Microorganisms comprise most of the evolutionary and genetic diversity in the tree of life, and produce a significant proportion of the standing crop of cellular carbon on the Earth. Prokaryotic microorganisms manifest their diversity in the form of morphological phenotypes (such as biofilm formation, multicellularity, and differentiation into specialized structures), ecological phenotypes (inhabiting environments that have particular temperature, salinity, and pH values), metabolic phenotypes (the ability to catalyze discrete chemical reactions), and the ability to perform biological processes (carrying out photosynthesis). Several studies have examined the evolution of microbial phenotypic traits in deep time. Nevertheless, most of these studies have focused on relatively small taxonomic groups, or have used a small number of phenotypic traits. This is because the taxon-by-character matrices (which record the presence and absence of traits for each taxon) required for these studies have been constructed manually and thus require significant efforts to build. Hence, the field needs to develop tools that can allow the accelerated, broad-scale study of the evolution of phenotypic traits across the prokaryotic domains of life.
Aims to standardize the annotation of experimental conditions in microbial data repositories. MCO provides growth conditions terms together with their definitions, synonyms, references, and higher-level relations. The terms offered by the ontology can unambiguously define and tag each attribute of a particular experimental condition in order to systematize the annotation. It was created with simple ontological terms to describe elementary components.
An ontology of prokaryotic phenotypes and metabolic characters. MicrO was built to support the ongoing development of a natural language processing algorithm, Microbial Phenomics Information Extractor. MicrO consists of ~2550 classes derived from text contained in the taxonomic descriptions of diverse prokaryotic taxa that span the archaeal and bacterial domains of life. The largest categories of classes in the ontology include assays (enzymatic, metabolic, and phenotypic assays), microbiological culture media and media ingredients, and prokaryotic qualities.
Provides consistent computable descriptions of phenotype data in fission yeast. FYPO uses several existing ontologies from the open biological and biomedical ontologies (OBO) collection. It organizes terms along three axes: one axis distinguishes normal from abnormal phenotypes, a second axis classifies phenotypes by the entity affected, and the last one axis distinguishes phenotypes relevant at the level of a cell are from those that can be observed only in a population of cells.
Covers and crosses the biomedical domains of clinical care, public health and biomedical research in the specific brucellosis field. Brucellosis Ontology (IDOBRU) is an extension ontology of the Infectious Disease Ontology (IDO) with a focus on brucellosis, a zoonotic infectious disease caused by Brucella spp. It can be used as a brucellosis knowledgebase and is applicable for brucellosis data exchange, data integration, and automated reasoning.