ArticleJournal of proteome research2016
An Automated Pipeline to Monitor System Performance in Liquid Chromatography-Tandem Mass Spectrometry Proteomic Experiments.
Article in Journal of proteome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed.
- MSstatsQC-ML: A Supervised Machine Learning Approach to Monitor System Suitability and Quality Control in Mass Spectrometry-Based Proteomics.Journal of proteome research · 2026Article
- QSample: An Automated System for Rapid Monitoring of Quality Indicators in Proteomics Samples.Journal of proteome research · 2025Article
- iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control.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
- A Framework for Quality Control in Quantitative Proteomics.Journal of proteome research · 2024Article
- A framework for quality control in quantitative proteomics.bioRxiv : the preprint server for biology · 2024Article
- A Scaled Proteomic Discovery Study for Prostate Cancer Diagnostic Markers Using ProteographInternational journal of molecular sciences · 2024Article
- Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers.Clinical proteomics · 2024Review
- Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics.Metabolites · 2023Article
- Establishing Quality Control Procedures for Large-Scale Plasma Proteomics Analyses.Journal of the American Society for Mass Spectrometry · 2023Article
- Discovery proteomics of human placental tissue.Rapid communications in mass spectrometry : RCM · 2023Article
- Application of Skyline software for detecting prohibited substances in doping control analysis.PloS one · 2023Article
- CovAnalytical chemistry · 2022Article
- An interactive mass spectrometry atlas of histone posttranslational modifications in T-cell acute leukemia.Scientific data · 2022Article
- Review
- Proteome-Based Serotyping of the Food-Borne PathogensMolecules (Basel, Switzerland) · 2022Article
- A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics.Scientific data · 2022Article
- Skyline Batch: An Intuitive User Interface for Batch Processing with Skyline.Journal of proteome research · 2022Article
- RawBeans: A Simple, Vendor-Independent, Raw-Data Quality-Control Tool.Journal of proteome research · 2021Article
- Methods for Proteomic Analyses of Mycobacteria.Methods in molecular biology (Clifton, N.J.) · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
We report the development of a completely automated pipeline to monitor system suitability in bottom-up proteomic experiments. LC-MS/MS runs are automatically imported into Skyline and multiple identification-free metrics are extracted from targeted peptides. These data are then uploaded to the Panorama Skyline document repository where metrics can be viewed in a web-based interface using powerful process control techniques, including Levey-Jennings and Pareto plots. The interface is versatile and takes user input, which allows the user significant control over the visualization of the data. The pipeline is vendor and instrument-type neutral, supports multiple acquisition techniques (e.g., MS 1 filtering, data-independent acquisition, parallel reaction monitoring, and selected reaction monitoring), can track performance of multiple instruments, and requires no manual intervention aside from initial setup. Data can be viewed from any computer with Internet access and a web browser, facilitating sharing of QC data between researchers. Herein, we describe the use of this pipeline, termed Panorama AutoQC, to evaluate LC-MS/MS performance in a range of scenarios including identification of suboptimal instrument performance, evaluation of ultrahigh pressure chromatography, and identification of the major sources of variation throughout years of peptide data collection.
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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.