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J Proteome Res. 2016 Nov 4;15(11):3961-3970. Epub 2016 Aug 24.

Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 2.1.

Author information

1
Institute for Systems Biology , 401 Terry Avenure North, Seattle, Washington 98109, United States.
2
Centre for Blood Research, Departments of Oral Biological & Medical Sciences, and Biochemistry & Molecular Biology, Faculty of Dentistry, University of British Columbia , Vancouver, British Columbia V6T 1Z3, Canada.
3
Advanced Clinical Biosystems Research Institute, Department of Medicine, Cedars Sinai Medical Center , Los Angeles, California 90048, United States.
4
Department of Biomedical Sciences, Faculty of Medicine and Health Science, Macquarie University , Sydney, New South Wales 2109, Australia.
5
Yonsei Proteome Research Center and Department of Biochemistry, Yonsei University , 50 Yonsei-ro, Sudaemoon-ku, Seoul 120-749, Korea.
6
The University of Texas , Health Science Center at San Antonio, San Antonio, Texas 78229, United States.
7
SIB Swiss Institute of Bioinformatics and Department of Human Protein Science, Faculty of Medicine, University of Geneva , CMU, Michel Servet 1, 1211 Geneva 4, Switzerland.
8
Department of Medical Protein Research, VIB , Ghent 9052, Belgium.
9
Department of Biochemistry, Ghent University , Ghent B-9000, Belgium.
10
French Proteomics Infrastructure, Biosciences and Biotechnology Institute of Grenoble (BIG), Université Grenoble Alpes, CEA, INSERM , U1038 Grenoble, France.
11
Department of Chemical Biology, Northeastern University , Boston, Massachusetts 02115, United States.
12
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus , Hinxton, Cambridge CB10 1SD, United Kingdom.
13
National Center for Protein Sciences , Beijing 102206, China.
14
Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, Zurich 8093, Switzerland.
15
Faculty of Science, University of Zurich , 8006 Zurich, Switzerland.
16
Departments of Computational Medicine & Bioinformatics, Internal Medicine, and Human Genetics and School of Public Health, University of Michigan , Ann Arbor, Michigan 48109-2218, United States.

Abstract

Every data-rich community research effort requires a clear plan for ensuring the quality of the data interpretation and comparability of analyses. To address this need within the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), we have developed through broad consultation a set of mass spectrometry data interpretation guidelines that should be applied to all HPP data contributions. For submission of manuscripts reporting HPP protein identification results, the guidelines are presented as a one-page checklist containing 15 essential points followed by two pages of expanded description of each. Here we present an overview of the guidelines and provide an in-depth description of each of the 15 elements to facilitate understanding of the intentions and rationale behind the guidelines, for both authors and reviewers. Broadly, these guidelines provide specific directions regarding how HPP data are to be submitted to mass spectrometry data repositories, how error analysis should be presented, and how detection of novel proteins should be supported with additional confirmatory evidence. These guidelines, developed by the HPP community, are presented to the broader scientific community for further discussion.

KEYWORDS:

Human Proteome Project; alternative protein matches; false-discovery rates; guidelines; mass spectrometry; standards

PMID:
27490519
PMCID:
PMC5096969
DOI:
10.1021/acs.jproteome.6b00392
[Indexed for MEDLINE]
Free PMC Article

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