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Algorithms for global protein–protein interaction network alignment

Abstract

Protein–protein interaction creates a complex relation among molecular organs. The pivotal functionalities among cells depend on these interactions. In the light of computational biology (CB), it is possible to retrieve the exact information regarding these relationships. The global alignments among proteins are more important. Toward the development of the PPI network analysis, various methods such as heuristics, evolutionary, probabilistic, semi-probabilistic, spectral graph analysis and mapping methods have been developed and this is an ongoing process of progress. Some remarkable contributions to the PPI global network alignments are Common neighbors-based global GRAph (C-GRAAL), GRAph ALigner (GRAAL), Hungarian algorithm-based GRAAL (H-GRAAL), Matching-based GRAph aligner (M-GRAAL), IsoRankN, IsoRank, Scalable Global alignment algorithm, SMETENA, (software package) and GraphCrunch 2 (software package). All the mentioned algorithms and software package mentioned above have tried to address the best illustration and mapping between protein networks for global protein network alignment. For simplicity we avoid local sequence alignment methods and algorithms. For experimental data analysis, five eukaryotic species such as C. elegans (CE), D. melanogaster (DM), S. cerevisiae (SC), H. sapiens (HS) and M. musculus (MM) have been considered.

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References

  • Anderson R, Chung F, Lang K (2006) Local graph partitioning using Page Rank vectors. Foundations of Computer Science, IEEE Computer Society, Los alamitos, C USA, 475–486

  • Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT (2000) Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet 25:25–29

    Article  Google Scholar 

  • Barabasi A, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512

    Article  MathSciNet  Google Scholar 

  • Breitkreutz BJ, Stark C, Reguly T, Boucher L, Breitkreutz A, Livstone M, Oughtred R, Lackner DH, Ba J, Wood V (2008) The BioGRID interaction database: 2008 update. Nucleic Acids Res 36:D637–D640

    Article  Google Scholar 

  • Brouwer AE, Haemers WH (2012) Spectra of graphs. Springer, Heidelberg

    Book  MATH  Google Scholar 

  • Chung LS, Kanghao L, Michael B, Rohit S, Bonnie BB (2009) IsoRankN: spectral methods for global alignment of multiple protein networks. Bioinformatics 25:i253–i258

    Article  Google Scholar 

  • Daniel P, Rohit S, Michael B, Liao CS, Bonnie B (2011) IsoBase: a database of functionally related proteins across PPI networks. Nucleic Acids Res 39(Database issue):D295–D300

    Google Scholar 

  • Erdos P, Rényi A (1959) On random graphs. Publicationes Mathematicae 6:290–297

    Google Scholar 

  • Finn R, Mistry J, Tate J, Coggill P, Heger A, Pollington J, Gavin O, Gunasekaran P, Ceric G, Forslund K (2010) The Pfam protein families database. Nucleic Acids Res 38(Suppl 1):D211

    Article  Google Scholar 

  • Flannick J, Novak A, Srinivasan BS, McAdams HH, Batzoglou S (2006) Graemlin: general and robust alignment of multiple large interaction networks. Genome Res 16(9):1168–1169

    Article  Google Scholar 

  • Golub GH, Van LC (2006) Matrix computations. Johns Hopkins University Press, USA

    Google Scholar 

  • Ito T (2001) A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA 98(8):4569–4574

    Article  Google Scholar 

  • Kalaev M, Bafna V, Sharan R (2009) Fast and accurate alignment of multiple protein networks. J Comput Biol 16(8):989–999

    Article  MathSciNet  Google Scholar 

  • Kelley BP et al (2004) Pathblast: a tool for alignment of protein interaction networks. Nucleic Acids Res 32(Web Server issue):W83–W88

    Article  Google Scholar 

  • Keshava TSP, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A (2009) Human protein reference database. Nucleic Acids Res 37:D767–D772

    Article  Google Scholar 

  • Koyuturk M, Grama A, Szpankowski W (2005) Pairwise local, alignment of protein interaction networks guided by models of evolution, Proc of the 9th International Conference on Research in Computational Molecular Biology (RECOMB)

