Volume 10, Issue 2 (2019)                   JMBS 2019, 10(2): 329-334 | Back to browse issues page

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Zare Mirak-Abad F, Ghorbanali Z. PRAF Framework for Global Protein-Protein Interaction Network Alignment. JMBS 2019; 10 (2) :329-334
URL: http://biot.modares.ac.ir/article-22-13493-en.html
1- Computer Science Department, Mathematics & Computer Science Faculty, Amirkabir University, Tehran, Iran, Computer Science Department, Mathematics & Computer Science Faculty, Amirkabir University, Tehran, Iran , f.zare@aut.ac.ir
2- Computer Science Department, Mathematics & Computer Science Faculty, Amirkabir University, Tehran, Iran
Abstract:   (3941 Views)
A biological network represents the interaction between a set of macromolecules to drive a particular biological process. In a biological environment, abnormalities happen not only in one molecule but also through a biological network. One of the most effective methods to detect anomaly is the comparison between healthy and diseased networks. In this regard, biological network alignment is one of the most efficient ways to find the difference between healthy and diseased cells. This problem, protein-protein interaction network alignment, has been raised in two main types: Local network alignment and Global network alignment. According to the NP-completeness of this problem, different non-deterministic approaches have been proposed to tackle the Global network alignment problem. Recently, NetAl has been introduced as a common algorithm to align two networks. Although this algorithm can align two networks at the appropriate time, it does not consider biological features. In this study, we present a new framework called PRAF to improve the results of network alignment algorithms such as NetAl by considering some biological features like gene ontology (GO).
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Article Type: Research Paper | Subject: Agricultural Biotechnology
Received: 2018/02/4 | Accepted: 2018/03/3 | Published: 2018/06/20

References
1. Rivas DL, Fontanillo C. Protein-protein interactions essentials: Key concepts to building and analyzing interactome networks. PLoS Computat Biol. 2010;6(6):e1000807. [Link] [DOI:10.1371/journal.pcbi.1000807]
2. Young KH. Yeast two-hybrid: so many interactions, (in) so little time. Biol Reprod. 1998;58(2):302-11. [Link] [DOI:10.1095/biolreprod58.2.302]
3. Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature. 2003;422:198-207. [Link] [DOI:10.1038/nature01511]
4. Lathrop RH. The protein threading problem with sequence amino acid interaction preferences is NP-complete. Protein Eng. 1994;7(9):1059-68. [Link] [DOI:10.1093/protein/7.9.1059]
5. Micale G, Pulvirenti A, Giugno R, Ferro A. GASOLINE: a Greedy and Stochastic algorithm for Optimal Local multiple alignment of Interaction Networks. Plos One. 2014;9(6):e98750. [Link] [DOI:10.1371/journal.pone.0098750]
6. Singh R, Xu J., Berger B. Global alignment of multiple protein interaction networks with application to functional orthology detection. PNAS. 2008;105(35);12763-12768. [Link] [DOI:10.1073/pnas.0806627105]
7. Yang J, Li J, Grunewald S, Wan XF. BinAligner: A heuristic method to align biological networks. BMC Bioinformatics. 2013;14(Suppl 14):S8. [Link] [DOI:10.1186/1471-2105-14-S14-S8]
8. Neyshabur B, Khadem A, Hashemifar S, Arab S. NETAL: A new graph-based method for global alignment of protein-protein interaction networks. Bioinformatics. 2013;29(13):1654-62. [Link] [DOI:10.1093/bioinformatics/btt202]
9. Morris JH, Apeltsin L, Newman AM, Baumbach J, Wittkop T, Su G et al. Clustermaker: A multi-algorithm clustering plugin for Cytoscape. BMC Bioinformatics. 2011;12:436. [Link] [DOI:10.1186/1471-2105-12-436]
10. Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods. 2012;9:471-2. [Link] [DOI:10.1038/nmeth.1938]
11. Li M, Tang Y, Li D, Wu F, Wang J. CytoCluster: A cytoscape plugin for cluster analysis and visualization of biological networks. Int J Mol Sci. 2017;18(9):E1880. [Link] [DOI:10.3390/ijms18091880]
12. Maere S, Heymans K, Kuiper M. BiNGO: A Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005;21(16):3448-9. [Link] [DOI:10.1093/bioinformatics/bti551]
13. Proulx SR, Promislow DE, Phillips PC. Network thinking in ecology and evolution. Trends Ecol Evol. 2005;20(6):345-53. [Link] [DOI:10.1016/j.tree.2005.04.004]
14. Xenarios I, Rice WD, Salwinski L, Baron MK, Marcotte EM, Eisenberg D. DIP: The database of interacting proteins. Nucleic Acids Res. 2000;28(1):289-91. [Link] [DOI:10.1093/nar/28.1.289]
15. Liao CS, Lu K, Baym M, Singh R, Berger B. IsoRankN: Spectral methods for global alignment of multiple protein networks. Bioinformatics. 2009;25(12:i253-i258. [Link] [DOI:10.1093/bioinformatics/btp203]
16. Sahraeian M, Yoon BJ. SMETANA: Accurate and scalable algorithm for probabilistic alignment of large-scale biological networks. PloS One. 2015;8(7):e67995. [Link] [DOI:10.1371/journal.pone.0067995]
17. Jeong H, Yoon B. Accurate multiple network alignment through context-sensitive random walk. BMC Sys Biol. 2015;9(Suppl 1):S7. [Link] [DOI:10.1186/1752-0509-9-S1-S7]
18. Alkan F, Erten C. BEAMS: Backbone extraction and merge strategy for the global many-to-many alignment of multiple PPI networks. Bioinformatics. 2014;30(4):531-9. [Link] [DOI:10.1093/bioinformatics/btt713]
19. Dohrmann J, Puchin J, Singh R. Global multiple protein-protein interaction network alignment by combining pairwise network alignments. BMC Bioinformatics. 2015;16(Suppl 13):S11. [Link] [DOI:10.1186/1471-2105-16-S13-S11]

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