=Paper=
{{Paper
|id=None
|storemode=property
|title=miRNAO: An Ontology for microRNAs
|pdfUrl=https://ceur-ws.org/Vol-897/poster_5.pdf
|volume=Vol-897
|dblpUrl=https://dblp.org/rec/conf/icbo/DritsouTDML12
}}
==miRNAO: An Ontology for microRNAs==
https://ceur-ws.org/Vol-897/poster_5.pdf
miRNAO: An Ontology for microRNAs
Vicky Dritsou 1 , Pantelis Topalis 1 , Emmanuel Dialynas 1 , Elvira Mitraka 1,2 and Christos
Louis 1,2∗
1
Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology - Hellas, Greece
2
Department of Biology, University of Crete, Greece
ABSTRACT in other popular ontologies. A decision we had to take while deve-
MicroRNAs (miRNAs) are short RNA molecules (∼22 nt long) loping miRNAO was to either borrow the existing terms and their
that act as gene regulators in many eukaryotes. By binding to definitions from other ontologies or to create our own concepts and
complementary strands of messenger RNAs (mRNAs), they induce define them regardless of the existing sources. Even though creating
translational repression or target degradation. Since their discovery our own terms would allow us to define them according only to our
in 1993 (Lee et al., 1993), they have received a lot of attention scope, we chose not to re-create those. Instead, we prefer to re-use
in the research area and some thousands of miRNAs have been the existing knowledge by importing them from other ontologies, in
reported in the literature; version 18.0 of miRBase (Kozomara and an attempt to facilitate knowledge sharing and reuse. Interoperabi-
Griffiths-Jones, 2011) contains 18.226 entries representing precur- lity among different databases is then easier achieved. The majority
sor miRNAs in 168 species, which express 21.643 mature miRNA of shared terms come from the Gene Ontology (GO) (Ashburner
products. et al., 2000) and the Sequence Ontology (SO) (Eilbeck et al., 2005).
Each of these small RNA structures regulates many target miRNAO is the first ontology designed to express all the infor-
mRNAs (Baek et al., 2008), while the detection of the targets mation related to miRNAs. Exploiting this ontology in different
is a great challenge for researchers. Besides the number of iden- knowledge bases can be very beneficial, both by extracting infor-
tified miRNAs that grows very fast, the experiments performed mation from each source and also by interchanging and deriving
aiming at detecting targets have also increased. Large data sets information from different sources.
of miRNAs and their experimentally detected targets are sto-
red in various databases. Examples of popular miRNA databases ACKNOWLEDGEMENTS
are microRNA.org (Betel et al., 2008), miRBase (Kozomara and The work was supported by the Hellenic General Secretariat for
Griffiths-Jones, 2011) and TarBase 6.0 (Vergoulis et al., 2011). Research and Technology (MicroRNA project, 09SYN-13-1055)
Sharing knowledge among different knowledge bases is beneficial and, in part, by the VectorBase project (NIAID).
and can be achieved with the use of ontologies. The classification of
the aforementioned huge data sets under an ontology that expresses REFERENCES
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∗ To whom correspondence should be addressed: louis[at]imbb.forth.gr Nucleic Acids Res.
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