This article is part of the supplement: Proceedings of the 10th Bio-Ontologies Special Interest Group Workshop 2007. Ten years past and looking to the future

Proceedings

Facilitating the development of controlled vocabularies for metabolomics technologies with text mining

Irena Spasić^1,2 , Daniel Schober³ , Susanna-Assunta Sansone³ , Dietrich Rebholz-Schuhmann³ , Douglas B Kell^1,4 and Norman W Paton^1,2

¹Manchester Centre for Integrative Systems Biology, The University of Manchester, 131 Princess Street, Manchester, M1 7ND, UK

²School of Computer Science, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK

³The European Bioinformatics Institute, EMBL Outstation - Hinxton, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK

⁴School of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK

author email corresponding author email

BMC Bioinformatics 2008, 9(Suppl 5):S5doi:10.1186/1471-2105-9-S5-S5

Published:	29 April 2008

Abstract

Background

Many bioinformatics applications rely on controlled vocabularies or ontologies to consistently interpret and seamlessly integrate information scattered across public resources. Experimental data sets from metabolomics studies need to be integrated with one another, but also with data produced by other types of omics studies in the spirit of systems biology, hence the pressing need for vocabularies and ontologies in metabolomics. However, it is time-consuming and non trivial to construct these resources manually.

Results

We describe a methodology for rapid development of controlled vocabularies, a study originally motivated by the needs for vocabularies describing metabolomics technologies. We present case studies involving two controlled vocabularies (for nuclear magnetic resonance spectroscopy and gas chromatography) whose development is currently underway as part of the Metabolomics Standards Initiative. The initial vocabularies were compiled manually, providing a total of 243 and 152 terms. A total of 5,699 and 2,612 new terms were acquired automatically from the literature. The analysis of the results showed that full-text articles (especially the Materials and Methods sections) are the major source of technology-specific terms as opposed to paper abstracts.

Conclusions

We suggest a text mining method for efficient corpus-based term acquisition as a way of rapidly expanding a set of controlled vocabularies with the terms used in the scientific literature. We adopted an integrative approach, combining relatively generic software and data resources for time- and cost-effective development of a text mining tool for expansion of controlled vocabularies across various domains, as a practical alternative to both manual term collection and tailor-made named entity recognition methods.