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

 
 
 
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  • 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 bycomparing the genes using venn diagram representation.here the user can upload our own gene sequences into the application where the venn diagram representatorclusters.the genes having similar functionality and structural similarity from the uploaded data.


  • Keywords


    Venn Diagram Representator; Orthologous; Paralogous; UPGMA; SOM.

  • References


      [2] Servant, F., Bru, C., Carrère, S., Courcelle, E., Gouzy, J., Peyruc, D., & Kahn, D. (2002). ProDom: automated clustering of homologous domains.Briefings in bioinformatics, 3(3), 246-251.https://doi.org/10.1093/bib/3.3.246.

      [3] Wei, X., Kuhn, D. N., & Narasimhan, G. (2003, August). Degenerate primer design via clustering. In Bioinformatics Conference, 2003. CSB 2003.Proceedings of the 2003 IEEE (pp. 75-83). IEEE.https://doi.org/10.1109/CSB.2003.1227306.

      [4] Thomas, J. H. (2006). Analysis of homologous gene clusters in

      [5] Caenorhabditis elegans reveals striking regional cluster Domains. Genetics, 172 (1), 127-143.https://doi.org/10.1534/genetics.104.040030.

      [6] Shannon, M., Hamilton, A. T., Gordon, L., Branscomb, E., & Stubbs, L. (2003). Differential expansion of zinc-finger transcription factor loci in homologous human and mouse gene clusters. Genome research, 13(6a), 1097-1110.https://doi.org/10.1101/gr.963903.

      [7] Bipin Nair B J #1 # DNA Sequence Alignment Using Matching Algorithm to Identify the Rare Genetic Mutation in Various Proteins.

      [8] Sujith, M., &Alphonsa, M. V. Self-regulating Exploration for Orthologous in Homologous Hematologic Gene Sequence Data Using UPGMA Method.

      [9] Wang, Y., Coleman-Derr, D., Chen, G., & Gu, Y. Q. (2015). OrthoVenn: a web server for genome wide comparison and annotation of orthologous clusters across multiple species. Nucleic acids research, 43(W1), W78-W84.https://doi.org/10.1093/nar/gkv487.

      [10] Peterson, M. E., Chen, F., Saven, J. G., Roos, D. S., Babbitt, P. C., &Sali, A. (2009). Evolutionary constraints on structural similarity in orthologs and paralogs. Protein Science, 18(6), 1306-1315.https://doi.org/10.1002/pro.143

      [11] Fouts, D. E., Brinkac, L., Beck, E., Inman, J., & Sutton, G. (2012). PanOCT: automated clustering of orthologs using conserved gene neighborhood for pan-genomic analysis of bacterial strains and closely related species.Nucleic acids research, 40(22), e172-e172.https://doi.org/10.1093/nar/gks757.

      [12] Lechner, M., Hernandez-Rosales, M., Doerr, D., Wieseke, N., Thévenin, A., Stoye, J., & Stadler, P. F. (2014). Orthology detection combining clustering and synteny for very large datasets. PLoS One, 9(8), e105015.https://doi.org/10.1371/journal.pone.0105015.

      [13] Berglund, A. C., Sjölund, E., Östlund, G., &Sonnhammer, E. L. (2007). InParanoid 6: eukaryotic ortholog clusters with inparalogs. Nucleic acidsresearch, 36(suppl_1), D263-D266.https://doi.org/10.1093/nar/gkm1020.

      [14] Singh, L. N., &Hannenhalli, S. (2009). Correlated changes between regulatory cis elements and condition-specific expression in paralogous gene families. Nucleic acids research, 38(3), 738-749.https://doi.org/10.1093/nar/gkp989.

      [15] 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.

      [16] Fouts, D. E., Brinkac, L., Beck, E., Inman, J., & Sutton, G. (2012). PanOCT: automated clustering of orthologs using conserved gene neighborhood for pan-genomic analysis of bacterial strains and closely related species.Nucleic acids research, 40(22), e172-e172.https://doi.org/10.1093/nar/gks757.

      [17] Muller, J., Szklarczyk, D., Julien, P., Letunic, I., Roth, A., Kuhn, M., & Bork, P. (2009). eggNOG v2. 0: extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species andfunctional annotations. Nucleic acids research, 38(suppl_1), D190-D195.

      [18] Alexeyenko, A., Tamas, I., Liu, G., &Sonnhammer, E. L. (2006). Automatic clustering of orthologs and inparalogs shared by multiple proteomes.Bioinformatics, 22(14), e9-e15.https://doi.org/10.1093/bioinformatics/btl213.

      [19] Quackenbush, J., Liang, F., Holt, I., Pertea, G., & Upton, J. (2000). The TIGR gene indices: reconstruction and representation of expressed gene sequences. Nucleic acids research, 28(1), 141-145.https://doi.org/10.1093/nar/28.1.141.

      [20] 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 AcidsResearch, 29(1), 159-164.https://doi.org/10.1093/nar/29.1.159.

      [21] Chen, R., &Jeong, S. S. (2000). Functional prediction: identification of protein orthologs and paralogs. Protein Science, 9(12), 2344-2353.https://doi.org/10.1110/ps.9.12.2344.

      [22] Uchiyama, I. (2006). Hierarchical clustering algorithm forcomprehensive orthologous-domain classification in multiple genomes. Nucleic acids research, 34(2), 647-658.https://doi.org/10.1093/nar/gkj448.


 

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Article ID: 9755
 
DOI: 10.14419/ijet.v7i1.9.9755




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