ArticleInternational journal of molecular sciences2022
Software Tool for Visualization and Validation of Protein Turnover Rates Using Heavy Water Metabolic Labeling and LC-MS.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Computational Model for Determining Labeling Duration in Protein Turnover Studies Using a Single Deuterated Water Labeled Sample.Journal of the American Society for Mass Spectrometry · 2026Article
- Leveraging machine learning models to forecast pediatric allergic rhinitis exacerbation risk based on environmental exposure data.Frontiers in pediatrics · 2026Article
- Turnover Rates and Numbers of Exchangeable Hydrogens in Deuterated Water Labeled Samples.International journal of molecular sciences · 2025Article
- Numbers of Exchangeable Hydrogens from LC-MS Data of Heavy Water Metabolically Labeled Samples.Journal of the American Society for Mass Spectrometry · 2024Article
- Mass Spectrometric Proteomics 2.0.International journal of molecular sciences · 2024Article
- Flexible Quality Control for Protein Turnover Rates Using d2ome.International journal of molecular sciences · 2023Article
- A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling.Scientific data · 2023Article
- Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling.Journal of proteome research · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
Metabolic stable isotope labeling followed by liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful tool for in vivo protein turnover studies of individual proteins on a large scale and with high throughput. Turnover rates of thousands of proteins from dozens of time course experiments are determined by data processing tools, which are essential components of the workflows for automated extraction of turnover rates. The development of sophisticated algorithms for estimating protein turnover has been emphasized. However, the visualization and annotation of the time series data are no less important. The visualization tools help to validate the quality of the model fits, their goodness-of-fit characteristics, mass spectral features of peptides, and consistency of peptide identifications, among others. Here, we describe a graphical user interface (GUI) to visualize the results from the protein turnover analysis tool, d2ome, which determines protein turnover rates from metabolic D
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