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https://github.com/egonw/semanticchemistry

An ontology that aims to establish a standard in representing chemical information including chemical structure and the ability to richly describe chemical properties, whether intrinsic or computed. It includes terms for the descriptors commonly used in cheminformatics software applications and the algorithms which generate them.

Proper citation: Chemical Information Ontology (RRID:SCR_003290) Copy   

•   Source: SciCrunch Registry

http://theswo.sourceforge.net

An ontology for describing software tools, their types, tasks, versions, provenance and data associated (the input and output data types and the uses the software can be put to).

Proper citation: Software Ontology (RRID:SCR_003493) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/GRO

Ontology that is a conceptual model for the domain of gene regulation. It covers processes that are linked to the regulation of gene expression as well as physical entities that are involved in these processes (such as genes and transcription factors) in terms of ontology classes and semantic relations between classes. GRO is intended to represent common knowledge about gene regulation in a formal way rather than representing extremely fine-grained classes as can be found in ontologies such as the Gene Ontology (GO) (created for data base annotation purposes) and various relevant databases. The main purpose of the ontology is to support NLP applications. It has a particular focus on the relations between processes and the molecules (participants) involved. The basic structure of the GRO is a direct acyclic graph (DAG) with ontology classes as nodes and is-a relations between classes as edges. The taxonomic backbone is further enriched by several semantic relation types (part-of, from-species, participates-in with the two sub-relations agent-of and patient-of).

Proper citation: Gene Regulation Ontology (RRID:SCR_010590) Copy   

•   Source: SciCrunch Registry

https://www.ebi.ac.uk/chebi/beta/

Collection of chemical compounds and other small molecular entities that incorporates an ontological classification of chemical compounds of biological relevance, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. The molecular entities in question are either products of nature or synthetic products used to intervene in the processes of living organisms.

Proper citation: CHEBI (RRID:SCR_002088) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/efo/

An application focused ontology modelling the experimental factors in ArrayExpress and Gene Expression Atlas. It has been developed to increase the richness of the annotations that are currently made in the ArrayExpress repository, to promote consistent annotation, to facilitate automatic annotation and to integrate external data. The ontology describes cross-product classes from reference ontologies in area such as disease, cell line, cell type and anatomy. The methodology employed in the development of EFO involves construction of mappings to multiple existing domain specific ontologies, such as the Disease Ontology and Cell Type Ontology. This is achieved using a combination of automated and manual curation steps and the use of a phonetic matching algorithm. The ontology is evaluated with use cases from the ArrayExpress repository and ArrayExpress Atlas. You may also browse the EFO in the NCBO Bioportal. Term submissions are welcome.

Proper citation: Experimental Factor Ontology (RRID:SCR_003574) Copy   

•   Source: SciCrunch Registry

http://edamontology.org/

An ontology of bioinformatics operations (tool, application, or workflow functions), types of data including identifiers, topics (application domains), and data formats. The applications of EDAM are within organizing tools and data, finding suitable tools in catalogues, and integrating them into complex applications or workflows. Semantic annotations with EDAM are applicable to diverse entities such as for example Web services, databases, programmatic libraries, standalone tools and toolkits, interactive applications, data schemas, data sets, or publications within bioinformatics. Annotation with EDAM may also contribute to data provenance, and EDAM terms and synonyms can be used in text mining. EDAM - and in particular the EDAM Data sub-ontology - serves also as a markup vocabulary for bioinformatics data on the Semantic Web.

Proper citation: EDAM Ontology (RRID:SCR_006620) Copy   

•   Source: SciCrunch Registry

https://wiki.nci.nih.gov/display/LexEVS/LexGrid

LexGrid (Lexical Grid) provides support for a distributed network of lexical resources such as terminologies and ontologies via standards-based tools, storage formats, and access/update mechanisms. The Lexical Grid Vision is for a distributed network of terminological resources. It is the foundation of the National Center for Biomedical Ontology BioPortal interface and web-services, and can parse OBO format, as well as other formats such as OWL. Currently, there are many terminologies and ontologies in existence. Just about every terminology has its own format, its own set of tools, and its own update mechanisms. The only thing that most of these pieces have in common with each other is their incompatibility. This makes it very hard to use these resources to their full potential. We have designed the Lexical Grid as a way to bridge terminologies and ontologies with a common set of tools, formats and update mechanisms. The Lexical Grid is: * accessible through a set of common APIs * joined through shared indices * online accessible * downloadable * loosely coupled * locally extendable * globally revised * available in web-space on web-time * cross-linked The realization of this vision requires three interlocking components, which are: * Standards - access methods and formats need to be published and openly available * Tools - standards based tools must be readily available * Content - commonly used terminologies have to be available for access and download Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: LexGrid (RRID:SCR_006627) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/sbo/

A set of controlled, relational vocabularies of terms commonly used in Systems Biology, and in particular in computational modeling. The ontology consists of seven orthogonal vocabularies defining: the roles of reaction participants (eg. substrate), quantitative parameters (eg. Michaelis constant), a precise classification of mathematical expressions that describe the system (eg. mass action rate law), the modeling framework used (eg. logical framework), and a branch each to describe entity (eg. macromolecule) and interaction (eg. process) types, and a branch to define the different types of metadata that may be present within a model. SBO terms can be used to introduce a layer of semantic information into the standard description of a model, or to annotate the results of biochemical experiments in order to facilitate their efficient reuse. SBO is an Open Biomedical Ontologies (OBO) candidate ontology, and is free for use. A programmatic access to the content of the Systems Biology Ontology is provided by Web Services.

Proper citation: SBO (RRID:SCR_006753) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/ORDO

Ontology to provide a structured vocabulary for rare diseases capturing relationships between diseases, genes and other relevant features which will form a useful resource for the computational analysis of rare diseases. It derived from the Orphanet database (http://www.orpha.net) , a multilingual database dedicated to rare diseases populated from literature and validated by international experts. It integrates a nosology (classification of rare diseases), relationships (gene-disease relations, epiemological data) and connections with other terminologies (MeSH, SNOMED CT, UMLS, MedDRA), databases (OMIM, UniProtKB, HGNC, ensembl, Reactome, IUPHAR, Geantlas) or classifications (ICD10). The ontology will be maintained by Orphanet and further populated with new data. Orphanet classifications can be browsed in the OLS view. The Orphanet Rare Disease Ontology is updated monthly and follows the OBO guidelines on deprecation of terms. It constitutes the official ontology of rare diseases produced and maintained by Orphanet (INSERM, US14).

Proper citation: Orphanet Rare Disease Ontology (RRID:SCR_010402) Copy   

•   Source: SciCrunch Registry