Medusa: A tool for exploring and clustering biological networks

BMC Research Notes

Jan Aerts²  Reinhard Schneider⁴  Alejandro Sifrim²  Sean D Hooper¹  Georgios A Pavlopoulos³ 

[1] Department of Genetics and Pathology, Uppsala University, SE-751 85 Uppsala, Sweden;Katholieke Universiteit Leuven, Faculty of Engineering - ESAT/SCD, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium;European Molecular Biology Laboratory (EMBL), Structural and Computational Biology, Meyerhofstrasse 1, 69117, Heidelberg, Germany;Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Limpertsberg, 162A, Avenue de la Faïencerie, L-1511, Luxembourg

关键词: data integration;    clustering analysis;    biological networks;    visualization;    graph;   

Others  :  1167128
DOI  :  10.1186/1756-0500-4-384

 received in 2011-05-31, accepted in 2011-10-06,  发布年份 2011

【 摘 要 】

Background

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.

Findings

Medusa 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.

Conclusions

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. It can be found at: https://sites.google.com/site/medusa3visualization webcite.

【 授权许可】

   
2011 Pavlopoulos et al; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

【 参考文献 】

• [1]Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J, Schneider R, Bagos PG: Using graph theory to analyze biological networks. BioData Min 2011, 4(1):10. BioMed Central Full Text
• [2]Pavlopoulos GA, Wegener AL, Schneider R: A survey of visualization tools for biological network analysis. BioData Min 2008, 1:12. BioMed Central Full Text
• [3]Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003, 13(11):2498-2504.
• [4]Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD: Cytoscape Web: an interactive web-based network browser. Bioinformatics 2010, 26(18):2347-2348.
• [5]Breitkreutz BJ, Stark C, Tyers M: Osprey: a network visualization system. Genome Biol 2003, 4(3):R22. BioMed Central Full Text
• [6]Kohler 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 2006, 22(11):1383-1390.
• [7]Hooper SD, Bork P: Medusa: a simple tool for interaction graph analysis. Bioinformatics 2005, 21(24):4432-4433.
• [8]Pavlopoulos GA, O'Donoghue SI, Satagopam VP, Soldatos TG, Pafilis E, Schneider R: Arena3D: visualization of biological networks in 3D. BMC Syst Biol 2008, 2:104. BioMed Central Full Text
• [9]Batagelj V, Mrvar A: Pajek - Program for Large Network Analysis. Connections 1998, 21:47-57.
• [10]Goldovsky L, Cases I, Enright AJ, Ouzounis CA: BioLayout(Java): versatile network visualisation of structural and functional relationships. Appl Bioinformatics 2005, 4(1):71-74.
• [11]ClusterMaker [http://www.rbvi.ucsf.edu/cytoscape/cluster/clusterMaker.html] webcite
• [12]Maere S, Heymans K, Kuiper M: BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 2005, 21(16):3448-3449.
• [13]Pavlopoulos GA, Seán IOD, Venkata PS, Soldatos T, Pafilis E, Schneider R: Arena3D: visualization of biological networks in 3D. BMC Syst Biol 2008., 2
• [14]Fruchterman TMJ, Reingold EM: Graph Drawing by Force-Directed Placement. Software, Practice and Experience 1991, 21:1129-1164.
• [15]Crippen GM, Havel TF: Distance Geometry and Molecular Conformation. New York: Wiley; 1988.
• [16]Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000, 25(1):25-29.
• [17]Frey BJ, Dueck D: Clustering by passing messages between data points. Science 2007, 315(5814):972-976.
• [18]MacQueen JB: Kmeans Some Methods for classification and Analysis of Multivariate Observations. In 5-th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley University of California Press; 1967:281-297.
• [19]Paccanaro A, Casbon JA, Saqi MA: Spectral clustering of protein sequences. Nucleic Acids Res 2006, 34(5):1571-1580.
• [20]Gavin AC, Aloy P, Grandi P, Krause R, Boesche M, Marzioch M, Rau C, Jensen LJ, Bastuck S, Dumpelfeld B, et al.: Proteome survey reveals modularity of the yeast cell machinery. Nature 2006, 440(7084):631-636.
• [21]Moschopoulos CN, Pavlopoulos GA, Likothanassis SD, Kossida S: An enhanced Markov clustering method for detecting protein complexes. 8st IEEE International Conference on Bioinformatics and Bioengineering: 8-10 October 2008, 2008; Athens, Greece
• [22]Pavlopoulos GA, Moschopoulos CN, Hooper SD, Schneider R, Kossida S: jClust: a clustering and visualization toolbox. Bioinformatics 2009, 25(15):1994-1996.
• [23]Satagopam VP, Theodoropoulou MC, Stampolakis CK, Pavlopoulos GA, Papandreou NC, Bagos PG, Schneider R, Hamodrakas SJ: GPCRs, G-proteins, effectors and their interactions: human-gpDB, a database employing visualization tools and data integration techniques. Database (Oxford) 2010, 2010:baq019.
• [24]von Mering C, Jensen LJ, Kuhn M, Chaffron S, Doerks T, Krüger B, Snel B, Bork P: STRING 7-recent developments in the integration and prediction of protein interactions. Nucleic Acids Res 2006., 35(D358-62)
• [25]Hooper SD, Anderson IJ, Pati A, Dalevi D, Mavromatis K, Kyrpides NC: Integration of phenotypic metadata and protein similarity in Archaea using a spectral bipartitioning approach. Nucleic Acids Res 2009, 37(7):2096-2104.
• [26]Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, et al.: The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 2003, 19(4):524-531.
• [27]Hermjakob H, Montecchi-Palazzi L, Bader G, Wojcik J, Salwinski L, Ceol A, Moore S, Orchard S, Sarkans U, von Mering C, et al.: The HUPO PSI's molecular interaction format--a community standard for the representation of protein interaction data. Nat Biotechnol 2004, 22(2):177-183.
• [28]Processing [http://processing.org/] webcite