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NetCoffee2: A Novel Global Alignment Algorithm for Multiple PPI Networks Based on Graph Feature Vectors

  • Jialu Hu
  • Junhao He
  • Yiqun Gao
  • Yan Zheng
  • Xuequn Shang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

Network alignment provides a fast and effective framework to automatically identify functionally conserved proteins in a systematic way. However, due to the fast growing biological data, there is an increasing demand for more accurate and efficient tools to deal with multiple PPI networks. Here, we present a novel global alignment algorithm NetCoffee2 to discover functionally conserved proteins. To test the algorithm performance, NetCoffee2 and several existing algorithms were applied on eight real biological datasets. Results show that NetCoffee2 is superior to IsoRankN, NetCoffee and multiMAGNA++ in terms of both coverage and consistency. The binary and source code are freely available at https://github.com/screamer/NetCoffee2.

Keywords

PPI network alignment Simulated annealing Functionally conserved proteins 

Notes

Funding

This project has been funded by the National Natural Science Foundation of China (Grant No. 61332014 and 61702420); the China Postdoctoral Science Foundation (Grant No. 2017M613203); the Natural Science Foundation of Shaanxi Province (Grant No. 2017JQ6037); the Fundamental Research Funds for the Central Universities (Grant No. 3102018zy032).

References

  1. 1.
    U. P. Consortium: Uniprot: a hub for protein information. Nucleic Acids Res. 43(Database issue), 204–212 (2015)Google Scholar
  2. 2.
    Marcotte, E., Pellegrini, M., Ng, H.L., Rice, D.W.: Detecting protein function and protein-protein interactions from genome sequences. Science 285(5428), 751–753 (1999)CrossRefGoogle Scholar
  3. 3.
    Klau, G.W.: A new graph-based method for pairwise global network alignment. BMC Bioinform. 10(Suppl. 1), 1–9 (2009)Google Scholar
  4. 4.
    Narad, P., Chaurasia, A., Wadhwab, G., Upadhyayaa, K.C.: Net2align: analgorithm for pairwise global alignment of biological networks. Bioinformation 12(12), 408 (2016)CrossRefGoogle Scholar
  5. 5.
    Hu, J., Reinert, K.: LocalAli: an evolutionary-based local alignment approach to identify functionally conserved modules in multiple networks. Bioinformatics 31(3), 363–372 (2015)CrossRefGoogle Scholar
  6. 6.
    Saraph, V., Milenković, T.: MAGNA: Maximizing accuracy in global network alignment. Bioinformatics 30(20), 2931 (2013)CrossRefGoogle Scholar
  7. 7.
    Singh, R., Xu, J., Berger, B.: Global alignment of multiple protein interaction networks with application to functional orthology detection. Proc. Natl. Acad. Sci. U.S.A. 105(35), 12763–12768 (2008)CrossRefGoogle Scholar
  8. 8.
    Liao, C.S., Lu, K., Baym, M., Singh, R., Berger, B.: Isorankn: spectral methods for global alignment of multiple protein networks. Bioinformatics 25(12), 253–258 (2009)CrossRefGoogle Scholar
  9. 9.
    Notredame, C., Higgins, D.G., Heringa, J.: T-coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 302(1), 205–217 (2000)CrossRefGoogle Scholar
  10. 10.
    Hu, J., Kehr, B., Reinert, K.: Netcoffee: a fast and accurate global alignment approach to identify functionally conserved proteins in multiple networks. Bioinformatics 30(4), 540 (2014)CrossRefGoogle Scholar
  11. 11.
    Vijayan, V., Saraph, V., Milenković, T.: MAGNA++: maximizing accuracy in global network alignment via both node and edge conservation. Bioinformatics 31(14), 2409–2411 (2015)CrossRefGoogle Scholar
  12. 12.
    Vijayan, V., Milenković, T.: Multiple network alignment via multiMAGNA++. IEEE/ACM Trans. Comput. Biol. Bioinform. PP(99), 1 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jialu Hu
    • 1
  • Junhao He
    • 1
  • Yiqun Gao
    • 1
  • Yan Zheng
    • 1
  • Xuequn Shang
    • 1
  1. 1.School of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina

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