Gene Annotation of Cancer Vaccine for Homo sapiens

Year : 2024 | Volume :02 | Issue : 01 | Page : 1-14
By

Nimilita Chakraborty,

  1. student, KIIT University, Bhubaneswar, India

Abstract

Objectives: Gene annotation helps us to deduce the structural and functional aspect of a gene which encodes for a functional protein in our body. Thus determining the coding sequence and gene location we can derive meaningful insights as to what these genes do in our body. In this study, an unknown gene, Cancer Vaccine For Homo sapiens has been studied and annotated. Methods:This study was based on a computational approach using various web-interface tools to annotate an unknown gene taken from NCBI database. The chosen gene was structurally annotated using %GC-content calculator, visually represented using Microsoft Excel, Augustus for gene prediction, RNA-fold to determine the mRNA structure of the same gene. Functional annotation was done using Blastp, gene ontology was confirmed using Uniprot database, phylogenetic tree was analysed using HOGENOM database and TMHMM to visualize the transmembrane domain of the protein encoded by the gene, expression of the gene by Bgee, antibody analysis, subcellular localization, and functional analysis was accomplished using Human Protein Atlas, wolF PSORT and InterProScan respectively. Results: After completing the gene annotation, the cancer vaccine for Homo sapiens query was found to be 99. 9 % similar to Four-jointed box protein 1 precursor [Homo sapiens] which exhibit low cancer tissue specificity and mostly related renal and urothelial cancer. Conclusion: The Cancer vaccine for Homo Sapiens entry present in the NCBI database, which had no annotation previously, was annotated structurally and functionally in this study. Now we can say this entry belongs to the gene coding for a four-box jointed protein-1 precursor protein which is useful for cancer diagnosis in the early stages and is related to poor prognosis of the disease. Often,specific peptides are designed for FJX-1 protein which have been found to be beneficial in treatment of cancers showing elevated expression of FJX1 proteins and are often used in the form of vaccines.

Keywords: Gene prediction, mRNA structure, cancer, vaccination, local sequence alignment, protein, transmembrane domain.

[This article belongs to International Journal of Molecular Biotechnological Research (ijmbr)]

How to cite this article:
Nimilita Chakraborty. Gene Annotation of Cancer Vaccine for Homo sapiens. International Journal of Molecular Biotechnological Research. 2024; 02(01):1-14.
How to cite this URL:
Nimilita Chakraborty. Gene Annotation of Cancer Vaccine for Homo sapiens. International Journal of Molecular Biotechnological Research. 2024; 02(01):1-14. Available from: https://journals.stmjournals.com/ijmbr/article=2024/view=176357

