In large-scale proteomics studies there is a temptation, after months of experimental work, to plug resulting data into a convenient-if poorly implemented-set of tools, which may neither do the data justice nor help answer the scientific question. In this paper we have captured key concerns, including arguments for community-wide open source software development and "big data" compatible solutions for the future. For the meantime, we have laid out ten top tips for data processing. With these at hand, a first large-scale proteomics analysis hopefully becomes less daunting to navigate. However there is clearly a real need for robust tools, standard operating procedures and general acceptance of best practises. Thus we submit to the proteomics community a call for a community-wide open set of proteomics analysis challenges-PROTEINCHALLENGE-that directly target and compare data analysis workflows, with the aim of setting a community-driven gold standard for data handling, reporting and sharing. This article is part of a Special Issue entitled: New Horizons and Applications for Proteomics [EuPA 2012].
Martin, S. F., Falkenberg, H., Dyrlund, T. F., Khoudoli, G. A., Mageean, C. J., & Linding, R. (2013, November 12). PROTEINCHALLENGE: Crowd sourcing in proteomics analysis and software development. Journal of Proteomics. Elsevier. https://doi.org/10.1016/j.jprot.2012.11.014