Evolutionary clustering annotation of ortho-paralogous gene in a multi species using Venn diagram visualization

  • Authors

    • Bipin Nair B J
    • Sarath M S
    https://doi.org/10.14419/ijet.v7i1.9.9755

    Received date: February 26, 2018

    Accepted date: February 26, 2018

    Published date: March 1, 2018

  • Venn Diagram Representator, Orthologous, Paralogous, UPGMA, SOM.
  • Abstract

    The evolutionary analysis of the genome of the immediate cluster is an important part of comparative genomics research. Identifying the overlap between immediate homologous clusters allows us to elucidate the function and evolution of proteins between species. Here, we report a network platform called Ortho-paralogous Venn-diagram representation that can be used to compare and visualize a wide range of ortho-paralogous clustering of genomes. In our work Ortho-paralogous Venn-diagram results show a functional summary of interactive Venn diagrams, summary counts, and interspecies shared cluster separations and intersections. Ortho-paralogous Venn-diagram also uses a variety of sequence analysis tools to gain an in-depth understanding of the cluster. In addition, Ortho-paralogous Venn identifies direct homologous clusters of single copy genes and allows custom search of specific gene clusters. It enables us in wide analysis of the genes and protein by comparing the genes using Venn diagram .Here the user can upload our own gene sequences into the application ,using three clustering approach to check the best clustering approches like SOM,K-means and advanced clustering after that we are using the Venn diagram repersentator to evolutionary cluster the genes having similar functionality and structural similarity from the uploaded data.Here we are using a venn diagram representation as an application which used to cluster the orthologous and paralogous gene on basics of their evolution and functional aspects.it enables us in wide analysis of the genes and protein by comparing the genes using venn diagram representation.here the user can upload our own gene sequences into the application where the venn diagram representator clusters.the genes having similar functionality and structural similarity from the uploaded data.

