ZOOMA is an application for discovering optimal ontology mappings, developed by the Samples, Phenotypes and Ontologies Team at EBI. It can be used to automatically annotate "properties" (plain text, descriptive values about biological entities) with "semantic tags" (ontology classes).
If you are new to ZOOMA, take a look at our getting started guide.
ZOOMA was developed by the Samples, Phenotypes and Ontologies Team at the EBI. We help a number of groups and databases annotate their data to ontologies, including Expression Atlas, Open Targets, the GWAS Catalog and many others. This means we handle a wide variety of diverse datasets that contain metadata descriptions about species, anatomical components, cell types, drugs treatments and compounds, diseases and phenotypes and many others.
To support richer querying, we assist with the annotation of these metadata descriptions to a series of ontologies, allowing us to harmonise data across experiments to drive advanced searches and improve data visualisation. Generating such annotations is a manually intensive operation that requires considerable curation. And, as ontologies evolve and new technologies for performing functional genomics experiments emerge, annotations need reviewing and improving. We call the difference between the data we have submitted, and the data that is accurately mapped to ontology terms, an "annotation gap".
ZOOMA exists to reduce this annotation gap through automatic annotation. ZOOMA contains a linked data repository of annotation knowledge and highly annotated data that has been seeded with manually curated annotation derived from publicly available databases, such as the Expression Atlas, as well as other sources. By creating this repository of annotations, tracking their provenance and scoring their quality, we have been able to build a "smart" annotation search service. Because the source data has been curated by hand, it is a rich source of knowledge that is optimised for this task of semantic mark-up of keywords, in contrast with text-mining approaches. It is also capable of capturing more obscure types of annotations such as those involving compound properties.
The Samples, Phenotypes and Ontologies Team is also responsible for the development and maintainence of the Experimental Factor Ontology (EFO) and the Ontology Lookup Service (OLS). If you'd like to know more about ontologies, need new know where to find or request terms to describe your data, or would just like some information, check out the EFO or OLS websites, or have a look at our SPOT homepage. Alternatively, you can get in touch using the contact details below.
Why use ZOOMA?
You should use ZOOMA if you have data that has been described with any series of keywords. ZOOMA provides a mechanism to search for these keywords, using typing or context information or combinations of co-occuring keywords to improve accuracy, and automatically annotate your data to ontology terms.
Using ZOOMA, it is trivially easy to automatically annotate any data that has been richly described previously. This improves the interoperability of your data, but also frees up curators to work on more difficult and interesting tasks, rather than spending time making the same old fixes and corrections to align data with the current state of the art in ontologies.
For questions and to provide feedback, please contact email@example.com.
For bug reports and feature requests, please use our JIRA bug tracker.
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