Skip Navigation


Briefings in Bioinformatics Advance Access originally published online on February 15, 2008
Briefings in Bioinformatics 2008 9(2):174-188; doi:10.1093/bib/bbn004
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
9/2/174    most recent
bbn004v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Stead, D. A.
Right arrow Articles by Preece, A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Stead, D. A.
Right arrow Articles by Preece, A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2008. Published by Oxford University Press. For Permissions, please email: journals.permissions@oxfordjournals.org

Software Review

Information quality in proteomics

David A. Stead, Norman W. Paton, Paolo Missier, Suzanne M. Embury, Cornelia Hedeler, Binling Jin, Alistair J. P. Brown and Alun Preece

Corresponding author. David A. Stead, School of Medical Sciences, University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen, AB25 2ZD, UK. Tel: +44 (0) 1224 555804; Fax: +44 (0) 1224 555844; E-mail: d.stead{at}abdn.ac.uk

Proteomics, the study of the protein complement of a biological system, is generating increasing quantities of data from rapidly developing technologies employed in a variety of different experimental workflows. Experimental processes, e.g. for comparative 2D gel studies or LC-MS/MS analyses of complex protein mixtures, involve a number of steps: from experimental design, through wet and dry lab operations, to publication of data in repositories and finally to data annotation and maintenance. The presence of inaccuracies throughout the processing pipeline, however, results in data that can be untrustworthy, thus offsetting the benefits of high-throughput technology. While researchers and practitioners are generally aware of some of the information quality issues associated with public proteomics data, there are few accepted criteria and guidelines for dealing with them. In this article, we highlight factors that impact on the quality of experimental data and review current approaches to information quality management in proteomics. Data quality issues are considered throughout the lifecycle of a proteomics experiment, from experiment design and technique selection, through data analysis, to archiving and sharing.

Keywords: information quality, proteomics, standards, quality assessment, information management

Submitted: July 12, 2007. Received (in revised form): January 11, 2008.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.