Abstract
Data from high-throughput experimental methods are currently being used to construct complex biological networks. These include regulatory gene networks, regulatory protein–DNA networks, protein–protein interaction networks, or metabolic networks. Independent of its type, every network can be characterized by a number of parameters such as number of nodes, number of edges connecting nodes, direction and weight of edges, in- and out-degree of nodes, etc. One can draw an analogy of such rather simple network parameters to the primary sequence of proteins or nucleic acids. More insight can be gained by an analysis of the secondary and tertiary structure of biomolecules, which often contain motifs. The same holds for biological networks. The occurrence and frequency of certain motifs or pattern characterize the topology and often the functional space of a network. Here, we describe the utilization of the free software MAVisto, which was designed to mine networks for typical motifs by combining a flexible motif search algorithm with interactive exploration methods and sophisticated visualization techniques.
Key words
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cori C, Cori G. (1929) Glycogen formation in the liver from d- and l-lactic acid. J Biol Chem, 81:389–403.
Billson C. A history of the London tube maps 2010. [http://homepage.ntlworld.com/clivebillson/tube/tube.html].
Appel R, Bairoch A, Hochstrasser D. (1994) A new generation of information retrieval tools for biologists: the example of the ExPASy WWW server. Trends Biochem Sci, 19:258260.
Michal G. (1999) Biochemical Pathways. Wiley-Spektrum, New York/Heidelberg.
Caspi R, Altman T, Dale JM, Dreher K, Fulcher CA, Gilham F, Kaipa P, Karthikeyan AS, Kothari A, Krummenacker M, Latendresse M, Mueller LA, Paley S, Popescu L, Pujar A, Shearer AG, Zhang P, Karp PD. (2008) The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res, 36:D623–D631.
Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita K, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 28:27–30.
Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. (2010) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res, 38:D355–D360.
Friedland A, Lu T, Wang X, Shi D, Church G, Collins J. (2009) Synthetic gene networks that count. Science, 324:1199–1202.
Mangan S, Alon U. (2003) Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci U S A, 100:1198011985.
Lee T, Rinaldi N, Robert F, Odom D, Bar-Joseph Z, Gerber G, Hannett N, Harbison C, Thompson C, Simon I, Zeitlinger J, Jennings E, Murray H, Gordon D, Ren B, Wyrick J, Tagne J, Volkert T, Fraenkel E, Gifford D, Young R. (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 298:799–804.
Martínez-Antonio A, Janga SC, Thieffry D. (2008) Functional organisation of Escherichia coli transcriptional regulatory network. J Mol Biol, 381:238–247.
Schreiber F, Schwöbbermeyer H. (2005) MAVisto: a tool for the exploration of network motifs. Bioinformatics, 21:3572–3574.
Schreiber F, Schwöbbermeyer H. (2005) Frequency concepts and pattern detection for the analysis of motifs in networks. Trans Comput Syst Biol, 3:89–104.
Fruchterman T, Reingold E. (2000) Graph drawing by force-directed placement. Software Pract Ex, 30:1303–1324.
Koschützki D, Schwöbbermeyer H, Schreiber F. (2007) Ranking of network elements based on functional substructures. J Theor Biol, 248(3):471–479.
Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U. (2002) Network motifs: simple building blocks of complex networks. Science, 298:824827.
Maslov S, Sneppen K. (2002) Specificity and stability in topology of protein networks. Science, 296:910–913.
Shen-Orr S, Milo R, Mangan S, Alon U. (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet, 31:64–68.
Gama-Castro S, Jiménez-Jacinto V, Peralta-Gil M, Santos-Zavaleta A, naloza Spinola MIP, Contreras-Moreira B, Segura-Salazar J, niz Rascado LM, Martínez-Flores I, Salgado H, Bonavides-Martínez C, Abreu-Goodger C, Rodríguez-Penagos C, Miranda-Ríos J, Morett E, Merino E, Huerta AM, no Quintanilla LT, Collado-Vides J. (2008) RegulonDB (version 6.0): gene regulation model of Escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation. Nucl Acid Res, 36:D120D124.
Holleis P, Zimmermann T, Gmach D. (2005) Drawing graphs within graphs. J Graph Alg Appl, 9:7–18.
Klukas C, Koschützki D, Schreiber F. (2005) Graph pattern analysis with PatternGravisto. J Graph Alg Appl, 9:19–29.
Wünschiers R. (2004) Computational Biology: Unix, Linux, Data Processing and Programming. Springer, New York.
Batagelj V, Mrvar A. (1998) Pajek – program for large network analysis. Connections, 21:47–57.
Klukas C, Schreiber F, Schw¨obbermeyer H. (2006) Coordinated Perspectives and Enhanced Force-Directed Layout for the Analysis of Network Motifs. In Asia Pacific Symposium on Information Visualisation (APVIS2006), Volume 60 of Conferences in Research and Practice in Information Technology (CRPIT). Edited by Misue K, Sugiyama K, Tanaka J, ACS, Tokyo, 39–48.
Acknowledgments
The authors like to thank Professor Dr. Falk Schreiber, head of the Bioinformatics group at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) in Gatersleben/Germany, and his team for support and hosting of MAVisto.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Schwöbbermeyer, H., Wünschiers, R. (2012). MAVisto: A Tool for Biological Network Motif Analysis. In: van Helden, J., Toussaint, A., Thieffry, D. (eds) Bacterial Molecular Networks. Methods in Molecular Biology, vol 804. Springer, New York, NY. https://doi.org/10.1007/978-1-61779-361-5_14
Download citation
DOI: https://doi.org/10.1007/978-1-61779-361-5_14
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-61779-360-8
Online ISBN: 978-1-61779-361-5
eBook Packages: Springer Protocols