Martin, Sarah F.8; Falkenberg, Heiner9; Dyrlund, Thomas Franck5; Khoudoli, Guennadi A.10; Mageean, Craig J.11; Linding, Rune1
1 Department of Systems Biology, Technical University of Denmark2 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark3 University of Edinburgh4 Heinrich-Heine-University Dusseldorf5 Aarhus University6 University of Dundee7 University of Liverpool8 University of Edinburgh9 Heinrich-Heine-University Dusseldorf10 University of Dundee11 University of Liverpool
crowd sourcing in proteomics analysis and software development
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].
Journal of Proteomics, 2013, Vol 88, p. 41-46
Computational Biology; Proteomics; Software Design; Crowd sourcing; Community challenge; Data analysis; Software; Benchmarking;; Benchmarking; Open source