References

  1. Moore, J. H. and Williams, S. M. (2002). New strategies for identifying gene-gene interactions in hypertension. Annals of Medicine, 34(2), pp. 88–95. doi:https://doi. org/10. 1080/07853890252953473.
  2. Mercer, T. R. and Mattick, J. S. (2013). Understanding the regulatory and transcriptional complexity of the genome through structure. Genome Research, 23(7), pp. 1081–1088. doi:https://doi. org/10. 1101/gr. 156612. 113.
  3. Puente, X. S., Sánchez, L. M., Gutiérrez-Fernández, A., Velasco, G. and López-Otín, C. (2005). A genomic view of the complexity of mammalian proteolytic systems. Biochemical Society Transactions, 33(2), pp. 331–334. doi:https://doi. org/10. 1042/bst0330331.
  4. Pareek, C. S., Smoczynski, R. and Tretyn, A. (2011). Sequencing technologies and genome sequencing. Journal of Applied Genetics, [online] 52(4), pp. 413–435. doi:https://doi. org/10. 1007/s13353-011-0057-x.
  5. Ramsey, J., Rasche, H., Maughmer, C., Criscione, A., Mijalis, E., Liu, M., Hu, J. C., Young, R. and Gill, J.J. (2020). Galaxy and Apollo as a biologist-friendly interface for high-quality cooperative phage genome annotation. PLOS Computational Biology, 16(11), p. e1008214. doi:https://doi. org/10. 1371/journal. pcbi. 1008214.
  6. Afgan, E., Baker, D., Batut, B., van den Beek, M., Bouvier, D., Čech, M., Chilton, J., Clements, D., Coraor, N., Grüning, B. A., Guerler, A., Hillman-Jackson, J., Hiltemann, S., Jalili, V., Rasche, H., Soranzo, N., Goecks, J., Taylor, J., Nekrutenko, A. and Blankenberg, D. (2018). The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Research, 46(W1), pp. W537–W544. doi:https://doi. org/10. 1093/nar/gky379.
  7. Wang, Z., Chen, Y. and Li, Y. (2004). A Brief Review of Computational Gene Prediction Methods. Genomics, Proteomics & Bioinformatics, [online] 2(4), pp. 216–221. doi:https://doi. org/10. 1016/s1672-0229(04)02028-5.
  8. Hoff, K. J. and Stanke, M. (2018). Predicting Genes in Single Genomes with AUGUSTUS. Current Protocols in Bioinformatics, p. e57. doi:https://doi. org/10. 1002/cpbi. 57.
  9. Chan, P. P. and Lowe, T. M. (2019). tRNAscan-SE: Searching for tRNA Genes in Genomic Sequences. Methods in molecular biology (Clifton, N. J. ), [online] 1962, pp. 1–14. doi:https://doi. org/10. 1007/978-1-4939-9173-0_1.
  10. Gruber, A. R., Lorenz, R., Bernhart, S. H., Neubox, R. and Hofacker, I. L. (2008). The Vienna RNA Websuite. Nucleic Acids Research, 36(Web Server), pp. W70–W74. doi:https://doi. org/10. 1093/nar/gkn188.
  11. Oehmen, C. and Nieplocha, J. (2006). ScalaBLAST: A Scalable Implementation of BLAST for High-Performance Data-Intensive Bioinformatics Analysis. IEEE Transactions on Parallel and Distributed Systems, [online] 17(8), pp. 740–749. doi:https://doi. org/10. 1109/TPDS. 2006. 112.
  12. Neumann, R. S., Kumar, S. and Shalchian-Tabrizi, K. (2013). BLAST output visualization in the new sequencing era. Briefings in Bioinformatics, 15(4), pp. 484–503. doi:https://doi. org/10. 1093/bib/bbt009.
  13. Sussman, J. L., Lin, D., Jiang, J., Manning, N. O., Prilusky, J., Ritter, O. and Abola, E. E. (1998). Protein Data Bank (PDB): Database of Three-Dimensional Structural Information of Biological Macromolecules. Acta Crystallographica Section D Biological Crystallography, 54(6), pp. 1078–1084. doi:https://doi. org/10. 1107/s0907444998009378.
  14. UniProt: a hub for protein information. (2014). Nucleic Acids Research, 43(D1), pp. D204–D212. doi:https://doi. org/10. 1093/nar/gku989. ‌
  15. Jones, P., Binns, D., Chang, H. -Y., Fraser, M., Li, W., McAnulla, C., McWilliam, H., Maslen, J., Mitchell, A., Nuka, G., Pesseat, S., Quinn, A. F., Sangrador-Vegas, A., Scheremetjewo, M., Yong, S. -Y., Lopez, R. and Hunter, S. (2014). InterProScan 5: genome-scale protein function classification. Bioinformatics, [online] 30(9), pp. 1236–1240. doi:https://doi. org/10. 1093/bioinformatics/btu031.
  16. Magwanga, R. O., Lu, P., Kirungu, J. N., Cai, X., Zhou, Z., Wang, X., Diouf, L., Xu, Y., Hou, Y., Hu, Y., Dong, Q., Wang, K. and Liu, F. (2018). Whole Genome Analysis of Cyclin Dependent Kinase (CDK) Gene Family in Cotton and Functional Evaluation of the Role of CDK4 Gene in Drought and Salt Stress Tolerance in Plants. International Journal of Molecular Sciences, [online] 19(9). doi:https://doi. org/10. 3390/ijms19092625.
  17. Krogh, A., Larsson, B., von Heijne, G. and Sonnhammer, E. L. L. (2001). Predicting transmembrane protein topology with a hidden markov model: application to complete genomes 11 Edited by F. Cohen. Journal of Molecular Biology, [online] 305(3), pp. 567–580. doi:https://doi. org/10. 1006/jmbi. 2000. 4315.
  18. com. (2022). Available at: https://academic. oup. com/nar/article/49/D1/D831/5920517.
  19. Breitwieser, F. P., Lu, J. and Salzberg, S. L. (2017). A review of methods and databases for metagenomic classification and assembly. Briefings in Bioinformatics, 20(4), pp. 1125–1136. doi:https://doi. org/10. 1093/bib/bbx120.
  20. Pontén, F., Jirström, K. and Uhlen, M. (2008). The Human Protein Atlas—a tool for pathology. The Journal of Pathology, 216(4), pp. 387–393. doi:https://doi. org/10. 1002/path. 2440.
  21. Chai, S. J., Ahmad Zabidi, M. M., Gan, S. P., Rajadurai, P., Lim, P. V. H., Ng, C. C., Yap, L. F., Teo, S. H., Lim, K. P., Patel, V. and Cheong, S. C. (2019). An Oncogenic Role for Four-Jointed Box 1 (FJX1) in Nasopharyngeal Carcinoma. Disease Markers, 2019, pp. 1–10. doi:https://doi. org/10. 1155/2019/3857853.
  22. Salzberg, S. L. (2019). Next-generation genome annotation: we still struggle to get it right. Genome Biology, 20(1). doi:https://doi. org/10. 1186/s13059-019-1715-2. ‌

 


Regular Issue Subscription Original Research
Volume 02
Issue 01
Received 19/04/2024
Accepted 01/05/2024
Published 20/08/2024

Check Our other Platform for Workshops in the field of AI, Biotechnology & Nanotechnology.
Check Out Platform for Webinars in the field of AI, Biotech. & Nanotech.