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.2018 Jan 11;13(1):e0189209.
doi: 10.1371/journal.pone.0189209. eCollection 2018.

QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories

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QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories

Cristina Chiva et al. PLoS One..

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.

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Conflict of interest statement

Competing Interests:The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. QCloud overview.
Overview of the QCloud system structure consisting in i) a thin client in the mass spectrometer acquisition computer, ii) the cloud-based processing infrastructure, and iii) the web user interface.
Fig 2
Fig 2. Detailed scheme of the cloud-based processing infrastructure pipeline.
Fig 3
Fig 3. Table scheme and relationship of the persistent layer.
Fig 4
Fig 4. Web server front-end.
A) Schematic architecture of the server back and front-end. B) Example of quality control data point annotations with controlled vocabulary in the QCloud system in a profile plot of log2(Area) of multiple selected peptides (see Table 2).
Fig 5
Fig 5. Quality control charts.
A sample of several quality control charts displayed in the web interface by the QCloud system, including peptide areas, injection time, total numbers of proteins, peptides and PSM, chromatographic resolution, peak capacity, and retention time drift. Plotted parameters are defined in Table 1.
Fig 6
Fig 6. Troubleshooting with QCloud.
A) Example of slight mass calibration problems identified using QC1 controls after an instrument maintenance procedure; B) Sudden losses of performance classified as non-conformities QC1 samples (red dots) that triggered maintenance interventions annotated as vertical lines; C) Example of sample carry over detected in the TIC plot of one QC1 sample acquired after a problematic sample; D) Increase in the total number of PSMs, peptides, and proteins from a QC2 quality control sample after a cleaning routine. All plots correspond to quality control data generated in an Orbitrap Fusion Lumos.
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Grants and funding

This work was supported by: 1. “Plataforma de Recursos Biomoleculares y Bioinformáticos (ProteoRed)” supported by grant PT13/0001 of Instituto de Salud Carlos III from the Spanish Government,http://www.isciii.es/. 2. Spanish Ministry of Economy and Competitiveness, “Centro de Excelencia Severo Ochoa 2013-2017”, SEV-2012-0208,http://www.mineco.gob.es/. 3. “Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya” (2014SGR678),www.gencat.cat.

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