INFORMATION TECHNOLOGIES DRIVING INNOVATION IN BIOLOGICAL SCIENCE: A FOCUS ON BIOINFORMATICS APPLICATION DEVELOPMENT
Main Article Content
Keywords
sequences, proteins, DNA, RNA, bioinformatics, biology, biological data, technologies
Abstract
Background: DNA sequences contain vast amounts of information, necessitating advanced computing methods and data modeling techniques for analysis. The University of Technology's Systems Engineering and Computer Science program's (Iraqi research group/university) research group aims to leverage information technologies to drive significant progress in biological science.
Objective: This study explores computational methods conducive to developing bioinformatics applications to expedite computation, enhance inference times, and bolster the reliability of analyses derived from DNA sequence data.
Methods: The research scrutinizes various computational methods suitable for bioinformatics applications within the framework of the University of Technology's Systems Engineering and Computer Science program's (IRAQI RESEARCH GROUP/UNIVERSITY) research group.
Results: Identified computational methods exhibit promise in accelerating analysis processes and improving the reliability of results derived from DNA sequence data. These methods serve as foundational tools for advancing scientific inquiry in biological science.
Conclusion: By employing sophisticated computational methods, such as those investigated within the University of Technology's Systems Engineering and Computer Science program's IRAQI RESEARCH GROUP/UNIVERSITY research group, bioinformatics applications can achieve significant advancements, facilitating progress in biological science.
References
2. Beck, D., Ben Maamar, M., & Skinner, M. K. (2022). Genome-wide CpG density and DNA methylation analysis method (MeDIP, RRBS, and WGBS) comparisons. Epigenetics, 17(5), 518-530.
3. Chen, C., Wu, Y., Li, J., Wang, X., Zeng, Z., Xu, J., Liu, Y., Feng, J., Chen, H., & He, Y. (2023). TBtools-II: A “one for all, all for one” bioinformatics platform for biological big-data mining. Molecular Plant, 16(11), 1733-1742.
4. Ge, H., Yan, Y., Wu, D., Huang, Y., & Tian, F. (2018). Potential role of LINC00996 in colorectal cancer: a study based on data mining and bioinformatics. OncoTargets and therapy, 4845-4855.
5. Jovic, D., Liang, X., Zeng, H., Lin, L., Xu, F., & Luo, Y. (2022). Single‐cell RNA sequencing technologies and applications: A brief overview. Clinical and Translational Medicine, 12(3), e694.
6. Jung, Y., Lee, S., Choi, H.-S., Kim, S.-N., Lee, E., Shin, Y., Seo, J., Kim, B., Jung, Y., & Kim, W. K. (2011). Clinical validation of colorectal cancer biomarkers identified from bioinformatics analysis of public expression data. Clinical Cancer Research, 17(4), 700-709.
7. Li, Y., Ma, L., Wu, D., & Chen, G. (2021). Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine. Briefings in Bioinformatics, 22(5), bbab024.
8. Liu, M., Clarke, L. J., Baker, S. C., Jordan, G. J., & Burridge, C. P. (2020). A practical guide to DNA metabarcoding for entomological ecologists. Ecological entomology, 45(3), 373-385.
9. Maljkovic Berry, I., Melendrez, M. C., Bishop-Lilly, K. A., Rutvisuttinunt, W., Pollett, S., Talundzic, E., Morton, L., & Jarman, R. G. (2020). Next generation sequencing and bioinformatics methodologies for infectious disease research and public health: approaches, applications, and considerations for development of laboratory capacity. The Journal of infectious diseases, 221(Supplement_3), S292-S307.
10. Miller, S., Chiu, C., Rodino, K. G., & Miller, M. B. (2020). Point-counterpoint: should we be performing metagenomic next-generation sequencing for infectious disease diagnosis in the clinical laboratory? Journal of clinical microbiology, 58(3), 10.1128/jcm. 01739-01719.
11. Novák, P., Neumann, P., & Macas, J. (2020). Global analysis of repetitive DNA from unassembled sequence reads using RepeatExplorer2. Nature Protocols, 15(11), 3745-3776.
