A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling.
Henock M DebernehDoaa R AbdelrahmanSunil K VermaJennifer J LinaresAndrew J MurtonWilliam K RussellMuge N Kuyumcu-MartinezBenjamin F MillerRovshan G SadygovPublished in: Scientific data (2023)
Metabolic stable isotope labeling with heavy water followed by liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful tool for in vivo protein turnover studies. Several algorithms and tools have been developed to determine the turnover rates of peptides and proteins from time-course stable isotope labeling experiments. The availability of benchmark mass spectrometry data is crucial to compare and validate the effectiveness of newly developed techniques and algorithms. In this work, we report a heavy water-labeled LC-MS dataset from the murine liver for protein turnover rate analysis. The dataset contains eighteen mass spectral data with their corresponding database search results from nine different labeling durations and quantification outputs from d2ome+ software. The dataset also contains eight mass spectral data from two-dimensional fractionation experiments on unlabeled samples.
Keyphrases
- mass spectrometry
- liquid chromatography
- electronic health record
- machine learning
- bone mineral density
- big data
- high resolution mass spectrometry
- tandem mass spectrometry
- optical coherence tomography
- randomized controlled trial
- amino acid
- data analysis
- high resolution
- high performance liquid chromatography
- systematic review
- deep learning
- capillary electrophoresis
- gas chromatography
- protein protein
- binding protein
- computed tomography
- magnetic resonance
- postmenopausal women
- solid phase extraction