Advertisement

Topological Inquisition into the PPI Networks Associated with Human Diseases Through Graphlet Frequency Distribution

  • Debjani Bhattacharjee
  • Sk Md Mosaddek HossainEmail author
  • Raziya Sultana
  • Sumanta RayEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10597)

Abstract

In this article, we have proposed a new framework to compare topological structure of protein-protein interaction (PPI) networks constructed from disease associated proteins. Here, similarity of local topological structure between networks is discovered through the analysis of frequent sub-pattern occurred in them using a novel similarity measure based on graphlet frequency distribution. Graphlets are small connected non-isomorphic induced subgraphs in a network which provides detailed topological statistics of it. We have analyzed pairwise similarity of 22 disease associated PPI networks and compared topological and biological characteristics. It has been observed that the PPI networks associated with disease classes ‘metabolic’ and ‘neurological’ have the highest similarity scores. Higher similarity has also been observed for networks of disease classes ‘bone’ and ‘skeletal’; ‘endocrine’ and ‘multiple’; and ‘gastrointestinal and respiratory’. Topological analysis of the networks also reveals that degree and betweenness centrality of proteins is strongly correlated for the network pairs with high similarity scores. We have also performed gene ontology and pathway based analysis of the proteins involved in the disease associated networks.

References

  1. 1.
    Goh, K., Cusick, M., Valle, D., Childs, B., Vidal, M., Barabasi, A.: The human disease network. PNAS 104, 8685–8690 (2007)CrossRefGoogle Scholar
  2. 2.
    Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., Hirakawa, M.: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 38(1), D355 (2010)CrossRefGoogle Scholar
  3. 3.
    Yang, J., Wu, S.-J., Yang, S.-Y., Peng, J.-W., Wang, S.-N., Wang, F.-Y., Song, Y.-X., Qi, T., Li, Y.-X., Li, Y.-Y.: DNetDB: the human disease network database based on dysfunctional regulation mechanism. BMC Syst. Biol. 10(1), 36 (2016)CrossRefGoogle Scholar
  4. 4.
    Chen, Y., Zhang, X., Zhang, G.-Q., Xu, R.: Comparative analysis of a novel disease phenotype network based on clinical manifestations. J. Biomed. Inf. 53, 113–120 (2015)CrossRefGoogle Scholar
  5. 5.
    Bandyopadhyay, S., Ray, S., Mukhopadhyay, A., Maulik, U.: A multiobjective approach for identifying protein complexes and studying their association in multiple disorders. Algorithms Mol. Biol. 10(24) (2014). doi: 10.1186/s13015-015-0056-2
  6. 6.
    Sonmez, A.B., Can, T.: Comparison of tissue/disease specific integrated networks using directed graphlet signatures. In: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ser. BCB 2016, New York, NY, USA, pp. 533–534. ACM (2016)Google Scholar
  7. 7.
    Le, D.-H.: A novel method for identifying disease associated protein complexes based on functional similarity protein complex networks. Algorithms Mol. Biol. 10(1), 14 (2015). https://doi.org/10.1186/s13015-015-0044-6 CrossRefGoogle Scholar
  8. 8.
    Pržulj, N.: Biological network comparison using graphlet degree distribution. Bioinformatics 23(2), e177 (2007)CrossRefGoogle Scholar
  9. 9.
    Ribeiro, P., Silva, F.: G-tries: a data structure for storing and finding subgraphs. Data Mining Knowl. Discov. 28(2), 337–377 (2014)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Hamosh, A., Scott, A.F., Amberger, J.S., Bocchini, C.A., McKusick, V.A.: Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33(Database issue), 514–517 (2005)CrossRefGoogle Scholar
  11. 11.
    Prasad, T., Goel, R., Kandasamy, K.: Human protein reference database. Nucleic Acids Res. 37, D767–D772 (2009)CrossRefGoogle Scholar
  12. 12.
    Huang, D., Sherman, B., Tan, Q., Collins, J., Alvord, W., Roayaei, J., Stephens, R., Baseler, M., Lane, H., Lempicki, R.: The David gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8(9), R183 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Acharya Prafulla Chandra CollegeKolkataIndia
  2. 2.Department of Computer Science and EngineeringAliah UniversityKolkataIndia

Personalised recommendations