The Future of Large-Scale Collaborative Proteomics

The postgenomics era has witnessed a rapid change in biological methods for knowledge elucidation and pharmacological approaches to biomarker discovery. Differential expression of proteins in health and disease holds the key to early diagnosis and accelerated drug discovery. This approach, however, has also brought an explosion of data complexity not mirrored by existing progress in proteome informatics. It has become apparent that the task is greater than that can be tackled by individual laboratories alone and large-scale open collaborations of the new human proteome organization (HUPO) have highlighted major challenges concerning the integration and cross-validation of results across different laboratories. This paper describes the state-of-the-art proteomics workflows (two-dimensional gel electrophoresis, liquid chromatography, and mass spectrometry) and their utilization by the participants of the HUPO initiatives towards comprehensive mapping of the brain, liver, and plasma proteomes. Particular emphasis is given to the limitations of the underlying data analysis techniques for large-scale collaborative proteomics. Emerging paradigms including statistical data normalization, direct image registration, spectral libraries, and high-throughput computation with Web-based bioinformatics services are discussed. It is envisaged that these methods will provide the basis for breaking the bottleneck of large-scale automated proteome mapping and biomarker discovery.