Proteomics, the study and analysis of the protein complement of an organelle, cell or tissue, plays an important role in elucidating the way in which living systems behave in health and disease. Built firmly on the breakthroughs achieved by the whole-genome sequencing efforts in the life sciences, proteomics shifts the focus from the genome of a living system to the proteins it encodes. Since these proteins are the main effector molecules in living cells, the cellular proteome needs to be dynamic over time in order for the cell to adapt to its ever-changing circumstances. Additionally, as individual cells in multicellular organisms take on specific roles, their protein complement will correspondingly be altered to reflect this specialization. Understanding cellular proteomes and their changes through time and differentiation stages will therefore allow us to obtain a much firmer grasp on the underlying biology. Furthermore, by studying changes that occur in cells or tissues as they transition from a healthy into a diseased state, we can obtain markers that signal the onset of disease as well as the stage of disease progression. Ultimately, we will gain invaluable insights towards finding novel drug targets that help us combat these diseases, or even prevent their occurrence.
It is clear that such spatio-temporal studies of the proteome require more information than is provided by examining protein composition. Indeed, the relative or absolute amounts of the identified proteins must be measured as well. Correspondingly, many methodologies for performing such quantitative proteomics have been proposed, and these different techniques each bring distinct advantages and drawbacks. As these methods are fairly young, the analysis of the resulting data is still in its infancy and frequently constitutes a bottleneck. We therefore here propose to examine the data generated by a variety of quantitative methods, develop appropriate data processing algorithms for the data, and provide evaluation criteria that will guide the interpretation of the results. Finally, in order to archive the information obtained from such analyses, we will develop the means to store these various data, and will update the already established PRIDE proteomics data repository (hosted by EMBL-EBI) to enable the global dissemination of the data to the scientific community.