Why do we need GO?

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To ask meaningful questions, biologists often need to retrieve and analyse data from disparate sources. For example, if you were searching for new targets for antibiotics, you might want to find all the gene products that are involved in bacterial protein synthesis, but that have significantly different sequences or structures from those in humans. But if one database describes these molecules as being involved in 'translation', whereas another uses the phrase 'protein synthesis', it will be difficult for you - and even harder for a computer - to find functionally equivalent terms.

The Gene Ontology (GO) project is a collaborative effort to address the need for consistent descriptions of gene products in different databases. The GO collaborators are developing three ontologies - a word used by computer scientists to mean 'specifications of a relational vocabulary' - that describe biological processes, cellular components and molecular functions in a species-independent manner.

Ontologies provide a vocabulary for representing and communicating knowledge about a topic, and a set of relationships that hold among the terms of the vocabulary. They can be structurally very complex, or relatively simple. Most importantly, ontologies capture domain knowledge in a way that can easily be dealt with by a computer . Because the terms in an ontology and the relationships between the terms are carefully defined, the use of ontologies facilitates making standard annotations, improves computational queries, and can support the construction of inference statements from the information at hand.

Genomic sequencing projects and microarray experiments alike produce electronically-generated data flows that require computer accessible systems to work with the information. As systems that make domain knowledge available to both humans and computers, bio-ontologies such as GO and the many other bio-ontologies being created (see the OBO web page for some examples) for are essential to the process of extracting biological insight from enormous sets of data.