Digitalization & Sustainability Review

Peer Reviewed Article

Vol. 1 No. 1 (2021)

Bioinformatics Tools and IT Infrastructure for High-Throughput Genomic Data Analysis

Submitted
1 April 2021
Published
25-05-2021

Abstract

This work assesses the present status of IT infrastructure and bioinformatics tools necessary for high-throughput genetic data analysis to identify developments, constraints, and policy impacts. The study includes IT solutions, integrated platforms, variant calling, quality control, sequence alignment, and data annotation tools using a secondary data review technique. Principal results show notable advancements in tool capabilities, including the thorough quality evaluations of FastQC and the effective alignment offered by BWA and Minimap2. However, there are still issues like the intricacy of GATK, Bioconductor's high learning curve, and QC tools' scalability. While cloud computing and HPC clusters optimize IT infrastructure and show improved scalability and performance, persistent problems remain with data security and cost management. The policy implications emphasize the necessity of investing in safe and user-friendly tools, standardizing protocols, and putting strong data protection measures in place. Future improvements should prioritize using AI and machine learning, enhancing interoperability, and resolving ethical problems to fully achieve the potential of genomic data analysis in personalized treatment and collaborative research. This thorough analysis highlights the importance of making deliberate policy decisions and maintaining ongoing development to advance genomic science.

