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Vasily Grinev
Dmitry Kushal
Vitaly Charapovich


Summary: A simple-to-use Java-based software package CelNetAnalyzer was developed. CelNetAnalyzer is managed through a graphical user interface and it returns a comprehensive list of the topological indices including compositional complexity, degree and neighbourhood, clustering, distance, centrality and heterogeneity indices as well as simple cycles and Shannon information entropy of undirected networks. Comparative studies have shown that due to parallelization and use of enhanced and newly developed algorithms, CelNetAnalyzer calculates these parameters significantly faster than competitors.

Availability and Implementation: CelNetAnalyzer is an open-source project and free distributed for non-commercial use. Software package, source code, test network and the results of the topological analysis can be downloaded from website of the Department of Genetics at Belarusian State University (http://bio.bsu.by/genetics/grinev_software.html).

Supplementary information: Supplementary data are available at Journal of Bioinformatics and Genomics online

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How to Cite
GRINEV, Vasily; KUSHAL, Dmitry; CHARAPOVICH, Vitaly. CELNETANALYZER: HIGH-PERFORMANCE JAVA PACKAGE FOR THE TOPOLOGICAL ANALYSIS OF CELLULAR NETWORKS. Journal of Bioinformatics and Genomics, [S.l.], n. 1 (3), may 2017. ISSN 2530-1381. Available at: <http://journal-biogen.org/article/view/53>. Date accessed: 23 jan. 2018. doi: http://dx.doi.org/10.18454/jbg.2017.1.3.3.
Novel computational tools and databases
Aric, A.A., Schult, D.A. and Swart, P.J. (2008). Exploring network structure, dynamics, and function using NetworkX. In: Varoquaux G., Vaught T., Millman J. (eds). Proceedings of the 7th Python in Science Conference (SciPy 2008). Pasa-dena, USA, pp. 11-15.
Assenov, Y., Ramírez, F., Schelhorn, S.E., Lengauer, T. and Albrecht, M. (2008). Computing topological parameters of biological networks. Bioinformatics, 24, 282-284. doi: 10.1093/bioinformatics/btm554
Barabási, A. L. and Oltvai, Z. N. (2004) Network biology: understanding the cell's functional organization. Nat. Rev. Genetic., 5, 101–113.
Batagelj, V. and Mrvar, A. (1998). Pajek – program for large network analysis. Connections, 21, 47-57.
Bonchev, D. and Buck, G. A. (2005) Quantitative measures of network complexity. In: Bonchev, D. and Rou-vray, D. H. (eds). Complexity in chemistry, biology and ecol-ogy. Springer, New York, pp. 191–235.
Brandes, U. (2001) A faster algorithm for betweenness centrality. // J. Math. Sociol., 25, 163–177.
Chatr-Aryamontri, A., Breitkreutz, B.J., Heinicke, S., Boucher, L., Winter, A., Stark, C., Nixon, J., Ramage, L., Kolas, N., O’Donnell, L., Reguly, T., Breitkreutz, A., Sellam, A., Chen, D., Chang, C., Rust, J., Livstone, M., Oughtred, R., Dolinski, K. and Tyers, M. (2013). The BioGRID interaction database: 2013 update. Nucleic Acids Research, 41, D816-D823. doi: 10.1093/nar/gks1158
Csardi, G. and Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Com-plex Systems, 1695.
Del Rio, G. et al. (2009) How to identify essential genes from molecular networks? BMC Sys. Biol., 3, 102. DOI:10.1186/1752–0509–3–102.
Diestel, R. (2005) Graph theory. Springer-Verlag, Heidel-berg, ISBN 3–540–26182–6.
Dong, J. and Horvath, S. (2007) Understanding network concepts in modules. BMC Sys. Biol., 1, 24. DOI:10.1186/1752–0509–1–24.
Freeman, L. C. (1977) A set of measures of centrality based on betweenness. Sociometry, 40, 35–41.
Freeman, L. C. (1978) Centrality in social networks. Con-ceptual clarification. Soc. Networks, 79, 215–239.
Hartley, R. V. L. (1928) Transmission of information. Bell Sys. Tech. J., 7, 535–563.
Hu, H.–B. and Wang, X.–F. (2008) Unified index to quan-tifying heterogeneity of complex networks. Physica A, 387, 3769–3780.
MacNeil, L.T. and Walhout, A.J.M. (2011). Gene regula-tory networks and the role of robustness and stochasticity in the control of gene expression. Genome Research, 21, 645-657. doi: 10.1101/gr.097378.109
Margolin, A.A., Wang, K., Lim, W.K., Kustagi, M., Ne-menman, I. and Califano, A. (2006). Reverse engineering cellular networks. Nature Protocols, 1, 663-672. doi: 10.1038/nprot.2006.106
Maslov, S. and Sneppen, K. (2002) Specificity and stabil-ity in topology of protein networks. Science, 296, 910–913.
Newman, M. E. J. (2005) A measure of betweenness cen-trality based on random walks. Soc. Networks, 27, 39–54.
Newman, M.E.J. (2010). Networks. An introduction. Ox-ford University Press, USA, 784 pp.
Shannon, C. E. (1948) A mathematical theory of commu-nication. Bell Sys. Tech. J., 27, 379–423, 623–656.
Soffer, S. N. and Vazquez, A. (2005) Network clustering coefficient without degree-correlation biases. Phys. Rev., 71, 057101–1–057101–4.
Stelzl, U. et al. (2005) A human protein-protein interaction network: a resource for annotating the proteome. Cell, 122, 957–968.
Szklarczyk, D., Franceschini, A., Kuhn, M., Simonovic, M., Roth, A., Minguez, P., Doerks, T., Stark, M., Muller, J., Bork, P., Jensen, L.J. and von Mering, C. (2011). The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Re-search, 39, D561-568. doi: 10.1093/nar/gkq973
Watts, D. J. and Strogatz, S. H. (1998) Collective dynam-ics of ?small-world’ networks. Nature, 393, 440–442.
Yoon, J. et al. (2006) An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality. Bioinformatics, 22, 3106–3108.