Mining metabolites: Extracting the yeast metabolome from the literature

Chikashi Nobata, Paul D. Dobson, Syed A. Iqbal, Pedro Mendes, Jun'ichi Tsujii, Douglas B. Kell, Sophia Ananiadou

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    Abstract

    Text mining methods have added considerably to our capacity to extract biological knowledge from the literature. Recently the field of systems biology has begun to model and simulate metabolic networks, requiring knowledge of the set of molecules involved. While genomics and proteomics technologies are able to supply the macromolecular parts list, the metabolites are less easily assembled. Most metabolites are known and reported through the scientific literature, rather than through large-scale experimental surveys. Thus it is important to recover them from the literature. Here we present a novel tool to automatically identify metabolite names in the literature, and associate structures where possible, to define the reported yeast metabolome. With ten-fold cross validation on a manually annotated corpus, our recognition tool generates an f-score of 78.49 (precision of 83.02) and demonstrates greater suitability in identifying metabolite names than other existing recognition tools for general chemical molecules. The metabolite recognition tool has been applied to the literature covering an important model organism, the yeast Saccharomyces cerevisiae, to define its reported metabolome. By coupling to ChemSpider, a major chemical database, we have identified structures for much of the reported metabolome and, where structure identification fails, been able to suggest extensions to ChemSpider. Our manually annotated gold-standard data on 296 abstracts are available as supplementary materials. Metabolite names and, where appropriate, structures are also available as supplementary materials. © 2010 The Author(s).
    Original languageEnglish
    Pages (from-to)94-101
    Number of pages7
    JournalMetabolomics
    Volume7
    Issue number1
    DOIs
    Publication statusPublished - 2011

    Keywords

    • Named entity recognition
    • Text mining
    • Yeast metabolome

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