Quality Control Analysis in Real-time (QC-ART): A Tool for Real-time Quality Control Assessment of Mass Spectrometry-based Proteomics Data
| Autor: | Stanfill, B.A., Nakayasu, E.S., Bramer, L.M., Thompson, A.M., Ansong, C.K., Clauss, T.R., Gritsenko, M.A., Monroe, M.E., Moore, R.J., Orton, D.J., Piehowski, P.D., Schepmoes, A.A., Smith, R.D., Webb-Robertson, B.M., Metz, T.O., TEDDY Study Group (Ziegler, A.-G., Beyerlein, A., Hummel, M., Hummel, S., Knopff, A., Roth, R., Scholz, M., Stock, J., Warncke, K., Wendel, L., Winkler, C.) |
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| Přispěvatelé: | Children's Hospital, Clinicum, HUS Children and Adolescents |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Proteomics Quality Control QUANTITATION Computer science media_common.quotation_subject Sample (statistics) SOFTWARE computer.software_genre Biochemistry Analytical Chemistry Cohort Studies 03 medical and health sciences User-Computer Interface Computer Systems Tandem Mass Spectrometry Humans Quality (business) Instrumentation (computer programming) LC-MS/MS Databases Protein Molecular Biology media_common IDENTIFICATION Flagging Technological Innovation and Resources BIOLOGICAL CONSEQUENCES PERFORMANCE METRICS Data structure Identification (information) 030104 developmental biology ROC Curve Data quality Isotope Labeling Outlier 1182 Biochemistry cell and molecular biology Data mining 3111 Biomedicine Peptides computer Oxidation-Reduction Algorithms |
| Zdroj: | Mol. Cell. Proteomics 17, 1824-1836 (2018) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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