chen; Kari B Green; Kelly K Nichols
Journal:  Invest Ophthalmol Vis Sci       Date:  2013-08-23       Impact factor: 4.799

Review 5.  An assessment of current bioinformatic solutions for analyzing LC-MS data acquired by selected reaction monitoring technology.

Authors:  Mi-Youn K Brusniak; Caroline S Chu; Ulrike Kusebauch; Mark J Sartain; Julian D Watts; Robert L Moritz
Journal:  Proteomics       Date:  2012-04       Impact factor: 3.984

6.  The mzIdentML data standard for mass spectrometry-based proteomics results.

Authors:  Andrew R Jones; Martin Eisenacher; Gerhard Mayer; Oliver Kohlbacher; Jennifer Siepen; Simon J Hubbard; Julian N Selley; Brian C Searle; James Shofstahl; Sean L Seymour; Randall Julian; Pierre-Alain Binz; Eric W Deutsch; Henning Hermjakob; Florian Reisinger; Johannes Griss; Juan Antonio Vizcaíno; Matthew Chambers; Angel Pizarro; David Creasy
Journal:  Mol Cell Proteomics       Date:  2012-02-27       Impact factor: 5.911

7.  On Credibility, Clarity, and Compliance.

Authors:  Al Burlingame; Steven A Carr; Ralph A Bradshaw; Robert J Chalkley
Journal:  Mol Cell Proteomics       Date:  2015-06-03       Impact factor: 5.911

8.  Using proteomics to advance the search for potential biomarkers for preeclampsia: A systematic review and meta-analysis.

Authors:  Thy Pham Hoai Nguyen; Cameron James Patrick; Laura Jean Parry; Mary Familari
Journal:  PLoS One       Date:  2019-04-05       Impact factor: 3.240

9.  Reproducibility and Transparency by Design.

Authors:  Vladislav A Petyuk; Laurent Gatto; Samuel H Payne
Journal:  Mol Cell Proteomics       Date:  2019-07-04       Impact factor: 5.911

10.  iProX: an integrated proteome resource.

Authors:  Jie Ma; Tao Chen; Songfeng Wu; Chunyuan Yang; Mingze Bai; Kunxian Shu; Kenli Li; Guoqing Zhang; Zhong Jin; Fuchu He; Henning Hermjakob; Yunping Zhu
Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

  10 in total

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