Genome and metagenome sequencing efforts are producing a wealth of new information about microbes. Phenotypes are the observable traits resulting from genotypes and thus are a fundamental aspect of the biology of all organisms. Our ability to fully exploit the power of phenotypes for functional and comparative genomics in basic, applied, and clinical microbiology is hindered by the lack of a controlled terminology to describe and classify them. Ontologies are controlled vocabularies with defined relationships between terms. The overall goal of this project is to build a system for ontology-based annotation of bacterial phenotypes. The project will develop the necessary components and infrastructure, test the system on a well-characterized model system, and involve the relevant scientific communities in its development. The components are: 1) an Ontology for Microbial Phenotypes (OMP). 2) An extension of an existing ontology, the Evidence Code Ontology (ECO) to describe how OMP phenotypes were determined and cross referencing of ECO terms to a methods resource. 3) A pilot project to use the system to catalog phenotypes of mutations from E. coli, a well- understood bacterial model system. 4) a web site to help the scientific community find these new resources for bacterial phenotype annotation. 5) Outreach and education to promote the use of the system and to incorporate it into undergraduate and graduate education in genetics and microbiology.
An improved phenotype annotation system will have broad impacts relevant to the mission of NIH. The ability to correlate differences in gene content with important phenotypic differences between strains or microbial communities can help researchers target genes or organisms likely to be involved in virulence, drug resistance, and other important processes to infectious disease and human health. In addition, it can be used to elucidate the function of proteins involved in pathogenesis through comparative analysis based on phenotypes.
|Chibucos, Marcus C; Mungall, Christopher J; Balakrishnan, Rama et al. (2014) Standardized description of scientific evidence using the Evidence Ontology (ECO). Database (Oxford) 2014:|