Dataset Information

JUMPm: A Tool for Large-Scale Identification of Metabolites in Untargeted Metabolomics.

ABSTRACT: Metabolomics is increasingly important for biomedical research, but large-scale metabolite identification in untargeted metabolomics is still challenging. Here, we present Jumbo Mass spectrometry-based Program of Metabolomics (JUMPm) software, a streamlined software tool for identifying potential metabolite formulas and structures in mass spectrometry. During database search, the false discovery rate is evaluated by a target-decoy strategy, where the decoys are produced by breaking the octet rule of chemistry. We illustrated the utility of JUMPm by detecting metabolite formulas and structures from liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) analyses of unlabeled and stable-isotope labeled yeast samples. We also benchmarked the performance of JUMPm by analyzing a mixed sample from a commercially available metabolite library in both hydrophilic and hydrophobic LC-MS/MS. These analyses confirm that metabolite identification can be significantly improved by estimating the element composition in formulas using stable isotope labeling, or by introducing LC retention time during a spectral library search, which are incorporated into JUMPm functions. Finally, we compared the performance of JUMPm and two commonly used programs, Compound Discoverer 3.1 and MZmine 2, with respect to putative metabolite identifications. Our results indicate that JUMPm is an effective tool for metabolite identification of both unlabeled and labeled data in untargeted metabolomics.

SUBMITTER: Wang X

PROVIDER: S-EPMC7281133 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

JUMPm: A Tool for Large-Scale Identification of Metabolites in Untargeted Metabolomics.

Wang Xusheng X Cho Ji-Hoon JH Poudel Suresh S Li Yuxin Y Jones Drew R DR Shaw Timothy I TI Tan Haiyan H Xie Boer B Peng Junmin J

Metabolites 20200512 5

Metabolomics is increasingly important for biomedical research, but large-scale metabolite identification in untargeted metabolomics is still challenging. Here, we present Jumbo Mass spectrometry-based Program of Metabolomics (JUMPm) software, a streamlined software tool for identifying potential metabolite formulas and structures in mass spectrometry. During database search, the false discovery rate is evaluated by a target-decoy strategy, where the decoys are produced by breaking the octet rul ...[more]

PMID: 32408578

Similar Datasets

Project description:Physiological status and pathological changes in an individual can be captured by metabolic state that reflects the influence of both genetic variants and environmental factors such as diet, lifestyle and gut microbiome. The totality of environmental exposure throughout lifetime - i.e., exposome - is difficult to measure with current technologies. However, targeted measurement of exogenous chemicals and untargeted profiling of endogenous metabolites have been widely used to discover biomarkers of pathophysiologic changes and to understand functional impacts of genetic variants. To investigate the coverage of chemical space and interindividual variation related to demographic and pathological conditions, we profiled 169 plasma samples using an untargeted metabolomics platform. On average, 1,009 metabolites were quantified in each individual (range 906 - 1,038) out of 1,244 total chemical compounds detected in our cohort. Of note, age was positively correlated with the total number of detected metabolites in both males and females. Using the robust Qn estimator, we found metabolite outliers in each sample (mean 22, range from 7 to 86). A total of 50 metabolites were outliers in a patient with phenylketonuria including the ones known for phenylalanine pathway suggesting multiple metabolic pathways perturbed in this patient. The largest number of outliers (N=86) was found in a 5-year-old boy with alpha-1-antitrypsin deficiency who were waiting for liver transplantation due to cirrhosis. Xenobiotics including drugs, diets and environmental chemicals were significantly correlated with diverse endogenous metabolites and the use of antibiotics significantly changed gut microbial products detected in host circulation. Several challenges such as annotation of features, reference range and variance for each feature per age group and gender, and population scale reference datasets need to be addressed; however, untargeted metabolomics could be immediately deployed as a biomarker discovery platform and to evaluate the impact of genomic variants and exposures on metabolic pathways for some diseases.

Dataset Information

JUMPm: A Tool for Large-Scale Identification of Metabolites in Untargeted Metabolomics.

Publications

JUMPm: A Tool for Large-Scale Identification of Metabolites in Untargeted Metabolomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets