QCMAP: An Interactive Web‐Tool for Performance Diagnosis and Prediction of LC‐MS Systems. Issue 13 (13th June 2019)

• Record Type: Journal Article
• Authors: Kim, Taiyun; Chen, Irene Rui; Parker, Benjamin L.; Humphrey, Sean J.; Crossett, Ben; Cordwell, Stuart J.; Yang, Pengyi; Yang, Jean Yee Hwa
• Is Part Of: Proteomics. Volume 19:Issue 13(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-06-13
• Subjects: interactive web‐tools -- liquid chromatography‐mass spectrometry (LC‐MS) -- performance diagnosis -- prediction -- proteomics; Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201900068
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract: The increasing role played by liquid chromatography‐mass spectrometry (LC‐MS)‐based proteomics in biological discovery has led to a growing need for quality control (QC) on the LC‐MS systems. While numerous quality control tools have been developed to track the performance of LC‐MS systems based on a pre‐defined set of performance factors (e.g., mass error, retention time), the precise influence and contribution of the performance factors and their generalization property to different biological samples are not as well characterized. Here, a web‐based application (QCMAP) is developed for interactive diagnosis and prediction of the performance of LC‐MS systems across different biological sample types. Leveraging on a standardized HeLa cell sample run as QC within a multi‐user facility, predictive models are trained on a panel of commonly used performance factors to pinpoint the precise conditions to a (un)satisfactory performance in three LC‐MS systems. It is demonstrated that the learned model can be applied to predict LC‐MS system performance for brain samples generated from an independent study. By compiling these predictive models into our web‐application, QCMAP allows users to benchmark the performance of their LC‐MS systems using their own samples and identify key factors for instrument optimization. QCMAP is freely available from:http://shiny.maths.usyd.edu.au/QCMAP/ .