References

  1. Addimulam, S., Mohammed, M. A., Karanam, R. K., Ying, D., Pydipalli, R., Patel, B., Shajahan, M. A., Dhameliya, N., & Natakam, V. M. (2020). Deep Learning-Enhanced Image Segmentation for Medical Diagnostics. Malaysian Journal of Medical and Biological Research, 7(2), 145-152. https://mjmbr.my/index.php/mjmbr/article/view/687
  2. Atwood, T. K., Bongcam-Rudloff, E., Brazas, M. E., Corpas, M., Gaudet, P. (2015). GOBLET: The Global Organisation for Bioinformatics Learning, Education and Training. PLoS Computational Biology, 11(4). https://doi.org/10.1371/journal.pcbi.1004143
  3. Bellazzi, R., Masseroli, M., Murphy, S., Shabo, A., Romano, P. (2012). Clinical Bioinformatics: Challenges and Opportunities. BMC Bioinformatics, 13(14), S1. https://doi.org/10.1186/1471-2105-13-S14-S1
  4. Blekherman, G., Laubenbacher, R., Cortes, D. F., Mendes, P., Torti, F. M. (2011). Bioinformatics Tools for Cancer Metabolomics. Metabolomics, 7(3), 329-343. https://doi.org/10.1007/s11306-010-0270-3
  5. Dai, L., Gao, X., Guo, Y., Xiao, J., Zhang, Z. (2012). Bioinformatics Clouds for Big Data Manipulation. Biology Direct, 7, 43. https://doi.org/10.1186/1745-6150-7-43
  6. Gill, S., Christopher, A., Gupta, V., Bansal, P. (2016). Emerging role of Bioinformatics Tools and Software in Evolution of Clinical Research. Perspectives in Clinical Research, 7(3). https://doi.org/10.4103/2229-3485.184782
  7. Hendriksen, R. S., Pedersen, S. K., Leekitcharoenphon, P., Malorny, B., Borowiak, M. (2018). Final Report of ENGAGE - Establishing Next Generation Sequencing ABILITY for Genomic Analysis in Europe. EFSA Supporting Publications, 15(6). https://doi.org/10.2903/sp.efsa.2018.EN-1431
  8. Kashyap, H., Ahmed, H. A., Hoque, N., Roy, S., Bhattacharyya, D. K. (2016). Big Data Analytics in Bioinformatics: Architectures, Techniques, Tools and Issues. Network Modeling Analysis in Health Informatics and Bioinformatics, 5(1), 28. https://doi.org/10.1007/s13721-016-0135-4
  9. Killcoyne, S., del Sol, A. (2014). FIGG: Simulating Populations of Whole Genome Sequences for Heterogeneous Data Analyses. BMC Bioinformatics, 15, 149. https://doi.org/10.1186/1471-2105-15-149
  10. Mohammed, M. A., Mohammed, R., Pasam, P., Addimulam, S. (2018). Robot-Assisted Quality Control in the United States Rubber Industry: Challenges and Opportunities. ABC Journal of Advanced Research, 7(2), 151-162.
  11. Mohammed, R., Addimulam, S., Mohammed, M. A., Karanam, R. K., Maddula, S. S., Pasam, P., & Natakam, V. M. (2017). Optimizing Web Performance: Front End Development Strategies for the Aviation Sector. International Journal of Reciprocal Symmetry and Theoretical Physics, 4, 38-45. https://upright.pub/index.php/ijrstp/article/view/142
  12. Pydipalli, R. (2018). Network-Based Approaches in Bioinformatics and Cheminformatics: Leveraging IT for Insights. ABC Journal of Advanced Research, 7(2), 139-150. https://doi.org/10.18034/abcjar.v7i2.743
  13. Pydipalli, R. (2020). AI-Driven Metabolic Engineering for Microbial Rubber Conversion: IT-enabled Strategies. Asian Journal of Applied Science and Engineering, 9(1), 209-220.
  14. Pydipalli, R., & Tejani, J. G. (2019). A Comparative Study of Rubber Polymerization Methods: Vulcanization vs. Thermoplastic Processing. Technology & Management Review, 4, 36-48. https://upright.pub/index.php/tmr/article/view/132
  15. Richardson, N., Pydipalli, R., Maddula, S. S., Anumandla, S. K. R., & Vamsi Krishna Yarlagadda. (2019). Role-Based Access Control in SAS Programming: Enhancing Security and Authorization. International Journal of Reciprocal Symmetry and Theoretical Physics, 6, 31-42. https://upright.pub/index.php/ijrstp/article/view/133
  16. Rodriguez, M., Tejani, J. G., Pydipalli, R., & Patel, B. (2018). Bioinformatics Algorithms for Molecular Docking: IT and Chemistry Synergy. Asia Pacific Journal of Energy and Environment, 5(2), 113-122. https://doi.org/10.18034/apjee.v5i2.742
  17. Sachani, D. K., & Vennapusa, S. C. R. (2017). Destination Marketing Strategies: Promoting Southeast Asia as a Premier Tourism Hub. ABC Journal of Advanced Research, 6(2), 127-138. https://doi.org/10.18034/abcjar.v6i2.746
  18. Tejani, J. G. (2017). Thermoplastic Elastomers: Emerging Trends and Applications in Rubber Manufacturing. Global Disclosure of Economics and Business, 6(2), 133-144. https://doi.org/10.18034/gdeb.v6i2.737
  19. Tejani, J. G. (2019). Innovative Approaches to Recycling Rubber Waste in the United States. ABC Research Alert, 7(3), 181–192. https://doi.org/10.18034/ra.v7i3.660
  20. Tejani, J. G. (2020). Advancements in Sustainable Rubber Production: Bio-Based Alternatives and Recycling Technologies. ABC Journal of Advanced Research, 9(2), 141-152. https://doi.org/10.18034/abcjar.v9i2.749
  21. Tejani, J., Shah, R., Vaghela, H., Kukadiya, T., Pathan, A. A. (2018). Conditional Optimization of Displacement Synthesis for Pioneered ZnS Nanostructures. Journal of Nanotechnology & Advanced Materials, 6(1), 1-7. https://www.naturalspublishing.com/Article.asp?ArtcID=13193
  22. Yarlagadda, V. K., & Pydipalli, R. (2018). Secure Programming with SAS: Mitigating Risks and Protecting Data Integrity. Engineering International, 6(2), 211–222. https://doi.org/10.18034/ei.v6i2.709

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