Background: The sequence Arginine-Glycine-Aspartic acid (RGD tripeptide) has been
identified in most proteins implicated in cell adhesion and signal transduction. Moreover, the RGD
paradigm extends to the plant and microbial kingdoms. Investigating this field can be facilitated by
combining data from multiple databases into a single one. The RGD tripeptide database is a
comprehensive resource with records including general annotation, ontology, database cross-references,
sequence and structure data.
Objective: In this work, we present the integration of a novel visualization tool within the RGDtrip 1.0
version data collection and retrieval environment for proteins containing the RGD tripeptide. This
approach allows state-of-the-art data querying combined with an advanced, user-friendly visualization
Method: The overall system architecture is based on a three-tier client-server model, thus comprising
three main components: the client application, the application server and the database server. The
underlying structure of RGDtrip is a relational database developed with Microsoft SQL Server. All the
data compiled in RGDtrip were originally scattered in other data bases, such as UNIProt, PDBdb, etc.
has been incorporated into a visualization tool based on the Microsoft’s PivotViewer software. The tool
enables users to see data under many different perspectives and thus to gain a better aspect and
understanding of them.
Results: The RGDtrip database may be used for the investigation of proteins containing the RGD
tripeptide and the shaping of meaningful conclusions regarding, among other things, evolution,
phylogenesis and pharmacological interactions with disease- implicated entities and possible loci of
side-effects. The RGDtrip database offers the following main advantages: (i) a collection of about
32,000 proteins containing the RGD tripeptide in just one database and through a unique user interface;
(ii) the utilization of state-of-the-art technologies to deliver new data querying and visualization tools
for scientists, thus allowing Visual Data Mining, for both basic and applied research on the above
Conclusion: This paper describes the integration of existing information with advanced visualization
and querying tools, in a dedicated database to implement Visual Data Mining, for basic and applied
research on RGD-containing proteins.