A systematic approach to modeling, capturing, and disseminating proteomics experimental data

Chris F. Taylor, Norman W. Paton, Kevin L. Garwood, Paul D. Kirby, David A. Stead, Zhikang Yin, Eric W. Deutsch, Laura Selway, Janet Walker, Isabel Riba-Garcia, Shabaz Mohammed, Michael J. Deery, Julie A. Howard, Tom Dunkley, Ruedi Aebersold, Douglas B. Kell, Kathryn S. Lilley, Peter Roepstorff, John R. Yates, Andy BrassAlistair J.P. Brown, Phil Cash, Simon J. Gaskell, Simon J. Hubbard, Stephen G. Oliver

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229 Citations (Scopus)

Résumé

Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository.

langue originaleAnglais
Pages (de - à)247-254
Nombre de pages8
journalNature Biotechnology
Volume21
Numéro de publication3
Les DOIs
étatPublié - 1 mars 2003
Modification externeOui

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