The awesome power of microbial genetics is based on the ability of geneticists to infer the components and properties of complex biological systems from the phenotypes of mutants. The systematic analysis of microbial phenotypes in thousands of bacterial genomes and metagenomes will be strengthened by a universal system to compare phenotypes across species and strains. The overall goal of this project is to build a such a system for ontology-based annotation of microbial phenotypes that will enable the efficient mining of relevant data to facilitate, and possibly one day automate, hypothesis generation leading to experimental studies. Under the first grant for this project, the Ontology of Microbial Phenotypes (OMP) was built and has so far been used to annotate 30% of the genes in Escherichia coli K-12. The work proposed in this application will develop and deploy a relational database for annotation data storage; continue development of the OMP; continue development of terms in the Evidence Ontology as needed for OMP annotation; develop a pipeline for generation of a synteny-based database of alleles and intraspecies ortholog groups (pangenes); continue improvement of the OMP web/database infrastructure; continue and expand annotation efforts in E. coli; begin annotation of Saccharomyces cerevisiae; and engage in community outreach. OMP is being developed in the context of multiple projects to improve phenotype annotation across all domains of life. As OMP encompasses some of the best-studied model genetic systems, it is in a position to influence the direction of the entire field o phenotype analysis. This project will stimulate the development of bioinformatics tools for phenotype analysis, which will be useful across all of genetics.

Public Health Relevance

Our ability to use genetics to understand bacteria and fungi relevant to human health is limited by the need to organize and compare information from mutant phenotypes of different organisms. This project will develop controlled vocabularies and standards for describing phenotypes. This system is needed for development of analysis tools that can recognize patterns among different studies and suggest new avenues for understanding microbial contributions to disease and normal health.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Ravichandran, Veerasamy
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Texas A&M Agrilife Research
Earth Sciences/Resources
College Station
United States
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Chibucos, Marcus C; Siegele, Deborah A; Hu, James C et al. (2017) The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations. Methods Mol Biol 1446:245-259
Gaudet, Pascale; Škunca, Nives; Hu, James C et al. (2017) Primer on the Gene Ontology. Methods Mol Biol 1446:25-37
Nehring, Ralf B; Gu, Franklin; Lin, Hsin-Yu et al. (2016) An ultra-dense library resource for rapid deconvolution of mutations that cause phenotypes in Escherichia coli. Nucleic Acids Res 44:e41
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Chibucos, Marcus C; Mungall, Christopher J; Balakrishnan, Rama et al. (2014) Standardized description of scientific evidence using the Evidence Ontology (ECO). Database (Oxford) 2014:
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