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Bioinformatics tools: alternative approach for Poly Cystic Ovary Syndrome (PCOS) Detection

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Author Affiliations

  • 1Biotechnology, School of Pharmaceutical and Health care Sciences, CT University, Ludhiana, India
  • 2Biotechnology, School of Pharmaceutical and Health care Sciences, CT University, Ludhiana, India

Int. Res. J. Biological Sci., Volume 10, Issue (1), Pages 14-18, February,10 (2021)

Abstract

Poly cystic ovary syndrome (PCOS) is a complex endocrine disorder prevailing among women of reproductive age, specifically high in teenage girls and has become the most accepted cause of menstrual irregularities and infertility among them. Therefore, it was thought to explore an alternative approach for disease detection, prevention or treatment for PCOS with the help of bioinformatics. Bioinformatics is an expanding field of science involving biology, computer science and mathematics. It is growing in every field of life science including molecular sciences, biotechnology, medicine, agriculture and more. Genetic information stored in the bioinformatics tools can be used to develop personal medicine. In another study, it is said that although the genetics and mechanism of PCOS are not yet understood, the computational tools may be helpful in finding the cause of this syndrome and this will also help in prevention of the disease. In the present study, the gene and genome sequences responsible for causing PCOS have been identified using bioinformatics tools like BLAST, PDB, NCBI. This will help to prevent the disease by genetic manipulations. Finally, the primers have been designed using primer designing tools in NCBI which can be further used for the treatment of the disease by manipulating the identified gene through polymerase chain reaction.

References

  1. Hou, Y.,Wang, Y., Xu, S., Qi, G., & Wu, X. (2019)., Bioinformatics identification of microRNAs involved in polycystic ovary syndrome based on microarray data., Mol. Med. Rep., 20(1), 281-291.
  2. Palvi, S., & Sheveta, V. (2019)., Image Segmentation for detecting Polycystic Ovarian Disease using Deep Neural Network., Int. J. Comp. Sci. Eng., 7(3), 534-537.
  3. Samer, E. H., Lynn B., Layal H. H., Fadi, G. M., & Georges, D. (2016)., Poly Cystic Ovarian Syndrome: An Updated Overview., Front Physiol., 7, 124.
  4. Prathap, A., Subhalakshmi, T. P., & Varghese, P. J. (2018)., A Cross-sectional Study on the Proportion of Anxiety and Depression and Determinants of Quality of Life in Polycystic Ovarian Disease., Ind. J. Psychol. Med., 40(3), 257-262.
  5. Choudhary, A., Jain, S., & Chaudhari, P. (2017)., Prevalence and symptomatology of polycystic ovarian syndrome in Indian women: is there a rising incidence?., Int. J. Reproduction Contracep. Obstetrics Gynecol., 6(11), 4971-4975.
  6. Bharathi, V., Swetha, S., Neerajaa, J., Madhavica, V., Janani, D. M., Rekha, S. N., Ramya, S., & Usha, B. (2017)., An epidemiological survey: Effect of predisposing factors for PCOS in Indian urban and rural population., J. Middle East Fertility Society, 22, 313-316.
  7. Radhakrishnan, R., & Verghese, A. A. (2018)., Study on anxiety and depression among patients with polycystic ovary syndrome., J. Drug Delivery Therapeutics, 8(5), 338-340.
  8. Domecq, J. P., Prutsky, G., Mullan, R. J., Sundaresh, V., Wang, A. T., Erwin, P. J, Welt, C., Ehrmann, D., Montori, V. M., & Murad, M. H. (2013)., Adverse Effects of the Common Treatments for Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis., J. Clinical Endocrinolol. Metabolism, 98(12), 4646-4654.
  9. Bi, X., Zhai, Z., & Wang, S. (2019)., Identification of the key pathways and genes related to polycystic ovary syndrome using bioinformatics analysis., Gen. Physiol. Biophys., 38, 205-214.
  10. Uhlen, M., Fagerberg, L., Hallstrom, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., ... & Ponten, F. (2015)., Tissue-based map of the human proteome., Science, 347(6220).
  11. Zhang, X. Z., Pang, Y. L., & Li, Y. H. (2018)., Computational characterization and identification of human polycystic ovary syndrome genes., Sci Rep., 8, 12949.
  12. Panda, P. K., Rane, R., Chandran, R. R., Singh, S., & Panchal, H. (2016)., Genetics of PCOS: A systematic bioinformatics approach to unveil the proteins responsible for PCOS., Genomics Data, 8, 52-60.
  13. Jie, X. P., Ya, J. T., Fang, F. W., Ning, N.H., Yu, Q. X., Jun, Y. Z., Ye, L., Fan, Q., Qing, M., Jian, X., Jian, Z. S., & He, F. H. (2018)., Aberrant expression and DNA methylation of lipid metabolism genes in PCOS: a new insight into its pathogenesis., Clin. Epigenet., 10, 6.
  14. Tayebe, A., Massoud, S., Reza, A., Mahnaz, A., Iraj, A., Mahnaz, Y., Sara, S. A., Nooshin, S., Jamshid, K., & Mehdi, M. (2015)., Down regulation of adiponectin system in granulosa cells and low levels of HMW adiponectin in PCOS., J. Assisted Reproduction Genetics, 33(1), 101-110.
  15. Chen, L., Ling, M. H., Wang, F. U., Yang, H. Y., Huang, X. Y., Zhou, W., & Sun, H. X. (2017)., Genome wide association study for SNPs associated with PCOS in human patients., Experimental Therapeutic Med., 7(9), 4896-4900.
  16. Thapar, P. (2018)., Bioinformatics- tools and applications., Proceedings from 12th INDIA Com International Conference, New Delhi, India, 14th- 16th March. 5044-5047.