Skip to main content

Advertisement

Log in

Improved Firefly Optimization for Pairwise Network Alignment with its Biological Significance of Predicting GO Functions and KEGG Pathways

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Global pairwise biological network alignment is a pervasive technique in bioinformatics and computational biology. Even now, the computation of network alignment is a challenging effort for delivering an efficient and statistically significant results. Thus, the optimization algorithms have been used to get the precise results of protein network alignment. In this work, an Improved Firefly Optimization Algorithm method was used to align the biological protein networks in a pairwise technique which resulted in an optimal solution. By utilizing the final outcome of network alignment, the function of proteins in a network and KEGG pathways was also obtained and found that the aligned proteins have more functions that are common in nature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Abbreviations

PPI:

Protein–protein interaction

GRAAL:

GRAph Aligner

S3 :

Symmetric substructure score

IFOA:

Improved Firefly Optimization Algorithm

GoP:

Gene-ontology precision

SANA:

Simulated annealing network aligner

References

  1. Huang, J., Gong, M., & Ma, L. (2016). A global network alignment method using discrete particle swarm optimization. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 99, 1. https://doi.org/10.1109/TCBB.2016.2618380

    Article  Google Scholar 

  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), e38107. https://doi.org/10.1371/journal.pone.0038107

    Article  Google Scholar 

  3. Mina, M., & Guzzi, P. H. (2014). Improving the robustness of local network alignment: Design and extensive assessment of a Markov Clustering-based approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics., 11, 561–572. https://doi.org/10.1109/TCBB.2014.2318707

    Article  Google Scholar 

  4. Ngoc, H. T., & Xuan, H. H. (2016). ACOGNA: An efficient method for protein-protein interaction network alignment. In: Proceedings of IEEE eighth international conference on knowledge and systems engineering. https://doi.org/10.1109/KSE.2016.7758021

  5. Elmsallati, A., Clark, C., & Kalita, J. (2015). Global alignment of protein-protein interaction networks: A survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 13, 689–705. https://doi.org/10.1109/TCBB.2015.2474391

    Article  Google Scholar 

  6. Yerneni, S., Khan, I., Wei, Q., & Kihara, D. (2018). IAS: Interaction specific GO term associations for predicting protein-protein interaction networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics. https://doi.org/10.1109/TCBB.2015.2476809

    Article  Google Scholar 

  7. Wei, Q., Khan, I. K., Ding, Z., Yerneni, S., & Kihara, D. (2017). NaviGO: An interactive tool for visualization and functional similarity and coherence analysis with gene ontology. BMC Bioinformatics, 18, 177. https://doi.org/10.1186/s12859-017-1600-5

    Article  Google Scholar 

  8. Clark, C., & Kalita, J. (2015). A multiobjective memetic algorithm for PPI network alignment. Bioinformatics, 31(12), 1988–1998. https://doi.org/10.1093/bioinformatics/btv063

    Article  Google Scholar 

  9. Singh, R., Xu, J., & Berger, B. (2008). Global alignment of multiple protein interaction networks with application to functional orthology detection. Proceeding of the National Academy of Sciences of the United States of America., 105, 12763–12768. https://doi.org/10.1073/pnas.0806627105

    Article  Google Scholar 

  10. Memisevica, V., & Przulj, N. (2012). C-GRAAL: Common-neighbors-based global GRAph ALignment of biological networks. Integrated Biology., 7, 734–743. https://doi.org/10.1039/c2ib00140c

    Article  Google Scholar 

  11. Malod-Dognin, N., & Przulj, N. (2015). L-GRAAL: Lagrangian graphlet-based network aligner. Bioinformatics, 31, 2182–2189. https://doi.org/10.1093/bioinformatics/btv130

    Article  Google Scholar 

  12. Kuchaiev, O., & Przulj, N. (2011). Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics, 27, 1390–1396. https://doi.org/10.1093/bioinformatics/btr127

    Article  Google Scholar 

  13. Patro, R., & Kingsford, C. (2012). Global network alignment using multiscale spectral signatures. Bioinformatics, 28, 3105–3114. https://doi.org/10.1093/bioinformatics/bts592

    Article  Google Scholar 

  14. Hashemifar, S., Ma, J., Naveed, H., Canzar, S., & Xu, J. (2016). ModuleAlign: Module-based global alignment of protein-protein interaction networks. Bioinformatics, 32, i658–i664. https://doi.org/10.1093/bioinformatics/btw447

    Article  Google Scholar 

  15. Kazemi, E., Hassani, H., Grossglauser, M., & Modarres, H. P. (2016). PROPER: Global protein interaction network alignment through percolation matching. BMC Bioinformatics, 17, 527. https://doi.org/10.1186/s12859-016-1395-9

