Abstract
The increasing number of biomedical and translational applications in mass spectrometry-based proteomics poses new analytical challenges and raises the need for automated quality control systems. Despite previous efforts to set standard file formats, data processing workflows and key evaluation parameters for quality control, automated quality control systems are not yet widespread among proteomics laboratories, which limits the acquisition of high-quality results, inter-laboratory comparisons and the assessment of variability of instrumental platforms. Here we present QCloud, a cloud-based system to support proteomics laboratories in daily quality assessment using a user-friendly interface, easy setup, automated data processing and archiving, and unbiased instrument evaluation. QCloud supports the most common targeted and untargeted proteomics workflows, it accepts data formats from different vendors and it enables the annotation of acquired data and reporting incidences. A complete version of the QCloud system has successfully been developed and it is now open to the proteomics community (http://qcloud.crg.eu). QCloud system is an open source project, publicly available under a Creative Commons License Attribution-ShareAlike 4.0.
Highlights
Proteomics technologies have matured into a panoply of reliable methods for measuring with high sensitivity thousands of peptides in multiple biological samples
QCloud is a cloud-based quality control system that establishes a seamless quality control pipeline and, eliminates those barriers that usually prevent the adoption of quality control tools as an integral part of mass spectrometry proteomics workflows
The QCloud system consists in i) a thin client installed in the mass spectrometer acquisition computer, ii) a cloud-based processing infrastructure, and iii) a web user interface (Fig 1) that automate the complete quality control workflow by performing automatic data collection from the instrument, data processing, unbiased instrument evaluation, metrics display, and data self-archiving
Summary
Proteomics technologies have matured into a panoply of reliable methods for measuring with high sensitivity thousands of peptides in multiple biological samples. Persistent methodological developments have enabled new large-scope applications for clinical and translational proteomics research [1,2,3] in which hundreds of samples are prepared and analysed by mass spectrometry. These applications have increased the analytical challenges of proteomics experiments, and generated the need to implement systems to control and assure the quality of each of the steps involved in the proteomics workflow, including sample preparation, chromatographic peptide separation, mass spectrometric acquisition, and data analysis [4,5,6,7]. “Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya” (2014SGR678), www.gencat.cat
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