Information quality in proteomics

@article{citeulike:2614130, abstract = {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. 10.1093/bib/bbn004}, author = {Stead, David A. and Paton, Norman W. and Missier, Paolo and Embury, Suzanne M. and Hedeler, Cornelia and Jin, Binling and Brown, Alistair J. and Preece, Alun }, citeulike-article-id = {2614130}, doi = {http://dx.doi.org/10.1093/bib/bbn004}, journal = {Brief Bioinform}, keywords = {bioinformatics, pipeline, proteome, review}, month = {March}, number = {2}, pages = {174--188}, posted-at = {2008-04-29 19:53:13}, priority = {5}, title = {Information quality in proteomics}, url = {http://dx.doi.org/10.1093/bib/bbn004}, volume = {9}, year = {2008} }

TY - JOUR ID - citeulike:2614130 L3 - citeulike-article-id:2614130 TI - Information quality in proteomics JF - Brief Bioinform VL - 9 IS - 2 SP - 174 EP - 188 N2 - 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. 10.1093/bib/bbn004 KW - bioinformatics KW - pipeline KW - proteome KW - review AU - Stead, David AU - Paton, Norman AU - Missier, Paolo AU - Embury, Suzanne AU - Hedeler, Cornelia AU - Jin, Binling AU - Brown, Alistair AU - Preece, Alun PY - 2008/03/01/ UR - http://dx.doi.org/10.1093/bib/bbn004 ER -