Research Article

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

by  Ahmed El-Sawy, Mahmoud Mousa, Ahmed Hassan, Sammer Kamal
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Issue 53
Published: Sep 2019
Authors: Ahmed El-Sawy, Mahmoud Mousa, Ahmed Hassan, Sammer Kamal
10.5120/ijca2019919339
PDF

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 },
                        year    = { 2019 },
                        volume  = { 178 },
                        number  = { 53 },
                        pages   = { 5-11 },
                        doi     = { 10.5120/ijca2019919339 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2019
                        %A Ahmed El-Sawy
                        %A Mahmoud Mousa
                        %A Ahmed Hassan
                        %A Sammer Kamal
                        %T CoreAlign: Core-based Global Alignment for Protein-Protein Interaction Networks%T 
                        %J International Journal of Computer Applications
                        %V 178
                        %N 53
                        %P 5-11
                        %R 10.5120/ijca2019919339
                        %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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Janjić, V., & Pržulj, N. (2012). The Core Diseasome. Molecular BioSystems, 8(10), 2614–2625. https://doi.org/10.1039/c2mb25230a
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • MathWorks. (n.d.). Measure node importance - MATLAB centrality. Retrieved April 22, 2019, from https://www.mathworks.com/help/matlab/ref/graph.centrality.html
  • 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
  • Milenković, T., Ng, W. L., Hayes, W., & Pržulj, N. (2010). Optimal network alignment with graphlet degree vectors. Cancer Informatics, 9, 121–137.
  • 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
  • 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
  • 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
  • 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
  • West, D. (2001). Introduction to Graph Theory (2nd ed.). Perntice Hall, Upper Saddle River.
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

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

Powered by PhDFocusTM