98 5 Survey of Ontologies in Bioinformatics
ucts in a number of its databases, such as SWISS-PROT, TrEMBL, and InterPro
(Camon et al. 2003; GOA 2003).
Although GO is the most prominent of all bio-ontologies, it did not origi-
nally make use of a formal ontological framework such as XML or RDF. To
remedy this situation, the Gene Ontology Next Generation Project (GONG)
is developing a staged methodology to evolve the current representation of
the GO into the Web Ontology Language (OWL) introduced in section 4.4.
OWL allows one to take advantage of the richer formal expressiveness and
the reasoning capabilities of the underlying formal logic. Each stage pro-
vides a step-level increase in formal explicit semantic content with a view
to supporting validation, extension, and multiple classification of GO (Wroe
et al. 2003).5.1.3 Ontologies of Bioinformatics Ontologies
With the proliferation of biological ontologies and databases, the ontologies
themselves need to be organized and classified. This survey chapter gives
an informal classification, but a more formal approach is needed: an ontol-
ogy of biological ontologies. In this section we review two examples of such
“metaontologies.”OBO obo.sourceforge.net
The Open Biological Ontologies seeks to collect ontologies for the domains
of genomics and proteomics. The criteria for inclusion are that the ontology
be open, use either GO or OWL syntax, have definitions and unique identi-
fiers, and complement (rather than compete with) other OBO ontologies. An
example of a zebrafish anatomy ontology (development of the zygote from
the one-cell stage to the eight-cell stage) in OBO is shown in figure 5.4.
TAMBIS img.cs.man.ac.uk/tambis
TAMBIS is a project that aims to help researchers in biological science by
building a homogenizing layer on top of various biological information ser-
vices. The acronym stands fortransparentaccess tomultiplebiologicalin-
formationsources. The TAMBIS ontology is a semantic network that cov-
ers a wide range of bioinformatics concepts. It aims to provide transparent
information retrieval and filtering from biological information services by
building a homogenizing layer on top of the different sources. This layer
uses a mediator and many source wrappers to create the illusion of one all-
encompassing data source. TAMBIS uses a mediator (information broker)
to achieve this goal. This mediator uses an ontology of molecular biology