Dataset Information

QCMAP Brain proteomics data - QCMAP: An Interactive Web-Tool for Performance Diagnosis and Prediction of LC-MS Systems

ABSTRACT: we developed a web-based application (QCMAP) for interactive diagnosis and prediction of the per-formance of LC-MS systems across different biological sample types. Leveraging on a standardized HeLa sample run in Sydney MS core facility, we trained predictive models on a panel of commonly used performance factors to pinpoint the precise conditions to a (un)satisfactory performance in three LC-MS systems. Next, we 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 supply their own samples to benchmark the performance of their LC-MS systems and identify key factors for instrument opti-misation.. To demonstrate this, we obtained 10 datasets generated on a QECl instrument from mouse brain samples with different levels of quality.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Brain

SUBMITTER: Benjamin Parker

LAB HEAD: Benjamin Parker

PROVIDER: PXD010307 | Pride | 2019-05-06

REPOSITORIES: pride

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Dataset's files

Source:

			Action	DRS
	20180327_QECl_BenP_nLCII_100ngBrain_10.raw	Raw
	20180327_QECl_BenP_nLCII_100ngBrain_15.raw	Raw
	20180327_QECl_BenP_nLCII_100ngBrain_16.raw	Raw
	20180327_QECl_BenP_nLCII_100ngBrain_17.raw	Raw
	20180327_QECl_BenP_nLCII_100ngBrain_18.raw	Raw

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Publications

QCMAP: An Interactive Web-Tool for Performance Diagnosis and Prediction of LC-MS Systems.

Kim Taiyun T Chen Irene Rui IR Parker Benjamin L BL Humphrey Sean J SJ Crossett Ben B Cordwell Stuart J SJ Yang Pengyi P Yang Jean Yee Hwa JYH

Proteomics 20190613 13

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 ...[more]

PMID: 31099962

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