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http://bioportal.bioontology.org/annotator

A Web service that annotates textual metadata (e.g. journal abstract) with relevant ontology concepts. NCBO uses this Web service to annotate resources in the NCBO Resource Index. They also provide this Web service as a stand-alone service for users. This Web service can be accessed through BioPortal or used directly in your software. Currently, the annotation workflow is based on syntactic concept recognition (using concept names and synonyms) and on a set of semantic expansion algorithms that leverage the semantics in ontologies (e.g., is_a relations). Their service methodology leverages ontologies to create annotations of raw text and returns them using semantic web standards.

Proper citation: NCBO Annotator (RRID:SCR_005329) Copy   

•   Source: SciCrunch Registry
  

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

A web ontology for the semantic representation of the computer assisted brain trauma rehabilitation domain. This is a novel and emerging domain, since it employs the use of robotic devices, adaptation software and machine learning to facilitate interactive, adaptive and personalized rehabilitation care, patient monitoring and assisted living.

Proper citation: Computer Assisted Brain Injury Rehabilitation Ontology (RRID:SCR_005288) Copy   

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http://bmir.stanford.edu/

Mark Musen''s laboratory studies components for building knowledge-based systems, controlled terminologies and ontologies, and technology for the Semantic Web. For more than two decades, Musen''s group has worked to elucidate reusable building blocks of intelligent systems, and to develop scalable computational architectures for systems with significant applications in biomedicine. Informatics is the study of information: its structure, its communication, and its use. As society becomes increasingly information intensive, the need to understand, create, and apply new methods for modeling, managing, and acquiring information has never been greater especially in biomedicine. BMIR is home to world class scientists and trainees developing cutting-edge ways to acquire, represent, process, and manage knowledge and data related to health, health care, and the biomedical sciences. Our faculty, students, and staff are committed to ensuring the biomedical community is properly equipped for the information age, and believe our efforts will provide the structure for the burgeoning revolution of health care and the biomedical sciences.

Proper citation: Stanford Center for Biomedical Informatics Research (RRID:SCR_005698) Copy   

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http://www.clo-ontology.org/

A community-driven ontology that is developed to standardize and integrate cell line information and support computer-assisted reasoning. Its focus is on permanent cell lines from culture collections. Upper ontology structures that frame the skeleton of CLO include Basic Formal Ontology and Relation Ontology. Cell lines contained in CLO are associated with terms from other ontologies such as Cell Type Ontology, NCBI Taxonomy, and Ontology for Biomedical Investigation. A common design pattern for the cell line is used to model cell lines and their attributes, the Jurkat cell line provides ane xample. Currently CLO contains over 36,000 cell line entries obtained from ATCC, HyperCLDB, Coriell, and bymanual curation. The cell lines are derived from 194 cell types, 656 anatomical entries, and 217 organisms. The OWL-based CLO is machine-readable and can be used in various applications. The CLO development has become a community effort with international collaborations. The development consortium includes experts from all over the world: the USA, Europe, and Japan.

Proper citation: Cell Line Ontology (RRID:SCR_005840) Copy   

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

Ontology of clinical terms Version 3 (CTV3) (Read Codes) (Q199): National Health Service National Coding and Classification Centre

Proper citation: Read Codes Clinical Terms Version 3 (RRID:SCR_006055) Copy   

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

Ontology specifically for Randomized Controlled Trials in order to facilitate the production of systematic reviews and metaanalysis.

Proper citation: Randomized Controlled Trials Ontology (RRID:SCR_005992) Copy   

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

An application ontology for the domain of gene transcription regulation. The ontology integrates fragments of GO and MI with data from GOA, IntAct, UniProt, NCBI, KEGG and orthology relations.

