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Tae-Hyung Kim1
[email protected]
InSong Koh2
[email protected]
Gil-Mi Ryu1,2
[email protected]
Jong Park3
[email protected]
1 Department of Bioinformatics, Bioinformatics Cooperative Course, Pusan National University, Pusan, Korea
2 Section of Bioinformatics, Central Genome Center, National Institute of Health, Nokbun-Dong 5, Seoul, Korea
3 MRC-DUNN, Hills Road Cambridge CB2, 2XY, England, UK
1 Introduction
One of the major obstacle of bioinformatics is the difficulty in computation
with literature information. Unlike sequence and structure, it is impossible to
establish homology, similarity, interaction and function criteria for literature
information. To ease this problem, attempts to clarify the ontological
problems have become bioinformatic projects. The idea of ontology is to
define terms and concepts in a mechanical and computable units. The result
will be clear classification and mapping of text elements for computers. We
have applied this ontological advantage of classifying elements to the very
bioinformatics field. This project has an important merit of efficient
understanding and dissemination of bioinformatics knowledge to this fast
growing field. Any intuitive classification system of bioinformatics itself can
provide us with valuable project ideas and future directions. There are three
main components of ontology of bioinformatics field: 1) classification based
on methodology, 2) knowledge based classification (database systems) and 3)
classification based on biological data types. These components overlap and
they are different aspects of the same or similar information. However,
depending on the users interest, the certain view can be more relevant to
design and organize a bioinformatics project
2 Method and Results
2.1Classification based on methodology.
We tried to classify bioinformatics field according to analysis method of
biological data(DNA, RNA, Protein). In this way, bioinformatics can be
understood intuitively through a schematic map.
2.3 Classification based on biological data types
We categorized the component fields of bioinformatics according to the
implementation types used by the biologists after data acquisition. We
differentiate them by the common procedures used and tools applied to the
biological knowledge, which is a usual procedure carried out by biologists
Figure 5. Classification according to bi
ological data types. According to this cl
assification map, biological data can be
identified through prediction of
sequence structure and function. As
information acquired from data flows
from right to left, it becomes more and
more clear.
3 Discussion
In this classification of the components of bioinformatics, we introduced our o
ntology schema in classifying and mapping the bioinformatics field itself. This
ontological procedure was designed to represent the methodology, features of
databases and data content. So it allows us to find projects and relate the
problem domain in bioinformatics in the much more systematic way. Also it ca
n be used to cluster biological sequence data based on their bioinformatics
ontology characteristics and it can provide us computation on the specific
elements such as sequence and database.
In addition, schematic maps are drawn to show a visual tree so that one can get
the global picture on bioinformatics field, and obtain more precise information
intuitively and efficiently. The lower levels of each classification criterion is lin
ked
to
the
web
pages.(http://nihcgc.re.kr/BioinfoMap
and
http://interaction.mrc-dunn.cam.ac.uk/BioinfoMap/). The classification system
is still being developed and will be stored in an SQL based database for more
dynamic navigation between different component concepts of bioinformatics
field.
Acknowledgement
Figure 1. main window
Figure 2. sub windows
Figure 3. Ontological classification
based
on
methodology.
The
methodology for DNA sequence
determination
can
be
classified
according to work procedure such as
mapping, sequencing, assembly, and
searching. RNA analysis is classified
according to cDNA chip procedure
resulting in expression analysis. Protein
analysis methodology can be classified
as comparative and predictive methods.
2.2 Knowledge based classification (database systems).
These databases can be classified according to data features, thus classified
as 1) sequence, 2) protein, 3) metabolic pathway, 4) organism and 5) RNA
groups.
Figure 4. Classification of databases.
Biological databases can be classified
according to the data features. The
popular databases used in the biological
community were included in this
schematic map..
We thank Mi-Ae Yoo and Heui-Soo Kim(Pusan National University) for
support. This work was funded in part by the Bioinformatics Training Grant of
Ministry of Health & Welfare, Korea and supported by Pusan National
University, Korea and MRC, UK
References
[1] Patricia G. Baker, Carole A. Goble, Sean Bechhofer, Norman W. Paton,
Robert Stevens, Andy Brass, An ontology for bioinformatics applications,
Bioinformatics vol 15, no 6, 510-520, 1999
[2] Robert Stevens, Patricia Baker, Sean Bechhofer, Gary Ng, Alex Jacoby,
Norman W. Paton, Carole A. Goble, Andy Brass, TAMBIS: Transparent
Access to Multiple Bioinformatics Information Sources, Bioinformatics
vol. 16 no. 2, 184-185, 2000
[3] Andreas D. Baxevanis, The Molecular Biology Database Collection: an
online compilation of relevant database resources, Nucleic Acid Research,
vol. 28. No. 1, 2000
[4] The Gene Ontology Consortium, Gene ontology : Tool for the unification
of biology, Nature America Inc
http://genetics.nature.com., nature genetics volume 25, 2000