Metabolite identification via the Madison Metabolomics Consortium Database

By Q. Cui, I. Lewis, Adrian D. Hegeman, Mark E. Anderson, Jing Li, Christopher F. Schulte, William M. Westler, Hamid R. Eghbalnia, M. Sussman, John L. Markley. Nature Biotechnology, 2008

608 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a36a931a01c5165dda1b069bad943b7590b31c9b

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In Biomedical Engineering

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Citations in the corpus

• Metabolomics in pesticide research and development: review and future perspectives (2011, neutral): “…al. (2007) LIPID MAPS http://www.lipidmaps.org/index.html Fahy et al. (2007) Madison Metabolomics Consortium Database http://mmcd.nmrfam.wisc.edu/ Cui et al. (2008) MassBank http://www.massbank.jp/en/database.html#WEB-API Horai et al. (2008) Matador http://matador.embl.de Günther et al.…”
• xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data (2013, neutral): “edu/) [22] and Metlin Metabolite Database (http://metlin.”
• The Human Serum Metabolome (2011, background): “With the release of the Human Metabolome Database (HMDB) [4,5] and other related compound or spectral resources such as KEGG [6], LipidMaps [7], PubChem [8], ChEBI [9], MMCD [10], Metlin [11] and MassBank [12], we believe the field has taken an important step towards making metabolomics studies much”
• Predicting Network Activity from High Throughput Metabolomics (2013, background): “For example, a number of metabolites in metabolomics databases [36,65,66] are not in any of these metabolic models.”
• HMDB: a knowledgebase for the human metabolome (2008, methodology): “Spectral databases for metabolomics include the BMRB (11), MMCD (12), MassBank (13), the Golm Metabolome database (14) and Metlin (15).”
• Multivariate Analysis in Metabolomics. (2012, background): “Similarly, the lack of reference NMR or MS spectra for all known metabolites makes proper identification of metabolites challenging [20-22].”
• RIKEN tandem mass spectral database (ReSpect) for phytochemicals: a plant-specific MS/MS-based data resource and database. (2012, methodology): “, 2009, 2007), MMCD (Cui et al., 2008), and MassBank (Horai et al.”
• Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst (2011, background): “With the availability of several comprehensive metabolomic databases and improved spectral analysis tool”
• Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016, methodology): “…modules need to be matched to reference databases such as the human metabolome database (HMDB) (Wishart et al., 2009), KEGG (Kanehisa & Goto, 2000), the Madison Metabolomics Consortium Database (MMCD) (Cui et al., 2008), Metlin (C. A. Smith et al., 2005), and chemical databases (Baker, 2011).”
• Computational mass spectrometry for small molecules (2013, methodology): “The MMCD [1] is a hub for NMR and MS spectral data containing about 2 000 mass spectra from the literature collected under defined conditions.”
• The Future of NMR-Based Metabolomics (2016, background): “By setting tolerances for the matching of (1)H and (13)C signals, one can maximize compound identification while minimizing false positives [11].”