Generic placeholder image

Current Genomics

Editor-in-Chief

ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

Research Article

CatbNet: A Multi Network Analyzer for Comparing and Analyzing the Topology of Biological Networks

Author(s): Ehsan Pournoor, Naser Elmi and Ali Masoudi-Nejad*

Volume 20, Issue 1, 2019

Page: [69 - 75] Pages: 7

DOI: 10.2174/1389202919666181213101540

Price: $65

Abstract

Background: Complexity and dynamicity of biological events is a reason to use comprehensive and holistic approaches to deal with their difficulty. Currently with advances in omics data generation, network-based approaches are used frequently in different areas of computational biology and bioinformatics to solve problems in a systematic way. Also, there are many applications and tools for network data analysis and manipulation which their goal is to facilitate the way of improving our understandings of inter/intra cellular interactions.

Methods: In this article, we introduce CatbNet, a multi network analyzer application which is prepared for network comparison objectives.

Result and Conclusion: CatbNet uses many topological features of networks to compare their structure and foundations. One of the most prominent properties of this application is classified network analysis in which groups of networks are compared with each other.

Keywords: Network biology, Topological features, Bioinformatics, Python, Network comparing, Biological researches.

« Previous
Graphical Abstract
[1]
Freeman, L.C. Going the wrong way on a one-way street: Centrality in physics and biology. J. Soc. Struct., 2008, 9(2), 1-15.
[2]
Jeong, H. Lethality and centrality in protein networks. Nature, 2001, 411(6833), 41-42.
[3]
He, X.; Zhang, J. Why do hubs tend to be essential in protein networks? PLoS Genet., 2006, 2(6), e88.
[4]
Jalili, M.; Salehzadeh-Yazdi, A. A; Asgari. Y; Arab, S.S.; Yaghmaie, M.; Ghavamzadeh A.; Alimoghaddam, K . CentiServer: a comprehensive resource, web-based application and R package for centrality analysis. PloS. One., 2015, 10(1), e0143111.
[5]
Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; 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.
[6]
Assenov, Y.; Ramírez, F.; Schelhorn, S.E.; Lengauer, T.; Albrecht, M. Computing topological parameters of biological networks. Bioinformatics, 2007, 24(2), 282-284.
[7]
Chin, C.H.; Chen, S.H.; Wu, H.H.; Ho, C.W.; Ko, M.T.; Lin, C.Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Sys. Biol., 2014, 8(4), S11.
[8]
Tang, Y.; Li, M.; Wang, J.; Pan, Y.; Wu, F.X. CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems, 2015, 127( 2015), 67-72.
[9]
Junker, B.H.; Koschützki, D.; Schreiber, F. Exploration of biological network centralities with CentiBiN. BMC Bioinformatics, 2006, 7(219), 193-201.
[10]
Ashtiani, M.; Jafari, M. CINNA: Deciphering central informative nodes in network analysis. BioRxiv, 2017, 27(168757), 1-9.
[11]
Theodosiou, T.; Efstathiou, G.; Papanikolaou, N.; Kyrpides, N.C. Bagos, P.G.; Iliopoulos, I.; Pavlopoulos, G.A. NAP: The network analysis profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks. BMC. Res., 2017, 10(1), 278.
[12]
Hagberg, A.; Swart, P.S.; Chult, D. Exploring network structure, dynamics, and function using NetworkX. Los Alamos National Laboratory; LANL, 2008.
[13]
Barabasi, A.L.; Gulbahce, N.; Loscalzo, J. Network medicine: A network-based approach to human disease. Nat. Rev. Genet., 2011, 12(1), 56-68.
[14]
Loscalzo, J. Network Medicine; Harvard University Press, 2017.
[15]
Rolland, T.; Taşan, M.; Charloteaux, B.; Pevzner, S.J.; Zhong, Q. Sahni, N.; Yi, S.; Lemmens, I.; Fontanillo, C.; Mosca, R.; Kamburov, A.; Ghiassian, S.D.; Yang, X.; Ghamsari, L.; Balcha, D.; Begg, B.E.; Braun, P.; Brehme, M.; Broly, M.P.; Carvunis, A.R.; Convery-Zupan, D.; Corominas, R.; Coulombe-Huntington, J. Dann, E.; Dreze, M; Dricot, A.; Fan, C.; Franzosa, E.; Gebreab, F.; Gutierrez, B.J.; Hardy, M.F.; Jin, M.; Kang, S.; Kiros, R.; Lin, G.N.; Luck, K.; MacWilliams, A.; Menche, J.; Murray, R.R.; Palagi, A.; Poulin, M.M.; Rambout, X.; Rasla, J.; Reichert, P.; Romero, V.; Ruyssinck, E.; Sahalie, J.M.; Scholz, A.; Shah, A.A.; Sharma, A.; Shen, Y.; Spirohn, K.; Tam, S.; Tejeda, A.O.; Trigg, S.A.; Twizere, J.C.; Vega, K.; Walsh, J.; Cusick, M.E.; Xia, Y.; Barabási, A.L.; Iakoucheva, L.M.; Aloy, P.; De Las Rivas, J.; Tavernier, J.; Calderwood, M.A.; Hill, D.E.; Hao, T.; Roth, F.P.; Vidal, M. A Proteome-scale map of the human interactome network. Cell, 2014, 159(5), 1212-1226.
[16]
Guney, E.; Menche, J.; Vidal, M.; Barábasi, A.L. Network-based in silico drug efficacy screening. Nat. Comm., 2016, 7(10331), 1-13.
[17]
Cheng, F.; Liu, C.; Shen, B.; Zhao, Z. Investigating cellular network heterogeneity and modularity in cancer: A network entropy and unbalanced motif approach. BMC Syst. Biol., 2016, 10(65), 11-16.
[18]
West, J.; Bianconi, G.; Severini, S.; Teschendorff, A.E. Differential network entropy reveals cancer system hallmarks. Sci. Rep., 2012, 2(1), 802.
[19]
de Anda-Jáuregui, G.; Velázquez-Caldelas, T.E.; Espinal-Enríquez, J.; Hernández-Lemus, E. Transcriptional network architecture of breast cancer molecular subtypes. Front. Physiol., 2016, 7(1), 568.
[20]
Jalili, M. Graph theoretical analysis of Alzheimer’s disease: Discrimination of AD patients from healthy subjects. Information Sciences.., 2017, 384, 145-156.
[21]
Zhang, W.; Chien, J.; Yong, J.; Kuang, R. Network-based machine learning and graph theory algorithms for precision oncology. npj. Precis. Oncol, 2017, 1(1), 25.
[22]
Jamal, S.; Goyal, S.; Shanker, A.; Grover, A. Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes. BMC. Genome, 2016, 17(1), 807.
[23]
Walt, S.; Colbert, S.C.; Varoquaux, G. The NumPy array: A structure for efficient numerical computation. Comput. Sci. Eng., 2011, 13(2), 22-30.
[24]
Hunter, J.D. Matplotlib: A 2D graphics environment. Compt. Sci. Eng., 2007, 9(3), 90-95.
[25]
McKinney, W. Data structures for statistical computing in python. Proceedings of the 9th Python in Science Conference, SciPy, Texas, U.S.2010, pp. 51-6.
[26]
Jones, E.; Oliphant, T.; Peterson, P. SciPy: Open Source Scientific Tools for Python 2014.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy