FDR control has been a huge challenge for large-scale metabolome annotation. Although recent research indicated that the target-decoy strategy could be implemented to estimate FDR, it is hard to perform FDR control due to the difficulty of getting a reliable decoy database because of the complex fragmentation mechanism of metabolites and ubiquitous isomers. To tackle this problem, we developed a decoy generation method, which generates forged spectra from the reference target database by preserving the original reference signals to simulate the presence of isomers of metabolites. Benchmarks on GNPS data sets in Passatutto showed that the decoy database generated by our method is closer to the actual FDR than other methods, especially in the low FDR range (0-0.05). Large-scale metabolite annotation on 35 data sets showed that strict FDR reduced the number of annotated metabolites but increased the spectral efficiency, indicating the necessity of quality control. We recommended that the FDR threshold should be set to 0.01 in large-scale metabolite annotation. We implemented decoy generation, database search, and FDR control into a search engine called XY-Meta. It facilitates large-scale metabolome annotation applications.
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http://dx.doi.org/10.1021/acs.analchem.9b03355 | DOI Listing |
Curr Med Chem
January 2025
Department of Nutritional Sciences, University of Connecticut, Storrs, CT, 06269, United States.
Introduction/objective: The responsiveness to dietary interventions is influenced by complex, multifactorial interactions among genetics, diet, lifestyle, gut microbiome, environmental factors, and clinical characteristics, such as the metabolic phenotype. Detailed metabolic and microbial phenotyping using large human datasets is essential for better understanding the link between diet, the gut microbiome, and host metabolism in cardiovascular diseases (CVD). This review provides an overview of the interplay between diet, genome, metabolome, and gut microbiome in CVD.
View Article and Find Full Text PDFmSystems
January 2025
Department of Chemistry and Biochemistry, San Diego State University, San Diego, California, USA.
Infectious disease treatment success requires symptom resolution (clinical treatment success), which often but not always involves pathogen clearance. Both of these treatment goals face disease-specific and general challenges. In this review, we summarize the current state of knowledge in mechanisms of clinical and parasitological treatment failure in the context of Chagas disease, a neglected tropical disease causing cardiac and gastrointestinal symptoms.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
Department of Plant Physiology and Biochemistry, Faculty of Biology, St. Petersburg State University, Universitetskaya em., 7/9, 199034 St. Petersburg, Russia.
Plants known as obligate aerobes developed different mechanisms to overcome the damage incurred under oxygen limitation. One of the survival strategies to have commonly appeared in hydrophytic plants is the escape strategy, which accelerates plant axial organs' growth in order to escape hypoxic conditions as soon as possible. The present study aimed to distinguish the alterations in coleoptile elongation, viability and metabolic profiles in coleoptiles of slow- and fast-growing rice varieties.
View Article and Find Full Text PDFCancers (Basel)
December 2024
Institute of Radiation Emergency Medicine, Hirosaki University, Hirosaki 036-8564, Aomori, Japan.
Indoor radon is a significant risk factor for the development of LC. This study aimed to identify potential biomarkers for LC risk in high background radiation areas using a metabolomics approach (UHPLC-HRMS). Based on the indoor radon activity concentration measurements in the Kong Khaek subdistrict, serum samples were collected from 45 nonsmoker or former smoker participants, comprising 15 LC patients and 30 matched healthy controls (low- and high-radon groups, respectively).
View Article and Find Full Text PDFGenet Epidemiol
January 2025
Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts, USA.
Large-scale gene-environment interaction (GxE) discovery efforts often involve analytical compromises for the sake of data harmonization and statistical power. Refinement of exposures, covariates, outcomes, and population subsets may be helpful to establish often-elusive replication and evaluate potential clinical utility. Here, we used additional datasets, an expanded set of statistical models, and interrogation of lipoprotein metabolism via nuclear magnetic resonance (NMR)-based lipoprotein subfractions to refine a previously discovered GxE modifying the relationship between physical activity (PA) and HDL-cholesterol (HDL-C).
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