ayout 1 - EMBL Grenoble

Structural and Computational Biology Unit
Data integration and knowledge management
Previous and current research
Today it is widely recognised that a comprehensive integration of data can be one of the key factors
to improve productivity and efficiency in the biological research process. Successful data integration
helps researchers to discover relationships that enable them to make better and faster
decisions, thus considerably saving time and money.
Over the last 20 years, biological research has seen a very strong proliferation of data sources. Each
research group and new experimental technique generates a source of valuable data. The creation,
use, integration and warehousing of biological data is central to large-scale efforts in understanding
biological systems. These tasks pose significant challenges from the standpoint of data storage,
indexing, retrieval and system scalability
over disparate types of data.
Examples of the graphical features of Arena3D.
Heterogeneous data types can be visualised in
a 3D environment and a range of layout and
cluster algorithms can be applied.
easily mined, browsed and navigated. By providing access to all scientists
in the organisation, it will foster collaborations between researchers in different
cross-functional groups.
The group is involved in the following areas:
• Data schema design and technical implementation;
• Metadata annotation with respect to experimental data;
• Design and implementation of scientific data portals;
• Providing access to, and developing further, data-mining tools
(e.g. text-mining);
• Visualisation environment for systems biology data.
Reinhard
Schneider
PhD 199, University of
Heidelberg.
Postdoctoral research at
EMBL.
The current systems biology approaches
are generating data sets with
Co-founder and Chief
Information Officer at LION
rapidly growing complexity and dynamics.
One major challenge is to
bioscience AG.
Chief Executive Officer at
provide the mechanism for accessing LION bioscience Research
the heterogeneous data and to detect
the important information. We develop
interactive visual data analysis
Inc., Cambridge, MA.
Team leader at EMBL since
200.
techniques using automatic data
analysis pipelines. The combination of
techniques allows us to analyse otherwise unmanageable amounts of complex data.
The principal aim of the group is to capture and centralise the knowledge generated
by the scientists in the several divisions, and to organise that knowledge such
that it can be
Future projects and goals
Our goal is to develop a comprehensive knowledge platform for the life
sciences. We will first focus on the biology-driven research areas, but will
extend into chemistry-related fields, preliminary by collaborating with groups
inside EMBL. Other research areas will include advanced data-mining and visualisation
techniques.
OnTheFly and Reflect server. Figure (A,B,C) shows an
annotated table (A) of an PDF full text article, the generated
popup window with information about the protein YGL227W
(B), and an automatically generated protein-protein interaction
network (C) of associated entities for the proteins shown in part
(A). Part (D) shows the architecture and functionality.
Selected references
Pavlopoulos, G.A., O’Donoghue, S.I., Satagopam, V.P., Soldatos,
T.G., Pafilis, E. & Schneider, R. (2008). Arena3D: visualization of
biological networks in 3D. BMC Syst. Biol., 2, 10
Erhardt, R.A., Schneider, R. & Blaschke, C. (2006). Status of textmining
techniques applied to biomedical text. Drug Discov. Today,
11, 315-325
Kremer, A., Schneider, R. & Terstappen, G.C. (2005). A
bioinformatics perspective on proteomics: data storage, analysis,
and integration. Biosci. Rep., 25, 95-106
Ofran, Y., Punta, M., Schneider, R. & Rost, B. (2005). Beyond
annotation transfer by homology: novel protein-function prediction
methods to assist drug discovery. Drug Discov. Today, 10, 175-
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