MicrO: an ontology of phenotypic and metabolic characters, assays, and culture media found in prokaryotic taxonomic descriptions
AffiliationUniv Arizona, Sch Informat
Univ Arizona, CyVerse
Natural language processing
MetadataShow full item record
PublisherBIOMED CENTRAL LTD
CitationMicrO: an ontology of phenotypic and metabolic characters, assays, and culture media found in prokaryotic taxonomic descriptions 2016, 7 (1) Journal of Biomedical Semantics
JournalJournal of Biomedical Semantics
Rights© 2016 Blank et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractBackground: MicrO is an ontology of microbiological terms, including prokaryotic qualities and processes, material entities (such as cell components), chemical entities (such as microbiological culture media and medium ingredients), and assays. The ontology was built to support the ongoing development of a natural language processing algorithm, MicroPIE (or, Microbial Phenomics Information Extractor). During the MicroPIE design process, we realized there was a need for a prokaryotic ontology which would capture the evolutionary diversity of phenotypes and metabolic processes across the tree of life, capture the diversity of synonyms and information contained in the taxonomic literature, and relate microbiological entities and processes to terms in a large number of other ontologies, most particularly the Gene Ontology (GO), the Phenotypic Quality Ontology (PATO), and the Chemical Entities of Biological Interest (ChEBI). We thus constructed MicrO to be rich in logical axioms and synonyms gathered from the taxonomic literature. Results: MicrO currently has similar to 14550 classes (similar to 2550 of which are new, the remainder being microbiologically-relevant classes imported from other ontologies), connected by similar to 24,130 logical axioms (5,446 of which are new), and is available at (http://purl.obolibrary.org/obo/MicrO.owl) and on the project website at https://github.com/carrineblank/MicrO. MicrO has been integrated into the OBO Foundry Library (http://www.obofoundry.org/ontology/micro.html), so that other ontologies can borrow and re-use classes. Term requests and user feedback can be made using MicrO's Issue Tracker in GitHub. We designed MicrO such that it can support the ongoing and future development of algorithms that can leverage the controlled vocabulary and logical inference power provided by the ontology. Conclusions: By connecting microbial classes with large numbers of chemical entities, material entities, biological processes, molecular functions, and qualities using a dense array of logical axioms, we intend MicrO to be a powerful new tool to increase the computing power of bioinformatics tools such as the automated text mining of prokaryotic taxonomic descriptions using natural language processing. We also intend MicrO to support the development of new bioinformatics tools that aim to develop new connections between microbial phenotypes and genotypes (i.e., the gene content in genomes). Future ontology development will include incorporation of pathogenic phenotypes and prokaryotic habitats.
VersionFinal published version
SponsorsThis work was funded by grants from the National Science Foundation (award DEB-1208534 to CEB, DEB-1208567 to HC, and DEB-1208685 to LRM) and by a travel grant (to CEB) to attend the 2013 NESCent Ontologies for Evolutionary Biology workshop. RW was supported by CyVerse and the National Science Foundation under award numbers DBI-0735191 and DBI-1265383. Many thanks to Elvis Hsin-Hui Wu (University of Arizona), Gail Gasparich (Towson University), and Gordon Burleigh (University of Florida) for comments and/or assistance with ontology construction and compilation of taxonomic descriptions. We would also like to thank Chris Mungall (LBNL), Oliver He (University of Michigan) for technical assistance with OntoBee and OntoFox, and Gareth Owen (ChEBI project leader, head curator) and other curators at ChEBI for assistance in the incorporation of microbial-specific chemical terms and synonyms into ChEBI. Thanks also to the instructors (Melissa Haendel, Matt Yoder, Jim Balhoff) and students of the 2013 NESCent Ontologies for Evolutionary Biology workshop, and to Karen Cranston (NESCent) and the support staff at NESCent. Thanks also to the OBI-devel team for comments regarding the overall structure of assay terms, and associated object properties, in MicrO.