PROTEINCHALLENGE: Crowd sourcing in proteomics analysis and software development

摘要

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. Compared to other fields such as transcriptomics, computational biology, genomics and imaging, bioinformatics activities in mass spectrometry-based proteomics have been rather vendor specific, closed sourced and not involving large parts of the community. This is in stark contrast to highly successful academic open source software solutions in other fields, such as Cytoscape.org, Openmicroscopy.org, Ensembl.org and BLAST. Academic software development efforts in proteomics have tended to be based within large individual labs, and only a relatively few have been shared openly and more widely.