Dataset Information

XMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data.

ABSTRACT: Improved analytical technologies and data extraction algorithms enable detection of >10?000 reproducible signals by liquid chromatography-high-resolution mass spectrometry, creating a bottleneck in chemical identification. In principle, measurement of more than one million chemicals would be possible if algorithms were available to facilitate utilization of the raw mass spectrometry data, especially low-abundance metabolites. Here we describe an automated computational framework to annotate ions for possible chemical identity using a multistage clustering algorithm in which metabolic pathway associations are used along with intensity profiles, retention time characteristics, mass defect, and isotope/adduct patterns. The algorithm uses high-resolution mass spectrometry data for a series of samples with common properties and publicly available chemical, metabolic, and environmental databases to assign confidence levels to annotation results. Evaluation results show that the algorithm achieves an F1-measure of 0.8 for a data set with known targets and is more robust than previously reported results for cases when database size is much greater than the actual number of metabolites. MS/MS evaluation of a set of randomly selected 210 metabolites annotated using xMSannotator in an untargeted metabolomics human data set shows that 80% of features with high or medium confidence scores have ion dissociation patterns consistent with the xMSannotator annotation. The algorithm has been incorporated into an R package, xMSannotator, which includes utilities for querying local or online databases such as ChemSpider, KEGG, HMDB, T3DB, and LipidMaps.

SUBMITTER: Uppal K

PROVIDER: S-EPMC5447360 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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xMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data.

Uppal Karan K Walker Douglas I DI Jones Dean P DP

Analytical chemistry 20170104 2

Improved analytical technologies and data extraction algorithms enable detection of >10 000 reproducible signals by liquid chromatography-high-resolution mass spectrometry, creating a bottleneck in chemical identification. In principle, measurement of more than one million chemicals would be possible if algorithms were available to facilitate utilization of the raw mass spectrometry data, especially low-abundance metabolites. Here we describe an automated computational framework to annotate ions ...[more]

PMID: 27977166

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Project description:Membranous glomerulonephritis (MGN) is one of the most frequent causes of nephrotic syndrome in adults. It is characterized by the thickening of the glomerular basement membrane in the renal tissue. The current diagnosis of MGN is based on renal biopsy and the detection of antibodies to the few podocyte antigens. Due to the limitations of the current diagnostic methods, including invasiveness and the lack of sensitivity of the current biomarkers, there is a requirement to identify more applicable biomarkers. The present study aimed to identify diagnostic metabolites that are involved in the development of the disease using topological features in the component‑reaction‑enzyme‑gene (CREG) network for MGN. Significant differential metabolites in MGN compared with healthy controls were identified using proton nuclear magnetic resonance and gas chromatography‑mass spectrometry techniques, and multivariate analysis. The CREG network for MGN was constructed, and metabolites with a high centrality and a striking fold‑change in patients, compared with healthy controls, were introduced as putative diagnostic biomarkers. In addition, a protein‑protein interaction (PPI) network, which was based on proteins associated with MGN, was built and analyzed using PPI analysis methods, including molecular complex detection and ClueGene Ontology. A total of 26 metabolites were identified as hub nodes in the CREG network, 13 of which had salient centrality and fold‑changes: Dopamine, carnosine, fumarate, nicotinamide D‑ribonucleotide, adenosine monophosphate, pyridoxal, deoxyguanosine triphosphate, L‑citrulline, nicotinamide, phenylalanine, deoxyuridine, tryptamine and succinate. A total of 13 subnetworks were identified using PPI analysis. In total, two of the clusters contained seed proteins (phenylalanine‑4‑hydroxlylase and cystathionine γ‑lyase) that were associated with MGN based on the CREG network. The following biological processes associated with MGN were identified using gene ontology analysis: 'Pyrimidine‑containing compound biosynthetic process', 'purine ribonucleoside metabolic process', 'nucleoside catabolic process', 'ribonucleoside metabolic process' and 'aromatic amino acid family metabolic process'. The results of the present study may be helpful in the diagnostic and therapeutic procedures of MGN. However, validation is required in the future.

Dataset Information

XMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data.

Publications

xMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data.

Similar Datasets

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