Phenotypes are observable characteristics of an organism that result from the expression of a particular genotype in a particular environment. Examples of phenotypic traits in microbes are motility, sporulation, ability to perform anaerobic respiration, and resistance/sensitivity to an antibiotic.
Until recently, phenotypic information has been captured as free text descriptions in research papers. Ambiguities in natural language confound attempts to retrieve information across sources. For example, "serotype" and "serovar" both refer to the same phenotype, but a simple text-based query with either word alone would miss the other. Or a single term, such as "sporulation" is used to refer to multiple, distinct processes in different organisms. Issues such as these hamper the ability to integrate different phenotypic data sets for the same organism or to use phenotypic information in one organism to predict possible phenotypes in another organism. Ideally, phenotype information should be stored in a consistent, computable format for ease of data integration and mining.
Controlled vocabularies are used to provide both consistent terminology and a structured data format for the capture of biological information. Ontologies are controlled vocabularies of defined terms with unique identifiers and precise relationships to each other. There are phenotype ontologies available for many eukaryotic organisms, including fungi. However, when the OMP project was initiated, none of the existing ontologies was appropriate to comprehensively capture phenotypes for Bacteria or Archaea or to enable comparisons across microbial taxa.
The Siegele lab and our collaborators at TAMU and the Univ. of Maryland (IGS) are developing a formal Ontology of Microbial Phenotypes (OMP). Our lab is focused on term development and annotating microbial phenotypes. OMP can be accessed at microbialphenotypes.org. Releases of OMP are available at github.com/microbialphenotypes.
- Harvard University - (Cambridge, Massachusetts, United States), Postdoctoral Training 1992
- Ph.D. in Molecular Biology, University of Wisconsin - Madison - (Madison, Wisconsin, United States) 1989
- B.A. in Biochemistry, Northwestern University - (Evanston, Illinois, United States) 1976
- Hudson, M. A., Siegele, D. A., & Lockless, S. W. (2020). Use of a Fluorescence-Based Assay To Measure Escherichia coli Membrane Potential Changes in High Throughput.. Antimicrobial Agents and Chemotherapy. 64(9),
- Siegele, D. A., LaBonte, S. A., Wu, P., Chibucos, M. C., Nandendla, S., Giglio, M. G., & Hu, J. C. (2019). Phenotype annotation with the ontology of microbial phenotypes (OMP). Journal of Biomedical Semantics. 10(1), 13.
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- Chibucos, M. C., Siegele, D. A., Hu, J. C., & Giglio, M. (2016). The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations. Methods in Molecular Biology. The Gene Ontology Handbook. (pp. 245-259). Springer New York.
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- Chibucos, M., Zweifel, A., Siegele, D., Uetz, P., Giglio, M., & Hu, J. (2011). The ontology of microbial phenotypes (OMP): A precomposed ontology based on cross products from multiple external ontologies that is used for guiding microbial phenotype annotation. CEUR Workshop Proceedings. 833, 237-239.
- Ganesh, R., Siegele, D. A., & Ioerger, T. R. (2003). MOPAC: motif finding by preprocessing and agglomerative clustering from microarrays.. Biocomputing. 41-52.
- Champion, M. M., Campbell, C. S., Siegele, D. A., Russell, D. H., & Hu, J. C. (2002). Proteome analysis of Escherichia coli K-12 by two-dimensional native-state chromatography and MALDI-MS. 59-60.
- SIEGELE, D. A. (1993). ESCHERICHIA-COLI FUNCTIONS REQUIRED FOR RESUMING GROWTH AFTER STARVATION. Journal of Cellular Biochemistry. 282-282.
- Bain, Sherrie Valarie (2005-05). An unknown regulator affects cell division and the timing of entry into stationary phase in Escherichia coli. (Master's Thesis)
- Kalyanaraman, Gayathri (2003-12). Construction and characterization of yciGFE mutants in Escherichia coli. (Master's Thesis)