  • Krogan NJ, Cagney G, Zhong G, Guo G (2006) Global landscape of protein complexes in the yeast saccharomyces cerevisiae. Nature 440(7084):637–643

    Article  Google Scholar 

  • Kuchaiev O, Przulj N (2011) Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics 27:1390–1396

    Article  Google Scholar 

  • Kuchaiev O, Milenkovic T, Memisevic V, Hayes W, Przulj N (2010) GRAph aligner (GRAAL). J R Soc Interface 7:1341–1354

    Article  Google Scholar 

  • Ma YC, Lin HS, Lee CC, Tang YC, Berger B, Liao SC (2013) Reconstruction of phyletic trees by global alignment of multiple metabolic networks. BMC Bioinformatics 14(S2):1–9

    Google Scholar 

  • Mehta S, Hazzard K, Machiraju R, Parthasarathy S, Wilkins J (2004) Detection and visualization of anomalous structures in molecular dynamics simulation data, Proceedings of the Conference on Visualization ‘04 IEEE Computer Society, 465–472

  • Milenkovic T, Ng WLL, Hayes W, Przulj N (2010) Optimal network alignment with graphlet degree vectors. Cancer Inf 9:121–137

  • Oleksii K, Natasa P (2010) Global network alignment, Nature Precedings: hdl:10101/npre.2010.4505

  • Penrose M, Random M (2003) Geometric graphs. Oxford University Press, Oxford

  • Pinter e RY (2005) Alignment of metabolic pathways. Bioinformatics 21(16):3401–3408

    Article  Google Scholar 

  • Pržulj N (2007) Biological network comparison using graphlet degree distribution. Bioinformatics 23:e177–e183

    Article  Google Scholar 

  • Przulj N, Corneil D (2006) Modelling protein–protein interaction networks via a stickiness index. J R Soc Interface 3(10):711–716

    Article  Google Scholar 

  • Przulj N, Kuchaie O, Stevanovic A, Hayes W (2010) Geometric evolutionary dynamics of protein interaction networks. Proceedings of the Pacific Symposium on Biocomputing Big sland, Hawaii, pp 178–189

    Google Scholar 

  • Pržulj N, Corneil DG, Jurisic I (2004) Modeling interactome: scale-free or geometric? Bioinformatics 20(18):3508–3515

    Article  Google Scholar 

  • Rohit S, Jinbo X, Bonnie B (2008) Global alignment of multiple protein interaction networks with application to functional orthology detection. Massachusetts Institute of Technology, Cambridge, July 16

    Google Scholar 

  • Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D (2004) The database of interacting proteins. Nucleic Acids Res 32:D449–D451

    Article  Google Scholar 

  • Sharan R, Suthram S, Kelley RM, Kuhn T, McCuine S, Uetz P, Sittler T, Karp RM, Ideker T (2005) Conserved patterns of protein interaction in multiple species. Proc Natl Acad Sci USA 102(6):1974–1979

    Article  Google Scholar 

  • Stanford (2011) http://infolab.stanford.edu/~ullman/cs345-notes.html

  • Uetz P (2000) A comprehensive analysis of protein–protein interactions in saccharomyces cerevisiae. Nature 403(6770):623–627

    Article  Google Scholar 

  • Vazqueza A, Flamminia A, Maritana A, Vespignani A (2003) Modeling of protein interaction networks. Complexus 2003(1):38–44

    Google Scholar 

  • Vesna M, Przulj N (2012) C-GRAAL: common-neighbors-based global GRAph alignment of biological networks. Integr. Biol 4:734–743

    Article  Google Scholar 

  • Wiktionary (2012). http://en.wiktionary.org/wiki/ortholog

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Correspondence to Sonia Farhana Nimmy.

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Farhana Nimmy, S., Shohelur Rahman, M. Algorithms for global protein–protein interaction network alignment. Netw Model Anal Health Inform Bioinforma 3, 65 (2014). https://doi.org/10.1007/s13721-014-0065-y

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  • DOI: https://doi.org/10.1007/s13721-014-0065-y

Keywords

  • C-GRAAL
  • M-GRAAL
  • SMETENA
  • GraphCrunch2
  • IsoRankN