Consolidating metabolite identifiers to enable contextual and multi-platform metabolomics

Metabolomics Research Group, RIKEN Plant Science Center, 1-7-22 Tsurumi-ku, Suehiro-cho, Yokohama, Kanagawa, 230-0045, Japan.
BMC Bioinformatics (Impact Factor: 2.58). 04/2010; 11(1):214. DOI: 10.1186/1471-2105-11-214
Source: PubMed


Analysis of data from high-throughput experiments depends on the availability of well-structured data that describe the assayed biomolecules. Procedures for obtaining and organizing such meta-data on genes, transcripts and proteins have been streamlined in many data analysis packages, but are still lacking for metabolites. Chemical identifiers are notoriously incoherent, encompassing a wide range of different referencing schemes with varying scope and coverage. Online chemical databases use multiple types of identifiers in parallel but lack a common primary key for reliable database consolidation. Connecting identifiers of analytes found in experimental data with the identifiers of their parent metabolites in public databases can therefore be very laborious.
Here we present a strategy and a software tool for integrating metabolite identifiers from local reference libraries and public databases that do not depend on a single common primary identifier. The program constructs groups of interconnected identifiers of analytes and metabolites to obtain a local metabolite-centric SQLite database. The created database can be used to map in-house identifiers and synonyms to external resources such as the KEGG database. New identifiers can be imported and directly integrated with existing data. Queries can be performed in a flexible way, both from the command line and from the statistical programming environment R, to obtain data set tailored identifier mappings.
Efficient cross-referencing of metabolite identifiers is a key technology for metabolomics data analysis. We provide a practical and flexible solution to this task and an open-source program, the metabolite masking tool (MetMask), available at, that implements our ideas.

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Available from: Miyako Kusano, Oct 02, 2015
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    • "Each separate platform can individually be appropriate for a metabolomics study. For this investigation , the four separate data sets from each platform were collated using a documented data summarization strategy (Redestig et al. 2010; Kusano et al. 2011). Overall, the approach generated a total of 732 identified or annotated peaks (Supporting Information File S2). "
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    • "We only considered open-source applications as these can readily be adapted to the needs of the metabolic reconstruction community and integrated into metabolic reconstruction tools. Three applications that met these criteria were MetMask [29], the Chemical Translation System (CTS) [30] and UniChem [31]. These applications implement annotation strategies that go beyond name search. "
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    • "Okada et al. (2009) Exp: selection of metabolites Brassicaceae, Gramineae, Fabaceae Sawada et al. (2009) Exp: matrix-assisted laser desorption/ionization mass spectrometry Shroff et al. (2009) Exp: determination of gene function Arabidopsis thaliana Stracke et al. (2009) Bioinfo: complexity of relationship between plants and metabolites Takemoto et al. (2009) Bioinfo: metabolic pathway prediction Tanaka et al. (2009b) Exp: quality assessment Kampo medicine Tanaka et al. (2009a) Exp: quality assessment Angelica acutiloba Tianniam et al. (2009) Review: web resources in MS-based metabolomics Tohge and Fernie (2009) Bioinfo: metabolite annotation Wishart et al. (2009) Exp: diarylheptanoid biosynthesis Curcuma longa Xie et al. (2009) Review: functional genomics Yonekura-Sakakibara and Saito (2009) Exp: metabolite composition Rhizoctania solani Aliferis and Jabaji (2010) Exp: QTLs of barley, against Fusarium head blight Hordeum vulgare Bollina et al. (2010) Exp: changing color of flower from dark purple to white Brunfelsia calycina Bar-Akiva et al. (2010) Bioinfo: chemical similarity search and substructure matching of compounds Hattori et al. (2010) DB: MassBank, MS DB Horai et al. (2010) Review: MS data processing Kind and Fiehn (2010) Review: metabolomics in plant ecology and genetics Macel et al. (2010) Exp: metabolic profiling of different tissues Arabidopsis thaliana Matsuda et al. (2010) Review: identification of metabolites Neumann and Bocker (2010) DB: polyphenol contents in foods Neveu et al. (2010) Review: FT-ICR-MS. Reaction representation based on van Krevelen diagram Ohta et al. (2010) Review: relationship between individual omics data based on multivariate analysis and DB Medicinal plants Okada et al. (2010) Review: dietary intake Penn et al. (2010) Bioinfo: multiple metabolomics platforms for different types of MS Redestig et al. (2010) Review: functional genomics Saito and Matsuda (2010) DB: benzylisoquinoline alkaloids Singla et al. (2010) Exp: quality assessment Glycyrrhiza uralensis Tanaka et al. (2010) Review: annotation of gene function based on co-response gene and identification of metabolites Tohge and Fernie (2010) Bioinfo: network analysis of species–metabolite relationships Takemoto (2010) Bioinfo: MS data processing Weber et al. (2010) Bioinfo: QTL informatics Solanum tuberosum Acharjee et al. (2011) Exp: subcellular distribution of metabolites Arabidopsis thaliana Krueger et al. (2011) Review: pesticide research Aliferis and Chrysayi- Tokousbalides (2011) Bioinfo: metabolomics in medical purpose with systems chemical biology and chemoinformatics "
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