Abstract
BackgroundBiological processes such as metabolic pathways, gene regulation or protein-protein interactions are often represented as graphs in systems biology. The understanding of such networks, their analysis, and their visualization are today important challenges in life sciences. While a great variety of visualization tools that try to address most of these challenges already exists, only few of them succeed to bridge the gap between visualization and network analysis.FindingsMedusa is a powerful tool for visualization and clustering analysis of large-scale biological networks. It is highly interactive and it supports weighted and unweighted multi-edged directed and undirected graphs. It combines a variety of layouts and clustering methods for comprehensive views and advanced data analysis. Its main purpose is to integrate visualization and analysis of heterogeneous data from different sources into a single network.ConclusionsMedusa provides a concise visual tool, which is helpful for network analysis and interpretation. Medusa is offered both as a standalone application and as an applet written in Java. It can be found at: https://sites.google.com/site/medusa3visualization.
Highlights
Biological processes such as metabolic pathways, gene regulation or protein-protein interactions are often represented as graphs in systems biology
Medusa provides a concise visual tool, which is helpful for network analysis and interpretation
Medusa is offered both as a standalone application and as an applet written in Java
Summary
Biological processes such as metabolic pathways, gene regulation or protein-protein interactions are often represented as graphs in systems biology. The understanding of such networks, their analysis, and their visualization are today important challenges in life sciences. While a great variety of visualization tools that try to address most of these challenges already exists, only few of them succeed to bridge the gap between visualization and network analysis. The analysis and interpretation of complex relationships between biological molecules, networks and concepts presents a major bottleneck in systems biology. Different types of networks such as protein-protein interaction (PPI) networks, biochemical networks, transcriptional regulation networks, signal transduction or metabolic networks are significantly different in structure but often share characteristics and properties that need to be further explored in detail. The simultaneous analysis of heterogeneous networks within the same view
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