12. Phan, J. H., Quo, C.-F., & Wang, M. D. (2006). Functional genomics and proteomics in the clinical neurosciences: data mining and bioinformatics. Progress in Brain Research, 158, 83-108.
13. Qi, Y., Qi, H., Liu, Z., He, P., & Li, B. (2019). Bioinformatics analysis of key genes and pathways in colorectal cancer. Journal of Computational Biology, 26(4), 364-375.
14. Qian, X.-B., Chen, T., Xu, Y.-P., Chen, L., Sun, F.-X., Lu, M.-P., & Liu, Y.-X. (2020). A guide to human microbiome research: study design, sample collection, and bioinformatics analysis. Chinese Medical Journal, 133(15), 1844-1855.
15. Shanbehzadeh, M., Nopour, R., & Kazemi-Arpanahi, H. (2021). Comparison of four data mining algorithms for predicting colorectal cancer risk. Journal of Advances in Medical and Biomedical Research, 29(133), 100-108.
16. Shen, W., Song, Z., Zhong, X., Huang, M., Shen, D., Gao, P., Qian, X., Wang, M., He, X., & Wang, T. (2022). Sangerbox: a comprehensive, interaction‐friendly clinical bioinformatics analysis platform. Imeta, 1(3), e36.
17. Sigsgaard, E. E., Jensen, M. R., Winkelmann, I. E., Møller, P. R., Hansen, M. M., & Thomsen, P. F. (2020). Population‐level inferences from environmental DNA—Current status and future perspectives. Evolutionary Applications, 13(2), 245-262.
18. Thareja, P., & Chhillar, R. S. (2022). A detailed survey on data mining based optimization schemes for bioinformatics applications. ECS Transactions, 107(1), 4689.
19. Tyagi, A., Sharma, A., & Bhardwaj, M. (2022). Future of bioinformatics in India: A survey. International Journal of Health Sciences(II), 431187.
20. Urban, L., Holzer, A., Baronas, J. J., Hall, M. B., Braeuninger-Weimer, P., Scherm, M. J., Kunz, D. J., Perera, S. N., Martin-Herranz, D. E., & Tipper, E. T. (2021). Freshwater monitoring by nanopore sequencing. Elife, 10, e61504.
21. Vaisvila, R., Ponnaluri, V. C., Sun, Z., Langhorst, B. W., Saleh, L., Guan, S., Dai, N., Campbell, M. A., Sexton, B. S., & Marks, K. (2021). Enzymatic methyl sequencing detects DNA methylation at single-base resolution from picograms of DNA. Genome research, 31(7), 1280-1289.
22. van der Loos, L. M., & Nijland, R. (2021). Biases in bulk: DNA metabarcoding of marine communities and the methodology involved. Molecular Ecology, 30(13), 3270-3288.
23. Wood, A., Najarian, K., & Kahrobaei, D. (2020). Homomorphic encryption for machine learning in medicine and bioinformatics. ACM Computing Surveys (CSUR), 53(4), 1-35.
24. Yue, C., Liang, C., Li, P., Yan, L., Zhang, D., Xu, Y., Wei, Z., & Wu, J. (2019). DUXAP8 a pan-cancer prognostic marker involved in the molecular regulatory mechanism in hepatocellular carcinoma: a comprehensive study based on data mining, bioinformatics, and in vitro validation. OncoTargets and therapy, 11637-11650.
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Zeshan Anwar
M. Phil Biotechnology, University of Agriculture Faisalabad, Pakistan
Abdur Rahman
Scholar Accounting and Technology, School of Business, Emporia State University, USA
Bonface Obare
Research Associate, Department of Biotechnology, University of Pretoria, South Africa
Mohanapriya N
Lecturer, Department of Computer Science and ICT, Cavendish University Lusaka, Zambia
Tariq Rafique
Assistant Professor, Dadabhoy Institute of Higher Education, Karachi, Pakistan
Hrishitva Patel
PhD (Pursuing), Information Systems, University of Texas at San Antonio, United States of America
Rabia Tehseen
Assistant professor, Department of Computer Science, University of Central Punjab, Lahore, Pakistan