Proper citation: Regulation of Transcription Ontology (RRID:SCR_006238) Copy   

•   Source: SciCrunch Registry
  

http://obi-ontology.org/

An ontology for the description of biological and clinical investigations built with international, collaborative effort. The ontology represents the design of an investigation, the protocols and instrumentation used, the material used, the data generated and the type analysis performed on it. This includes a set of universal terms that are applicable across various biological and technological domains, and domain-specific terms relevant only to a given domain. Currently OBI is being built under the Basic Formal Ontology (BFO). This project was formerly titled the Functional Genomics Investigation Ontology (FuGO) project.

Proper citation: Ontology for Biomedical Investigations (RRID:SCR_006266) Copy   

•   Source: SciCrunch Registry
  

http://www.xenbase.org/anatomy/xao.do?method=display

A structured, controlled vocabulary of the anatomy and development of the African clawed frogs (Xenopus laevis and tropicalis), organized in a graphical structure. Tissues are shown as being part of other tissues and the timing of their development is indicated by start and end stages. The lineage of tissues is represented by develops from relationships between different tissues at different developmental stages. Many items have been classified according to the Common Anatomy Reference Ontology. The Xenopus Anatomical Ontology will be used to annotate Xenopus gene expression patterns and mutant and morphant phenotypes. Its robust developmental map will enable powerful database searches and data analyses. They encourage community recommendations for updates and improvements to the ontology.

Proper citation: Xenopus Anatomy Ontology (RRID:SCR_004337) Copy   

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

An ontology to describe the entities and relationships in the SEEK database, a Systems Biology environment for the sharing and exchange of data and models. The SysMO-SEEK database contains the work of the SysMO consortium (Systems Biology of Micro-Organisms) https://seek.sysmo-db.org/

Proper citation: SysMO JERM Ontology of Systems Biology for Micro-Organisms (RRID:SCR_004569) Copy   

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http://www.proconsortium.org/pro/

An ontological representation of protein-related entities by explicitly defining them and showing the relationships between them. Each PRO term represents a distinct class of entities (including specific modified forms, orthologous isoforms, and protein complexes) ranging from the taxon-neutral to the taxon-specific. The ontology has a meta-structure encompassing three areas: proteins based on evolutionary relatedness (ProEvo); protein forms produced from a given gene locus (ProForm); and protein-containing complexes (ProComp). NOTICE: The PRO ID format has changed from PRO: to PR: (e.g. PRO:000000563 is now PR:000000563).

Proper citation: PR (RRID:SCR_004964) Copy   

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

A structured controlled vocabulary for describing cereal plant development and growth stages. Please note that this ontology has now been superseded by the Plant Ontology.

Proper citation: Cereal Plant Development Ontology (RRID:SCR_005095) Copy   

•   Source: SciCrunch Registry
  

http://bioassayontology.org/

Ontology to describe and categorize chemical biology and drug screening assays and their results including high-throughput screening (HTS) data for the purpose of categorizing assays and data analysis. BAO is an extensible, knowledge-based, highly expressive (currently SHOIQ(D)) description of biological assays making use of descriptive logic based features of the Web Ontology Language (OWL). BAO currently has over 700 classes and also makes use of several other ontologies. It describes several concepts related to biological screening, including Perturbagen, Format, Meta Target, Design, Detection Technology, and Endpoint. Perturbagens are perturbing agents that are screened in an assay; they are mostly small molecules. Assay Meta Target describes what is known about the biological system and / or its components interrogated in the assay (and influenced by the Perturbagen). Meta target can be directly described as a molecular entity (e.g. a purified protein or a protein complex), or indirectly by a biological process or event (e.g. phosphorylation). Format describes the biological or chemical features common to each test condition in the assay and includes biochemical, cell-based, organism-based, and variations thereof. The assay Design describes the assay methodology and implementation of how the perturbation of the biological system is translated into a detectable signal. Detection Technology relates to the physical method and technical details to detect and record a signal. Endpoints are the final HTS results as they are usually published (such as IC50, percent inhibition, etc). BAO has been designed to accommodate multiplexed assays. All main BAO components include multiple levels of sub-categories and specification classes, which are linked via object property relationships forming an expressive knowledge-based representation.

