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IGNet: Constructing Rooted Phylogenetic Networks Based on Incompatible Graphs

  • Juan WangEmail author
  • Maozu Guo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

Phylogenetic networks are used in biology to describe reticulate or non-treelike evolution events. Thus, constructing phylogenetic networks is essential for the research on the evolution of species. IGNet is a web-based server for on-line construction of rooted phylogenetic networks from rooted phylogenetic trees based on an incompatible graph. Two methods, Open image in new window and BIMLR, were introduced and implemented in IGNet to help users construct rooted phylogenetic networks rapidly and efficiently. Besides the on-line computation service, the IGNet also provides downloadable Java programs of Open image in new window and BIMLR for off-line construction of rooted phylogenetic networks. IGNet is availability from http://bioinformatics.imu.edu.cn.

Keywords

IGNet Phylogenetic network Evolution Phylogenetic tree 

Notes

Acknowledgements

The work was supported by the National Natural Science Foundation of China (61661040, 61661039, 61571163, 61532014, 61671189); the National Key Research and Development Plan Task of China (Grant No. 2016YFC0901902).

References

  1. 1.
    Bandelt, H.J., Forster, P., Röhl, A.: Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16(1), 37–48 (1999)CrossRefGoogle Scholar
  2. 2.
    Bryant, D., Moulton, V.: Neighbor-net: an agglomerative method for the construction of phylogenetic networks. Mol. Biol. Evol. 21(2), 255–265 (2004)CrossRefGoogle Scholar
  3. 3.
    Grünewald, S., Forslund, K., Dress, A., Moulton, V.: QNet: an agglomerative method for the construction of phylogenetic networks from weighted quartets. Mol. Biol. Evol. 24(2), 532–538 (2007)CrossRefGoogle Scholar
  4. 4.
    Gunawan, A.D., Lu, B.L., Zhang, L.: A program for verification of phylogenetic network models. Bioinformatics 32(17), i503–i510 (2016)CrossRefGoogle Scholar
  5. 5.
    Guo, L., Yu, J., Liang, T., Zou, Q.: MIR-isomiREXP: a web-server for the analysis of expression of miRNA at the miRNA/isomiR levels. Sci. Rep. 6, 23700 (2016)CrossRefGoogle Scholar
  6. 6.
    Huson, D.H., Bryant, D.: Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23(2), 254–67 (2006)CrossRefGoogle Scholar
  7. 7.
    Huson, D.H., Dezulian, T., Klöpper, T., Steel, M.A.: Phylogenetic super-networks from partial trees. IEEE/ACM Trans. Comput. Biol. Bioinf. 1(4), 151–158 (2004)CrossRefGoogle Scholar
  8. 8.
    Huson, D.H., Rupp, R.: Summarizing Multiple Gene Trees Using Cluster Networks. Springer, Heidelberg (2008)Google Scholar
  9. 9.
    Huson, D.H., Rupp, R., Berry, V., Gambette, P., Paul, C.: Computing galled networks from real data. Bioinformatics 25(12), i85–i93 (2009)CrossRefGoogle Scholar
  10. 10.
    Huson, D.H., Rupp, R., Scornavacca, C.: Phylogenetic Networks: Concepts, Algorithms and Applications. Cambridge University Press, Cambridge (2011)Google Scholar
  11. 11.
    Huson, D.H., Scornavacca, C.: Dendroscope 3: an interactive tool for rooted phylogenetic trees and networks. Syst. Biol. 61(6), 1061–1067 (2012)CrossRefGoogle Scholar
  12. 12.
    van Iersel, L., Kelk, S., Rupp, R., Huson, D.: Phylogenetic networks do not need to be complex: using fewer reticulations to represent conflicting clusters. Bioinformatics 26(12), i124–i131 (2010)CrossRefGoogle Scholar
  13. 13.
    Semple, C.: Hybridization networks. Department of Mathematics and Statistics, University of Canterbury, Christchurch (2006)Google Scholar
  14. 14.
    Wang, J.: A metric on the space of partly reduced phylogenetic networks. BioMed Res. Int. 2016, 1–10 (2016)Google Scholar
  15. 15.
    Wang, J.: A survey of methods for constructing rooted phylogenetic networks. PLoS One 11(11), e0165834 (2016)CrossRefGoogle Scholar
  16. 16.
    Wang, J., Guo, M., Liu, X., Liu, Y., Wang, C., Xing, L., Che, K.: LNETWORK: an efficient and effective method for constructing phylogenetic networks. Bioinformatics 29(18), 2269–2276 (2013)CrossRefGoogle Scholar
  17. 17.
    Wang, J., Guo, M., Xing, L., Che, K., Liu, X., Wang, C.: BIMLR: a method for constructing rooted phylogenetic networks from rooted phylogenetic trees. Gene 527(1), 344–351 (2013)CrossRefGoogle Scholar
  18. 18.
    Xu, Y., Guo, M., Liu, X., Wang, C., Liu, Y., Liu, G.: Identify bilayer modules via pseudo-3D clustering: applications to miRNA-gene bilayer networks. Nucleic Acids Res. 44(20), e152 (2016)Google Scholar
  19. 19.
    Xu, Y., Wang, Y., Luo, J., Zhao, W., Zhou, X.: Deep learning of the splicing (epi) genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision. Nucleic Acids Res. 45(21), 12100–12112 (2017)CrossRefGoogle Scholar
  20. 20.
    Zeng, X., Zhang, X., Zou, Q.: Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Briefings Bioinf. 17(2), 193–203 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer ScienceInner Mongolia UniversityHohhotPeople’s Republic of China
  2. 2.School of Electrical and Information EngineeringBeijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China
  3. 3.Beijing Key Laboratory of Intelligent Processing for Building Big DataBeijingPeople’s Republic of China

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