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Siegele, Deborah individual record
Associate Professor

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.

selected publications
Academic Articles40
  • Xia, J., Chiu, L., Nehring, R. B., Nunez, M., Mei, Q., Perez, M., ... Rosenberg, S. M. (2019). Bacteria-to-Human Protein Networks Reveal Origins of Endogenous DNA Damage. CELL. 176(1-2), 127-+.
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  • Giglio, M., Tauber, R., Nadendla, S., Munro, J., Olley, D., Ball, S., ... Chibucos, M. C. (2019). ECO, the Evidence & Conclusion Ontology: community standard for evidence information.. Nucleic acids research. 47(D1), D1186-D1194.
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  • 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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  • The Gene Ontology Consortium, .. (2019). The Gene Ontology Resource: 20 years and still GOing strong.. Nucleic acids research. 47(D1), D330-D338.
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  • Carbon, S., Dietze, H., Lewis, S. E., Mungall, C. J., Munoz-Torres, M. C., Basu, S., ... Consortium, G. O. (2017). Expansion of the Gene Ontology knowledgebase and resources. Nucleic acids research. 45(D1), D331-D338.
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Conference Papers4
chaired theses and dissertations
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Texas A&M University; Biology; 3258 TAMU
College Station, TX 77843-3258