Proper citation: Bioassay Ontology (RRID:SCR_002638) Copy   

•   Source: SciCrunch Registry
  

http://www.geneontology.org/

Computable knowledge regarding functions of genes and gene products. GO resources include biomedical ontologies that cover molecular domains of all life forms as well as extensive compilations of gene product annotations to these ontologies that provide largely species-neutral, comprehensive statements about what gene products do. Used to standardize representation of gene and gene product attributes across species and databases.

Proper citation: Gene Ontology (RRID:SCR_002811) Copy   

•   Source: SciCrunch Registry
  

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

A structured controlled vocabulary for the phenotypes of Ascomycete fungi.

Proper citation: Ascomycete Phenotype Ontology (RRID:SCR_003254) Copy   

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http://code.google.com/p/bcgo-ontology/

An application ontology built for the Beta Cell Genomics database aiming to support database annotation, complicated semantic queries, and automated cell type classification. The ontology is developed using Basic Formal Ontology (BFO) as upper ontology, Ontology for Biomedical Investigations (OBI) as ontology framework and integrated subsets of multiple OBO Foundry (candidate) ontologies. Current the BCGO contains 2383 classes including terms referencing to 24 various OBO Foundry ontologies including CL, CLO, UBERON, GO, PRO, UO, etc.

Proper citation: Beta Cell Genomics Ontology (RRID:SCR_003259) Copy   

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http://code.google.com/p/bco/

Ontology developed as an application ontology as part of the Biocode Commons project whose goal is to support the interoperability of biodiversity data, including data on museum collections, environmental and metagenomic samples, and ecological surveys. It includes consideration of the distinctions between individuals, organisms, voucher specimens, lots, and samples the relations between these entities, and processes governing the creation and use of samples. Within scope as well are properties including collector, location, time, storage environment, containers, institution, and collection identifiers.

Proper citation: Biological Collections Ontology (RRID:SCR_003262) Copy   

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

An ontology that describes the LIPIDMAPS nomenclature classification explicitly using description logics (OWL-DL). Lipid classes are organized hierarchically with the super-classes restricted by generic necessary conditions. More specific necessary conditions are used to define membership requirements for sub classes of lipid according to appropriate functional groups. Lipid research is increasingly integrated within systems level biology such as lipidomics where lipid classification is required before appropriate annotation of chemical functions can be applied.

Proper citation: Lipid Ontology (RRID:SCR_003349) Copy   

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

An ontology designed to represent the variety of methods used to make qualitative and quantitative clinical and phenotype measurements both in the clinic and with model organisms.

Proper citation: Measurement Method Ontology (RRID:SCR_003373) Copy   

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http://sig.biostr.washington.edu/projects/fm/

A domain ontology that represents a coherent body of explicit declarative knowledge about human anatomy. It is concerned with the representation of classes or types and relationships necessary for the symbolic representation of the phenotypic structure of the human body in a form that is understandable to humans and is also navigable, parseable and interpretable by machine-based systems. Its ontological framework can be applied and extended to all other species. The description of how the OWL version was generated is in Pushing the Envelope: Challenges in a Frame-Based Representation of Human Anatomy by N. F. Noy, J. L. Mejino, C. Rosse, M. A. Musen: http://bmir.stanford.edu/publications/view.php/pushing_the_envelope_challenges_in_a_frame_based_representation_of_human_anatomy The Foundational Model of Anatomy ontology has four interrelated components: # Anatomy taxonomy (At), # Anatomical Structural Abstraction (ASA), # Anatomical Transformation Abstraction (ATA), # Metaknowledge (Mk), The ontology contains approximately 75,000 classes and over 120,000 terms; over 2.1 million relationship instances from over 168 relationship types link the FMA's classes into a coherent symbolic model.

Proper citation: FMA (RRID:SCR_003379) Copy   

•   Source: SciCrunch Registry