=Paper=
{{Paper
|id=None
|storemode=property
|title=Ondex: data integration and visualisation for the Semantic Web
|pdfUrl=https://ceur-ws.org/Vol-559/Poster3.pdf
|volume=Vol-559
|dblpUrl=https://dblp.org/rec/conf/swat4ls/CanevetSKSLR09
}}
==Ondex: data integration and visualisation for the Semantic Web==
https://ceur-ws.org/Vol-559/Poster3.pdf
Ondex: data integration and visualisation for the
Semantic Web
Catherine Canevet1, Andrea Splendiani1, Steve Kuo1,3, Robert Stevens2, Phil Lord3,
Chris Rawlings1
1
Centre for Mathematical and Computational Biology, Rothamsted Research, Harpenden, UK
2
School of Computer Science, University of Manchester, Manchester, UK
3
School of Computing Science, University of Newcastle, Newcastle, UK
Many systems approaches to biology need to identify, integrate and analyse
information that is captured in a myriad of databases which use a wide variety of
different formats and access methods.
The Ondex data integration platform [1] (www.ondex.org) enables data from diverse
biological data sets to be linked together, integrated, analysed and visualised using
graph-based techniques. At the basis of Ondex is a graph data structure where entities
and properties are associated to classes [2]. This data structure is closely related to the
data model of RDF and supports a limited representation of ontologies.
In the context of the SABR project (http://www.ondex.org/sabr.html), we are
investigating ways to create a mapping between the Ondex data structure and the
RDF/OWL. Our objective is to allow Ondex to query, visualise and analyse Semantic
Web based knowledge bases [3]. The challenges are to maintain acceptable
performance when translating between the RDF and Ondex data models. Once this
mapping is implemented, Ondex could also be used to build workflows for the
curation and management of such knowledge bases.
The Ondex data model shares many similarities with RDF. For example, it has
equivalents for Object Properties, Data Type properties, Types and Named graphs. On
the other hand, one of the differences is that there are no global identities such as
URIs available in Ondex. In fact, different information sources are aggregated by
representing the original entities in “blank nodes” first and then by computing
mappings among these nodes using their attributes.
We wish to devise a mapping strategy that will allow Semantic Web based knowledge
bases to be exposed using the Ondex data model because of the rich feature set that
Ondex supports. Among the features already implemented in Ondex that could be
deployed on a generic Semantic Web based knowledge bases are:
‐ Support for the definition of workflows using an Ondex-specific engine, via
Taverna or a scripting interface
‐ Methods to compute mappings between information resources, based on lexical
information, unique identifiers, sequence similarity or text mining (mapping free
text to entities and relations using information extraction).
‐ Methods to perform network analysis using betweenness and degree centrality
measures (how influential an entity is in the network, how many relations it has)
as well as a statistics module.
‐ Methods to interactively explore the content of integrated datasets as shown in
Figure 1. A set of filters help users narrow down their integrated data sets to
regions of interest in the network.
Fig. 1. Screenshots of the Ondex Visualisation ToolKit. Various filters and annotators allow
users to visualise and analyse their datasets, integrated using Ondex’s mapping methods.
Top-left: AraCyc, DRASTIC and microarray data. Pie charts show when genes are up/down
regulated (red/green) given the selected treatments.
Top-right: Poplar genome sequence, PoplarCyc, UniProt, Pfam, GO, GOA and Medline. The
genomic view allows user to select QTLs of interest and search their neighbourhood based on
keywords and a given neighbourhood depth.
Bottom-left: GOA, UniProt, Medline and GO of Arabidopsis thaliana. Data integration and
literature analysis methods are used to predict the function of previously unannotated genes.
Bottom-right: TAIR, IntAct and BioGRID. The resulting network shows the Arabidopsis
interactome. The threshold filter displays the distribution of the degree centrality calculated for
each protein and added to each protein as an attribute (previously by an annotator).
As a reference use case, we have started the investigation of how Ondex can be used
to interact with BioGateway [4], which implements a Sparql endpoint. Apart from
creating a mapping that would make BioGateway accessible in Ondex, we are
defining a set of guidelines to harmonise the development of Ondex modules to
ensure a common representation compliant with this mapping [5].
References
1. Köhler, J., Baumbach, J., Taubert, J., Specht, M., Skusa, A., Ruegg, A., Rawlings, C.,
Verrier, P., Philippi, S.: Graph-based analysis and visualization of experimental results with
Ondex. Bioinformatics 22 (11):1383-1390 (2006).
2. Taubert, J., Sieren, K.P., Hindle, M., Hoekman, B., Winnenburg, R., Philippi, S., Rawlings,
C., Köhler, J.: The OXL format for the exchange of integrated datasets. Journal of
Integrative Bioinformatics 4, 62 (2007).
3. Antezana, E., Kuiper, M., Mironov, V.: Biological knowledge management: the emerging
role of the Semantic Web technologies. Briefings in Bioinformatics 10, 392--407 (2009).
4. Antezana, E., Blondé, W., Egaña, M., Rutherford, A., Stevens, R., De Baets, B., Mironov,
V., Kuiper, M.: BioGateway: a semantic systems biology tool for the life sciences. BMC
Bioinformatics 10 (Suppl 10):S11 (2009).
5. Splendiani, A., Kuiper, M., Rawlings, C.: Toward a new paradigm for user interaction on
the Semantic Web to support life sciences investigation. To appear in Proceedings of ISWC-
SWUI (CEUR) (2009).