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MetabolomicsMetabolic reaction network-based recursive metabolite annotation for untargeted metabolomics (Aging fruit fly positive mode)

Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard...

2019-03-22 | MTBLS612 | MetaboLights
MetabolomicsMetabolic reaction network-based recursive metabolite annotation for untargeted metabolomics (Aging mouse liver positive mode)

Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard...

2019-03-26 | MTBLS601 | MetaboLights
MetabolomicsMetabolic reaction network-based recursive metabolite annotation for untargeted metabolomics (Aging mouse liver negative mode)

Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard...

2019-03-26 | MTBLS606 | MetaboLights
MetabolomicsMetabolic reaction network-based recursive metabolite annotation for untargeted metabolomics (Aging fruit fly negative mode)

Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard...

2019-03-26 | MTBLS615 | MetaboLights
ProteomicsMapping novel biomarkers of liver injury by tissue proteomic analysis
Liver damage is a dynamic process and evaluation of liver injury degree is the key step in disease diagnose. However, few common markers between different types of liver injury have been reported. Herein, we generate three liver injury mouse models including Con A induced, CCl4 injected and subjecte...
ORGANISM(S): Mus Musculus 
2020-11-25 | PXD022716 |
ProteomicsLabel-free quantitative proteomic analysis for normal and inflamed colonic mucosa from patients with ulcerative colitis
Label-free quantitative proteomic analysis for clinical tissues of 5 normal and 5 inflamed colonic mucosa. The EASY-nLC 1200 system and Q Exactive Plus were used for peptides detection. A total of 4102 proteins were identified, and 2713 proteins were quantified. According to the criterion fold chang...
ORGANISM(S): Homo Sapiens 
2025-03-12 | PXD061745 |
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