We live in a world that is being transformed by the scientific revolution. Traditionally, biobank was a term used to describe a biorepository for biological tissues. Today, biobanks are used in a much broader context and are used to store information not only about human tissue but also about population and disease-based biorepositories, genetic material, non-human material, specimens of endangered species. If we define biobanking in this broad sense, it opens up many new possibilities for researchers studying human and non-human populations.1
There are so many new disciplines and fields with countless potential applications. In medicine, we are entering a new era, where patients, health professionals, and researchers are increasingly working together to gather new insights and develop new forms of diagnosis and treatment. Biobanks are a good example of this shift in research methods.
Globally, a large number of institutional, national, and international biobanking efforts are currently underway. A biobank can be useful in studying complex diseases like cancer, cardiovascular disease, and diabetes when linked with subject data from questionnaires and medical records. Human genome sequencing, post-genomic analysis, computer and bioinformatics developments have been tremendously beneficial to the biobanking industry.2
In medical research, drug development, agriculture, and ecology biobanks are rapidly expanding the availability of information about plants, animals, and microbes, along with extensive stores of human biological materials. Research aiming to advance personalized medicine will be revolutionized by diversity in biobanks and the ability to share data. Biological and genetic data no longer remains in the hands of one lab, allowing researchers to store large amounts of data in communities for access and use by other researchers.3
Biobanks fulfill an essential role in advancing research and improving healthcare delivery by making high-quality, well-characterized, and related samples and data readily available for analysis. Therefore, it is becoming increasingly crucial for biobanks to have a greater capacity and adequate informatics capabilities to meet these growing demands.
By storing human biological samples in cryogenic facilities, such collections could revolutionize medicine by providing researchers with access to population-wide information through which they could study the relationship between genetics and disease. Scientists and researchers save lives every year by using the biobanking tool that enables them to investigate diseases. This endeavor has resulted in a significant reduction or complete elimination of many life-threatening diseases.4
In genomics, metabolomics, therapeutic target generation, proteomics, and molecular epidemiology, biobanks are invaluable data sources for modern science. This tool can contribute significantly to be used in medical research, to understand disease etiology, translational research, and public health advancement.
Data is becoming increasingly important to medical researchers as they ‘think bigger’ than ever before. A lot of samples and data have to be collected and administered, usually from multiple sources. Big data refers to a wide array of computerized technologies and software designed to extract knowledge and take action from very large amounts of heterogeneous data, like different kinds of biological data.5
Computer-based mechanisms are necessary for achieving this high-velocity capture, discovery, and processing. ‘Big data’ requires changes in a number of parameters that directly impact biobanking, including storage requirements and data analysis.6
Furthermore, biobank research could assist in understanding the genetic basis of human diseases. The focus does not seem to be just on the revolution in healthcare. Moreover, animal, plant, and microbiome biobanks are advancing rapidly. There are various types of private and public biobanks around the world, including genetic banks, disease-centric biobanks, blood banks, DNA/RNA banks, virtual biobanks, stem cell banks, tissue banks, and more.
It is evident that Information Technology is the closest partner and most significant component of Biobanking in the 21st century. Therefore, the future of biobanking will depend heavily on information technology. On the other side, modern biobanking poses many challenges in terms of data management and integration. Many hundred gigabytes of data are constantly being added to biobanks by high-throughput profiling machines.7
Contemporary biobanking is turning from a sample-driven to a data-driven approach. Despite this, specific challenges remain for precision medicine in order to fulfill the promise of precision medicine. Currently, medical research is based on analyzing samples with clinical data. Since these associations aren’t very strong, millions of samples and a lot of clinical data are needed. To introduce methods such as big data analytics and AI in precision medicine research, it must first be understood that this is the basic premise.8
Biobanks and biorepositories have become so widespread that they are not limited to one laboratory or department due to the diversity of data that is being collected. The biobank has emerged as a dominant entity amidst globalization, data exchange, and multidisciplinary collaboration. Furthermore, information technology will need to support “flexible and extensible metadata” equipped with consistent terminology useful for high-profile biobanking projects, equipped with data integration capabilities.9
Biobanking has great potential in the future. A number of techniques such as artificial intelligence, deep learning, computational models, and semantic search could revolutionize the world of biobanking. The role of artificial intelligence is expected to be significant in the coming years. Artificial intelligence will be used to determine the quality of biosamples. For example, AI systems could analyze DNA gel electrophoresis images to assess DNA integrity, while digital histopathology images of tissue samples would allow the detection of tumors and necrosis.
Additionally, the AI applications can analyze biospecimens and recommend ones that are appropriate for specific uses based on the content of the research proposal. To perform this task, AI could analyze the reference to preanalytical and analytical variations related to these elements (e.g., the type of sample, the type of analyte, method of analysis, disease targeted, and purpose of research) contained in the research proposal.10
Developing the biobanking infrastructure is now expected to transform the world and will play a significant role in the growth of scientific knowledge and economic development as it will impact our understanding of human health, personalized medicine, drugs, disease, and much more. Now is the perfect time to discover the risks and rewards of biobanking.11
- Coppola, L., Cianflone, A., Grimaldi, A. M., Incoronato, M., Bevilacqua, P., Messina, F., & Salvatore, M. (2019). Biobanking in health care: evolution and future directions. Journal of translational medicine, 17(1), 1-18.
- Paskal, W., Paskal, A. M., Dębski, T., Gryziak, M., & Jaworowski, J. (2018). Aspects of modern biobank activity–comprehensive review. Pathology & Oncology Research, 24(4), 771-785.
- Zohouri, M., & Ghaderi, A. (2020). Biobank; an essential infrastructure for the future of personalized medicine. Archives of Iranian Medicine, 23(1), 59.
- Mendy, M., Lawlor, R. T., van Kappel, A. L., Riegman, P. H., Betsou, F., Cohen, O. D., & Henderson, M. K. (2018). Biospecimens and biobanking in global health. Clinics in Laboratory Medicine, 38(1), 183-207.
- Savatt, J., Pisieczko, C. J., Zhang, Y., Lee, M. T. M., Faucett, W. A., & Williams, J. L. (2019). Biobanks in the Era of Genomic Data. Current Genetic Medicine Reports, 7(3), 153-161.
- Kinkorová, J., & Topolčan, O. (2020). Biobanks in the era of big data: objectives, challenges, perspectives, and innovations for predictive, preventive, and personalised medicine. EPMA Journal, 11(3), 333-341.
- Caenazzo, L., & Tozzo, P. (2020). The Future of Biobanking: What Is Next? BioTech, 9(4), 23
- Grizzle, W. E., Bledsoe, M. J., Al Diffalha, S., Otali, D., & Sexton, K. C. (2019). The utilization of biospecimens: Impact of the choice of biobanking model. Biopreservation and biobanking, 17(3), 230-242.
- Light, E., Wiersma, M., Dive, L., Kerridge, I., Critchley, C., & Lipworth, W. (2019). Disruption, diversity, and global biobanking. The American Journal of Bioethics, 19(5), 45-47.
- Mahajan, A., Vaidya, T., Gupta, A., Rane, S., & Gupta, S. (2019). Artificial intelligence in healthcare in developing nations: The beginning of a transformative journey. Cancer Research, Statistics, and Treatment, 2(2), 182.
- Kinkorová, J., & Topolčan, O. (2018). Biobanks in Horizon 2020: sustainability and attractive perspectives. Epma Journal, 9(4), 345-353.