ArticleMolecular & cellular proteomics : MCP2018
Quality Control Analysis in Real-time (QC-ART): A Tool for Real-time Quality Control Assessment of Mass Spectrometry-based Proteomics Data.
Article in Molecular & cellular proteomics : MCP, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
29 citing papers in PubMed, 44 citations in OpenAlex.
- MSstatsQC-ML: A Supervised Machine Learning Approach to Monitor System Suitability and Quality Control in Mass Spectrometry-Based Proteomics.Journal of proteome research · 2026Article
- Automated Near Real-Time QC for LC-HRMS.Rapid communications in mass spectrometry : RCM · 2026Article
- QSample: An Automated System for Rapid Monitoring of Quality Indicators in Proteomics Samples.Journal of proteome research · 2025Article
- Standard operating procedure combined with comprehensive quality control system for multiple LC-MS platforms urinary proteomics.Nature communications · 2025Article
- PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data.Journal of the American Society for Mass Spectrometry · 2024Article
- Rapid QC-MS: Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control.Analytical chemistry · 2024Article
- A Framework for Quality Control in Quantitative Proteomics.Journal of proteome research · 2024Article
- Quality Control in the Mass Spectrometry Proteomics Core: A Practical Primer.Journal of biomolecular techniques : JBT · 2024Article
- Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry.ACS measurement science au · 2024Review
- A framework for quality control in quantitative proteomics.bioRxiv : the preprint server for biology · 2024Article
- SpectiCal:Journal of proteome research · 2024Article
- SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data.Briefings in bioinformatics · 2024Article
- New Pathophysiological Insights from Serum Proteome Profiling in Equine Atypical Myopathy.ACS omega · 2024Article
- Protein carbamylation and proteomics: from artifacts to elucidation of biological functions.Frontiers in analytical science · 2024Article
- Article
- Plasma protein biomarkers predict the development of persistent autoantibodies and type 1 diabetes 6 months prior to the onset of autoimmunity.Cell reports. Medicine · 2023Article
- Establishing Quality Control Procedures for Large-Scale Plasma Proteomics Analyses.Journal of the American Society for Mass Spectrometry · 2023Article
- Quality Control-A Stepchild in Quantitative Proteomics: A Case Study for the Human CSF Proteome.Biomolecules · 2023Article
- Review of the Use of Liquid Chromatography-Tandem Mass Spectrometry in Clinical Laboratories: Part II-Operations.Annals of laboratory medicine · 2022Review
- Listening to your mass spectrometer: An open-source toolkit to visualize mass spectrometer data.Journal of mass spectrometry and advances in the clinical lab · 2022Article
Corrections and comments
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Authors and funding
16 authors at 4 institutions in 4 countries.
Funding
Abstract
Liquid chromatography-mass spectrometry (LC-MS)-based proteomics studies of large sample cohorts can easily require from months to years to complete. Acquiring consistent, high-quality data in such large-scale studies is challenging because of normal variations in instrumentation performance over time, as well as artifacts introduced by the samples themselves, such as those because of collection, storage and processing. Existing quality control methods for proteomics data primarily focus on post-hoc analysis to remove low-quality data that would degrade downstream statistics; they are not designed to evaluate the data in near real-time, which would allow for interventions as soon as deviations in data quality are detected. In addition to flagging analyses that demonstrate outlier behavior, evaluating how the data structure changes over time can aide in understanding typical instrument performance or identify issues such as a degradation in data quality because of the need for instrument cleaning and/or re-calibration. To address this gap for proteomics, we developed Quality Control Analysis in Real-Time (QC-ART), a tool for evaluating data as they are acquired to dynamically flag potential issues with instrument performance or sample quality. QC-ART has similar accuracy as standard post-hoc analysis methods with the additional benefit of real-time analysis. We demonstrate the utility and performance of QC-ART in identifying deviations in data quality because of both instrument and sample issues in near real-time for LC-MS-based plasma proteomics analyses of a sample subset of The Environmental Determinants of Diabetes in the Young cohort. We also present a case where QC-ART facilitated the identification of oxidative modifications, which are often underappreciated in proteomic experiments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.