    Article  Google Scholar 

  16. Dognin, N. M., Ban, K., & Pruzlj, N. (2017). Unified alignment of protein-protein interaction networks. Scientific Reports., 7, 953. https://doi.org/10.1038/s41598-017-01085-9

    Article  Google Scholar 

  17. Saraph, V., & Milenkovic, T. (2014). MAGNA: Maximizing accuracy in global network alignment. Bioinformatics, 30, 2931–2940. https://doi.org/10.1093/bioinformatics/btu409

    Article  Google Scholar 

  18. Vijayan, V., Saraph, V., & Milenkovic, T. (2015). MAGNA++: Maximizing accuracy in global network alignment via both node and edge conservation. Bioinformatics, 31, 2409–2411. https://doi.org/10.1093/bioinformatics/btv161

    Article  Google Scholar 

  19. Ibragimov, R., Martens, J., Guo, J., & Baumbach, J. (2013). NABEECO: Biological network alignment with bee colony optimization algorithm. In: Proceeding of 15th annual conference companion on genetic and evolutionary computation (pp. 43–44). https://doi.org/10.1145/2464576.2464600

  20. Tuncay, E. G., & Can, T. (2016). SUMONA: A supervised method for optimizing network alignment. Computational Biology and Chemistry., 63, 41–61. https://doi.org/10.1016/j.compbiolchem.2016.03.003

    Article  Google Scholar 

  21. Chindelevitch, L., Ma, C. Y., Liao, C. S., & Berger, B. (2013). Optimizing a global alignment of protein interaction networks. Bioinformatics, 29, 2765–2773. https://doi.org/10.1093/bioinformatics/btt486

    Article  Google Scholar 

  22. Mamano, N., & Hayes, W. B. (2017). SANA: Simulated Annealing far outperforms many other search algorithms for biological network alignment. Bioinformatics, 33, 2156–2164. https://doi.org/10.1093/bioinformatics/btx090

    Article  Google Scholar 

  23. Sun, Y., Crawford, J., Tang, J., & Milenkovic, T. (2014). Simultaneous optimization of both node and edge conservation in network alignment via WAVE. In M. Pop, sH. Touzet (Eds.), Algorithms in bioinformatics. WABI 2015. LNCS (p. 9289). https://doi.org/10.1007/978-3-662-48221-6_2.

  24. Yang, X. (2009). Firefly algorithms for multimodal optimization. stochastic algorithms: Foundations and applications SAGA 2009. LNCS (p. 5792). Heidelberg: Springer. https://doi.org/10.1007/978-3-642-04944-6_14

  25. Kaur, K., Salgotra, R., & Singh, U. (2017). An improved firefly algorithm for numerical optimization. Proceedings of International Conference on Innovations in Information, Embedded and Communication Systems. https://doi.org/10.1109/ICIIECS.2017.8275914

    Article  Google Scholar 

  26. Kuchaiev, O., Milenkovic, T., Memisevic, V., Hayes, W., & Przulj, N. (2010). Topological network alignment uncovers biological function and phylogeny. Journal of Royal Society Interface. https://doi.org/10.1098/rsif.2010.0063

    Article  Google Scholar 

  27. Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Cepas, J. H., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., Jensen, L. J., & Mering, C. V. (2019). STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research., 47, D607–D613. https://doi.org/10.1093/nar/gky1131

    Article  Google Scholar 

  28. Kerrien, S., Aranda, B., Breuza, L., Bridge, A., Broackes-Carter, F., Chen, C., Duesbury, M., Dumousseau, M., Feuermann, M., Hinz, U., et al. (2012). The intact molecular interaction database in 2012. Nucleic Acids Research, 40, D841–D846. https://doi.org/10.1093/nar/gkr1088

    Article  Google Scholar 

  29. Chatr-aryamontri, A., Breitkreutz, B.-J., Heinicke, S., Boucher, L., Winter, A., Stark, C., Nixon, J., Ramage, L., Kolas, N., O’Donnell, L., et al. (2013). The biogrid interaction database: 2013 update. Nucleic Acids Research., 41, D816–D823. https://doi.org/10.1093/nar/gks1158

    Article  Google Scholar 

  30. Needleman, S. B., & Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology., 48, 443–453. https://doi.org/10.1016/0022-2836(70)90057-4

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to D. Ramyachitra.

Ethics declarations

Conflict of interest

There are no conflicts of interest.

Competing interests

The authors declare no competing financial interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 618 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ranjani Rani, R., Ramyachitra, D. Improved Firefly Optimization for Pairwise Network Alignment with its Biological Significance of Predicting GO Functions and KEGG Pathways. Wireless Pers Commun 121, 2823–2844 (2021). https://doi.org/10.1007/s11277-021-08851-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08851-z

Keywords

Navigation