Integrating evolving tools for proteomics research

The recent development of high-throughput proteomics techniques has resulted in the exponential growth of experimental proteomics data. At the same time, the amount of published biological information-which includes not only journal articles but also gene sequences, annotations, and biological ontologies - is also growing rapidly. To extract information from these large, diverse data sets, biologists will require powerful data management, analysis, and integration tools. More than that, biologists will need these diverse tools integrated into a computational environment that allows them to focus on the science. Without an integrated environment, biologists must attend to non-scientific tasks, for example, moving data files or altering data formats; such tasks can be a nuisance with small data sets and significant obstacles with large data sets. This paper explores an integrated computational environment for proteomics by loosely connecting individual components for data storage, retrieval, analysis, and visualization.