ArticlePloS one2014
ALLocator: an interactive web platform for the analysis of metabolomic LC-ESI-MS datasets, enabling semi-automated, user-revised compound annotation and mass isotopomer ratio analysis.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 28 citations in OpenAlex.
- Recent advances in proteomics and metabolomics in plants.Molecular horticulture · 2022Review
- CPExtract, a Software Tool for the Automated Tracer-Based Pathway Specific Screening of Secondary Metabolites in LC-HRMS Data.Analytical chemistry · 2022Article
- Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput.Analytical chemistry · 2021Review
- Ultrahigh-Resolution Mass Spectrometry-Based Platform for Plasma Metabolomics Applied to Type 2 Diabetes Research.Journal of proteome research · 2021Article
- The ABRF Metabolomics Research Group 2016 Exploratory Study: Investigation of Data Analysis Methods for Untargeted Metabolomics.Metabolites · 2020Article
- Peak Annotation and Verification Engine for Untargeted LC-MS Metabolomics.Analytical chemistry · 2019Article
- In Vivo Stable-Isotope Labeling and Mass-Spectrometry-Based Metabolic Profiling of a Potent Tobacco-Specific Carcinogen in Rats.Analytical chemistry · 2018Article
- Systems-Level Annotation of a Metabolomics Data Set Reduces 25 000 Features to Fewer than 1000 Unique Metabolites.Analytical chemistry · 2017Article
- MetExtract II: A Software Suite for Stable Isotope-Assisted Untargeted Metabolomics.Analytical chemistry · 2017Article
- From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics.GigaScience · 2017Review
- Mass Spectral Feature List Optimizer (MS-FLO): A Tool To Minimize False Positive Peak Reports in Untargeted Liquid Chromatography-Mass Spectroscopy (LC-MS) Data Processing.Analytical chemistry · 2017Article
- Metabolomics for Plant Improvement: Status and Prospects.Frontiers in plant science · 2017Review
- Stable Isotope-Assisted Evaluation of Different Extraction Solvents for Untargeted Metabolomics of Plants.International journal of molecular sciences · 2016Article
- Tracing the metabolism of HT-2 toxin and T-2 toxin in barley by isotope-assisted untargeted screening and quantitative LC-HRMS analysis.Analytical and bioanalytical chemistry · 2015Article
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8 authors at 2 institutions in 1 country.
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Abstract
Adduct formation, fragmentation events and matrix effects impose special challenges to the identification and quantitation of metabolites in LC-ESI-MS datasets. An important step in compound identification is the deconvolution of mass signals. During this processing step, peaks representing adducts, fragments, and isotopologues of the same analyte are allocated to a distinct group, in order to separate peaks from coeluting compounds. From these peak groups, neutral masses and pseudo spectra are derived and used for metabolite identification via mass decomposition and database matching. Quantitation of metabolites is hampered by matrix effects and nonlinear responses in LC-ESI-MS measurements. A common approach to correct for these effects is the addition of a U-13C-labeled internal standard and the calculation of mass isotopomer ratios for each metabolite. Here we present a new web-platform for the analysis of LC-ESI-MS experiments. ALLocator covers the workflow from raw data processing to metabolite identification and mass isotopomer ratio analysis. The integrated processing pipeline for spectra deconvolution "ALLocatorSD" generates pseudo spectra and automatically identifies peaks emerging from the U-13C-labeled internal standard. Information from the latter improves mass decomposition and annotation of neutral losses. ALLocator provides an interactive and dynamic interface to explore and enhance the results in depth. Pseudo spectra of identified metabolites can be stored in user- and method-specific reference lists that can be applied on succeeding datasets. The potential of the software is exemplified in an experiment, in which abundance fold-changes of metabolites of the l-arginine biosynthesis in C. glutamicum type strain ATCC 13032 and l-arginine producing strain ATCC 21831 are compared. Furthermore, the capability for detection and annotation of uncommon large neutral losses is shown by the identification of (γ-)glutamyl dipeptides in the same strains. ALLocator is available online at: https://allocator.cebitec.uni-bielefeld.de. A login is required, but freely available.
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