Information quality in proteomics

Proteomics, the study of the protein complement of a biologicalsystem, is generating increasing quantities of data from rapidlydeveloping technologies employed in a variety of different experimentalworkflows. Experimental processes, e.g. for comparative 2D gelstudies or LC-MS/MS analyses of complex protein mixtures, involvea number of steps: from experimental design, through wet anddry lab operations, to publication of data in repositories andfinally to data annotation and maintenance. The presence ofinaccuracies throughout the processing pipeline, however, resultsin data that can be untrustworthy, thus offsetting the benefitsof high-throughput technology. While researchers and practitionersare generally aware of some of the information quality issuesassociated with public proteomics data, there are few acceptedcriteria and guidelines for dealing with them. In this article,we highlight factors that impact on the quality of experimentaldata and review current approaches to information quality managementin proteomics. Data quality issues are considered throughoutthe lifecycle of a proteomics experiment, from experiment designand technique selection, through data analysis, to archivingand sharing.