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In Silico Biology 7, 0004 (2006); ©2006, Bioinformation Systems e.V.  



MOVE: A Multi-level Ontology-based Visualization and Exploration framework for genomic networks

Diederik W. J. Bosman1, Evert-Jan Blom2, Patrick J. Ogao1, Oscar P. Kuipers2 and Jos B. T. M. Roerdink1,*

1 Institute for Mathematics and Computing Science, University of Groningen, Netherlands
2 Department of Molecular Genetics, University of Groningen, Netherlands



* Corresponding author
   Email: j.b.t.m.roerdink@rug.nl


Edited by E. Wingender; received August 03, 2006; revised and accepted November 23, 2006; published December 26, 2006


Abstract

Among the various research areas that comprise bioinformatics, systems biology is gaining increasing attention. An important goal of systems biology is the unraveling of dynamic interactions between components of living cells (e. g., proteins, genes). These interactions exist among others on genomic, transcriptomic, proteomic and metabolomic levels. The levels themselves are heavily interconnected, resulting in complex networks of different interacting biological entities. Currently, various bioinformatics tools exist which are able to perform a particular analysis on a particular type of network. Unfortunately, each tool has its own disadvantages hampering it to be used consistently for different types of networks or analytical methods. This paper describes the conceptual development of an open source extensible software framework that supports visualization and exploration of highly complex genomic networks, like metabolic or gene regulatory networks. The focus is on the conceptual foundations, starting from requirements, a description of the state of the art of network visualization systems, and an analysis of their shortcomings. We describe the implementation of some initial modules of the framework and apply them to a biological test case in bacterial regulation, which shows the relevance and feasibility of the proposed approach.


Keywords: gene regulatory networks, prokaryotes, visualization framework, information exploration