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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
U. P. Consortium: Uniprot: a hub for protein information. Nucleic Acids Res. 43(Database issue), 204–212 (2015)
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)
Klau, G.W.: A new graph-based method for pairwise global network alignment. BMC Bioinform. 10(Suppl. 1), 1–9 (2009)
Narad, P., Chaurasia, A., Wadhwab, G., Upadhyayaa, K.C.: Net2align: analgorithm for pairwise global alignment of biological networks. Bioinformation 12(12), 408 (2016)
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)
Saraph, V., Milenković, T.: MAGNA: Maximizing accuracy in global network alignment. Bioinformatics 30(20), 2931 (2013)
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)
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)
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)
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)
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)
Vijayan, V., Milenković, T.: Multiple network alignment via multiMAGNA++. IEEE/ACM Trans. Comput. Biol. Bioinform. PP(99), 1 (2017)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Hu, J., He, J., Gao, Y., Zheng, Y., Shang, X. (2018). NetCoffee2: A Novel Global Alignment Algorithm for Multiple PPI Networks Based on Graph Feature Vectors. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-95933-7_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95932-0
Online ISBN: 978-3-319-95933-7
eBook Packages: Computer ScienceComputer Science (R0)