Literature DB >> 17031794

The HUPO proteomics standards initiative--overcoming the fragmentation of proteomics data.

Henning Hermjakob1.   

Abstract

Proteomics is a key field of modern biomolecular research, with many small and large scale efforts producing a wealth of proteomics data. However, the vast majority of this data is never exploited to its full potential. Even in publicly funded projects, often the raw data generated in a specific context is analysed, conclusions are drawn and published, but little attention is paid to systematic documentation, archiving, and public access to the data supporting the scientific results. It is often difficult to validate the results stated in a particular publication, and even simple global questions like "In which cellular contexts has my protein of interest been observed?" can currently not be answered with realistic effort, due to a lack of standardised reporting and collection of proteomics data. The Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organisation (HUPO), defines community standards for data representation in proteomics to facilitate systematic data capture, comparison, exchange and verification. In this article we provide an overview of PSI organisational structure, activities, and current results, as well as ways to get involved in the broad-based, open PSI process.

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Year:  2006        PMID: 17031794     DOI: 10.1002/pmic.200600537

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  14 in total

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Review 3.  Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites.

Authors:  Brett C Covington; John A McLean; Brian O Bachmann
Journal:  Nat Prod Rep       Date:  2017-01-04       Impact factor: 13.423

Review 4.  Shotgun MS proteomic analysis of bronchoalveolar lavage fluid in normal subjects.

Authors:  Elizabeth V Nguyen; Sina A Gharib; Lynn M Schnapp; David R Goodlett
Journal:  Proteomics Clin Appl       Date:  2014-10       Impact factor: 3.494

5.  The Ontology Lookup Service: bigger and better.

Authors:  Richard Côté; Florian Reisinger; Lennart Martens; Harald Barsnes; Juan Antonio Vizcaino; Henning Hermjakob
Journal:  Nucleic Acids Res       Date:  2010-05-11       Impact factor: 16.971

6.  Statistical analysis of variation in the human plasma proteome.

Authors:  Todd H Corzett; Imola K Fodor; Megan W Choi; Vicki L Walsworth; Kenneth W Turteltaub; Sandra L McCutchen-Maloney; Brett A Chromy
Journal:  J Biomed Biotechnol       Date:  2010-01-14

7.  A community standard format for the representation of protein affinity reagents.

Authors:  David E Gloriam; Sandra Orchard; Daniela Bertinetti; Erik Björling; Erik Bongcam-Rudloff; Carl A K Borrebaeck; Julie Bourbeillon; Andrew R M Bradbury; Antoine de Daruvar; Stefan Dübel; Ronald Frank; Toby J Gibson; Larry Gold; Niall Haslam; Friedrich W Herberg; Tara Hiltke; Jörg D Hoheisel; Samuel Kerrien; Manfred Koegl; Zoltán Konthur; Bernhard Korn; Ulf Landegren; Luisa Montecchi-Palazzi; Sandrine Palcy; Henry Rodriguez; Sonja Schweinsberg; Volker Sievert; Oda Stoevesandt; Michael J Taussig; Marius Ueffing; Mathias Uhlén; Silvère van der Maarel; Christer Wingren; Peter Woollard; David J Sherman; Henning Hermjakob
Journal:  Mol Cell Proteomics       Date:  2009-08-11       Impact factor: 5.911

8.  Survey-based naming conventions for use in OBO Foundry ontology development.

Authors:  Daniel Schober; Barry Smith; Suzanna E Lewis; Waclaw Kusnierczyk; Jane Lomax; Chris Mungall; Chris F Taylor; Philippe Rocca-Serra; Susanna-Assunta Sansone
Journal:  BMC Bioinformatics       Date:  2009-04-27       Impact factor: 3.169

9.  Systems integration of biodefense omics data for analysis of pathogen-host interactions and identification of potential targets.

Authors:  Peter B McGarvey; Hongzhan Huang; Raja Mazumder; Jian Zhang; Yongxing Chen; Chengdong Zhang; Stephen Cammer; Rebecca Will; Margie Odle; Bruno Sobral; Margaret Moore; Cathy H Wu
Journal:  PLoS One       Date:  2009-09-25       Impact factor: 3.240

10.  The EIPeptiDi tool: enhancing peptide discovery in ICAT-based LC MS/MS experiments.

Authors:  Mario Cannataro; Giovanni Cuda; Marco Gaspari; Sergio Greco; Giuseppe Tradigo; Pierangelo Veltri
Journal:  BMC Bioinformatics       Date:  2007-07-15       Impact factor: 3.169

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