  • References

    1. Servant, F., Bru, C., Carrère, S., Courcelle, E., Gouzy, J., Peyruc, D., & Kahn, D. (2002). ProDom: automated clustering of homologous domains.Briefings in bioin-formatics, 3(3), 246-251. https://doi.org/10.1093/bib/3.3.246.
    2. Wei, X., Kuhn, D. N., & Narasimhan, G. (2003, August). Degenerate primer design via clustering. In Bioinformatics Confer-ence, 2003. CSB 2003. Proceedings of the 2003 IEEE (pp. 75-83). IEEE. https://doi.org/10.1109/CSB.2003.1227306.
    3. Thomas, J. H. (2006). Analysis of homol-ogous gene clusters in
    4. Caenorhabditis elegans reveals striking regional cluster Domains. Genetics, 172 (1), 127-143. https://doi.org/10.1534/genetics.104.040030.
    5. Shannon, M., Hamilton, A. T., Gordon, L., Branscomb, E., & Stubbs, L. (2003). Dif-ferential expansion of zinc-finger tran-scription factor loci in homologous hu-man and mouse gene clusters. Genome research, 13(6a), 1097-1110. https://doi.org/10.1101/gr.963903.
    6. Bipin Nair B J #1 # DNA Sequence Alignment Using Matching Algorithm to Identify the Rare Genetic Mutation in Various Proteins.
    7. Sujith, M., & Alphonsa, M. V. Self-regulating Exploration for Orthologous in Homologous Hematologic Gene Se-quence Data Using UPGMA Method.
    8. Wang, Y., Coleman-Derr, D., Chen, G., & Gu, Y. Q. (2015). OrthoVenn: a web serv-er for genome wide comparison and anno-tation of orthologous clusters across mul-tiple species. Nucleic acids research, 43(W1), W78-W84. https://doi.org/10.1093/nar/gkv487.
    9. Peterson, M. E., Chen, F., Saven, J. G., Roos, D. S., Babbitt, P. C., & Sali, A. (2009). Evolutionary constraints on struc-tural similarity in orthologs and paralogs. Protein Science, 18(6), 1306-1315. https://doi.org/10.1002/pro.143
    10. Fouts, D. E., Brinkac, L., Beck, E., Inman, J., & Sutton, G. (2012). PanOCT: auto-mated clustering of orthologs using con-served gene neighborhood for pan-genomic analysis of bacterial strains and closely related species.Nucleic acids re-search, 40(22), e172-e172. https://doi.org/10.1093/nar/gks757.
    11. Lechner, M., Hernandez-Rosales, M., Do-err, D., Wieseke, N., Thévenin, A., Stoye, J., & Stadler, P. F. (2014). Orthology de-tection combining clustering and synteny for very large datasets. PLoS One, 9(8), e105015. https://doi.org/10.1371/journal.pone.0105015.
    12. Berglund, A. C., Sjölund, E., Östlund, G., & Sonnhammer, E. L. (2007). InParanoid 6: eukaryotic ortholog clusters with in-paralogs. Nucleic acids research, 36(suppl_1), D263-D266. https://doi.org/10.1093/nar/gkm1020.
    13. Singh, L. N., & Hannenhalli, S. (2009). Correlated changes between regulatory cis elements and condition-specific ex-pression in paralogous gene families. Nu-cleic acids research, 38(3), 738-749. https://doi.org/10.1093/nar/gkp989.
    14. Frias-Lopez, J., Shi, Y., Tyson, G. W., Coleman, M. L., Schuster, S. C., Chisholm, S. W., & DeLong, E. F. (2008). Microbial community gene expression in ocean surface waters. Proceedings of the National Academy of Sciences, 105(10), 3805-3810. https://doi.org/10.1073/pnas.0708897105.
    15. Fouts, D. E., Brinkac, L., Beck, E., Inman, J., & Sutton, G. (2012). PanOCT: auto-mated clustering of orthologs using con-served gene neighborhood for pan-genomic analysis of bacterial strains and closely related species.Nucleic acids re-search, 40(22), e172-e172. https://doi.org/10.1093/nar/gks757.
    16. Muller, J., Szklarczyk, D., Julien, P., Le-tunic, I., Roth, A., Kuhn, M., & Bork, P. (2009). eggNOG v2. 0: extending the evo-lutionary genealogy of genes with en-hanced non-supervised orthologous groups, species andfunctional annotations. Nucleic acids research, 38(suppl_1), D190-D195.
    17. Alexeyenko, A., Tamas, I., Liu, G., & Sonnhammer, E. L. (2006). Automatic clustering of orthologs and inparalogs shared by multiple proteomes. Bioinfor-matics, 22(14), e9-e15. https://doi.org/10.1093/bioinformatics/btl213.
    18. Quackenbush, J., Liang, F., Holt, I., Per-tea, G., & Upton, J. (2000). The TIGR gene indices: reconstruction and represen-tation of expressed gene sequences. Nu-cleic acids research, 28(1), 141-145. https://doi.org/10.1093/nar/28.1.141.
    19. Quackenbush, J., Cho, J., Lee, D., Liang, F., Holt, I., Karamycheva, S., & White, J. (2001). The TIGR Gene Indices: analysis of gene transcript sequences in highly sampled eukaryotic species. Nucleic Ac-ids Research, 29(1), 159-164. https://doi.org/10.1093/nar/29.1.159.
    20. Chen, R., & Jeong, S. S. (2000). Func-tional prediction: identification of protein orthologs and paralogs. Protein Science, 9(12), 2344-2353. https://doi.org/10.1110/ps.9.12.2344.
    21. Uchiyama, I. (2006). Hierarchical cluster-ing algorithm forcomprehensive ortholo-gous-domain classification in multiple genomes. Nucleic acids research, 34(2), 647-658. https://doi.org/10.1093/nar/gkj448.
  • Downloads

  • How to Cite

    Nair B J, B., & M S, S. (2018). Evolutionary clustering annotation of ortho-paralogous gene in a multi species using Venn diagram visualization. International Journal of Engineering and Technology, 7(1.9), 162-166. https://doi.org/10.14419/ijet.v7i1.9.9755