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Reseach Article

CoreAlign: Core-based Global Alignment for Protein-Protein Interaction Networks

by Ahmed El-Sawy, Mahmoud Mousa, Ahmed Hassan, Sammer Kamal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 53
Year of Publication: 2019
Authors: Ahmed El-Sawy, Mahmoud Mousa, Ahmed Hassan, Sammer Kamal
10.5120/ijca2019919339

Ahmed El-Sawy, Mahmoud Mousa, Ahmed Hassan, Sammer Kamal . CoreAlign: Core-based Global Alignment for Protein-Protein Interaction Networks. International Journal of Computer Applications. 178, 53 ( Sep 2019), 5-11. DOI=10.5120/ijca2019919339

@article{ 10.5120/ijca2019919339,
author = { Ahmed El-Sawy, Mahmoud Mousa, Ahmed Hassan, Sammer Kamal },
title = { CoreAlign: Core-based Global Alignment for Protein-Protein Interaction Networks },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2019 },
volume = { 178 },
number = { 53 },
month = { Sep },
year = { 2019 },
issn = { 0975-8887 },
pages = { 5-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number53/30911-2019919339/ },
doi = { 10.5120/ijca2019919339 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:53:52.696084+05:30
%A Ahmed El-Sawy
%A Mahmoud Mousa
%A Ahmed Hassan
%A Sammer Kamal
%T CoreAlign: Core-based Global Alignment for Protein-Protein Interaction Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 53
%P 5-11
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Biological network alignment aims to find similar functional and topological regions to guide the transfer of biological knowledge of cellular functioning from known, well-studied species to unknown ones. The proposed aligner (CoreAlign) relays on the structural of the Protein-Protein Interactions (PPI) network by using network decomposition of what is called shells or internal network cores. The proposed aligner searches the space of each core to build the Alignment. CoreAlign has been compared with many aligners and it has competitive results among these aligners in either topological or biological measures.

References
  1. Bateman, A., Martin, M. J., O’Donovan, C., Magrane, M., Alpi, E., Antunes, R., … Zhang, J. (2017). UniProt: The universal protein knowledgebase. Nucleic Acids Research, 45(D1), D158–D169. https://doi.org/10.1093/nar/gkw1099
  2. Ciriello, G., Mina, M., Guzzi, P. H., Cannataro, M., & Guerra, C. (2012). AlignNemo: A local network alignment method to integrate homology and topology. PLoS ONE, 7(6). https://doi.org/10.1371/journal.pone.0038107
  3. Clark, C., & Kalita, J. (2014). A comparison of algorithms for the pairwise alignment of biological networks. Bioinformatics, 30(16), 2351–2359. https://doi.org/10.1093/bioinformatics/btu307
  4. Elmsallati, A., Clark, C., & Kalita, J. (2016). Global Alignment of Protein-Protein Interaction Networks: A Survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(4), 689–705. https://doi.org/10.1109/TCBB.2015.2474391
  5. Faisal, F. E., Meng, L., Crawford, J., & Milenković, T. (2015). The post-genomic era of biological network alignment. Eurasip Journal on Bioinformatics and Systems Biology, 2015(1). https://doi.org/10.1186/s13637-015-0022-9
  6. Hashemifar, S., Ma, J., Naveed, H., Canzar, S., & Xu, J. (2016). ModuleAlign: Module-based global alignment of protein-protein interaction networks. Bioinformatics, 32(17), i658–i664. https://doi.org/10.1093/bioinformatics/btw447
  7. Hashemifar, S., & Xu, J. (2014). HubAlign: An accurate and efficient method for global alignment of protein-protein interaction networks. Bioinformatics, 30(17), 438–444. https://doi.org/10.1093/bioinformatics/btu450
  8. Janjić, V., & Pržulj, N. (2012). The Core Diseasome. Molecular BioSystems, 8(10), 2614–2625. https://doi.org/10.1039/c2mb25230a
  9. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., & Tanabe, M. (2012). KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Research, 40(D1), 109–114. https://doi.org/10.1093/nar/gkr988
  10. Kelley, B. P., Yuan, B., Lewitter, F., Sharan, R., Stockwell, B. R., & Ideker, T. (2004). PathBLAST: A tool for alignment of protein interaction networks. Nucleic Acids Research, 32(WEB SERVER ISS.), 83–88. https://doi.org/10.1093/nar/gkh411
  11. Koyutürk, M., Kim, Y., Topkara, U., Subramaniam, S., Szpankowski, W., & Grama, A. (2006). Pairwise Alignment of Protein Interaction Networks. Journal of Computational Biology, 13(2), 182–199. https://doi.org/10.1089/cmb.2006.13.182
  12. Kuchaiev, O., Milenković, T., Memišević, V., Hayes, W., & Pržulj, N. (2010). Topological network alignment uncovers biological function and phylogeny. Journal of the Royal Society Interface, 7(50), 1341–1354. https://doi.org/10.1098/rsif.2010.0063
  13. Kuchaiev, O., & Pržulj, N. (2011). Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics, 27(10), 1390–1396. https://doi.org/10.1093/bioinformatics/btr127
  14. Liu, J. G., Ren, Z. M., Guo, Q., & Chen, D. B. (2014). Evolution characteristics of the network core in the facebook. PLoS ONE, 9(8). https://doi.org/10.1371/journal.pone.0104028
  15. Malod-Dognin, N., & Pržulj, N. (2015). L-GRAAL: Lagrangian graphlet-based network aligner. Bioinformatics, 31(13), 2182–2189. https://doi.org/10.1093/bioinformatics/btv130
  16. MathWorks. (n.d.). Measure node importance - MATLAB centrality. Retrieved April 22, 2019, from https://www.mathworks.com/help/matlab/ref/graph.centrality.html
  17. Memišević, V., & Pržulj, N. (2012). C-GRAAL: Common-neighbors-based global GRAph ALignment of biological networks. Integrative Biology (United Kingdom), 4(7), 734–743. https://doi.org/10.1039/c2ib00140c
  18. Milenković, T., Ng, W. L., Hayes, W., & Pržulj, N. (2010). Optimal network alignment with graphlet degree vectors. Cancer Informatics, 9, 121–137.
  19. Neyshabur, B., Khadem, A., Hashemifar, S., & Arab, S. S. (2013). NETAL: A new graph-based method for global alignment of protein-protein interaction networks. Bioinformatics, 29(13), 1654–1662. https://doi.org/10.1093/bioinformatics/btt202
  20. Saraph, V., & Milenković, T. (2014). MAGNA: Maximizing Accuracy in Global Network Alignment. Bioinformatics (Oxford, England), 30(20), 2931–2940. https://doi.org/10.1093/bioinformatics/btu409
  21. Singh, R., Xu, J., & Berger, B. (2007). Research in Computational Molecular Biology. Research in Computational Molecular Biology, (April). https://doi.org/10.1007/978-3-540-71681-5
  22. Vijayan, V., Saraph, V., & Milenković, T. (2015). MAGNA11: Maximizing accuracy in global network alignment via both node and edge conservation. Bioinformatics, 31(14), 2409–2411. https://doi.org/10.1093/bioinformatics/btv161
  23. West, D. (2001). Introduction to Graph Theory (2nd ed.). Perntice Hall, Upper Saddle River.
Index Terms

Computer Science
Information Sciences

Keywords

Protein-protein interactions PPI network alignment protein function network decomposition.