Biobanks are rich collections of data and biospecimens, specifically developed as resources for research.1 These repositories are needed in multiple sectors, including pharma, academia, animal breeding and charitable foundations, and are important for the progress of research into disease pathologies and population-based studies in the fields of epigenetics, preventative medical programs, rare diseases and epidemiology.2 Biospecimens can come from a variety of species, and range from samples of specific tumor types to umbilical cord, blood and cerebrospinal fluid.
Ever-increasing quantities of complex data from a large variety of biospecimens pose a challenge for biobanks that must effectively manage their data in order to facilitate collaboration and better advance research. Similarly, researchers across a complex network of partners need to be able to locate and procure the right biospecimens for their studies, from often large and diverse databases. Cloud-based laboratory information management systems (LIMS) are emerging as a better way to store, analyze and share biospecimens, offering an opportunity for biobanks looking to invest in effective data management solutions to support these interrelated goals.
Modern biobanks need more from their sample-tracking systems
Biobanks need to manage not only the physical storage of biospecimens, but also associate the patient demographic, disease, consent, assay results and other metadata with the sample. Information like how many times a sample has been frozen, thawed and refrozen impacts the specimen integrity, and biobanks need to track this information to determine if a specimen can or cannot be used for further testing.
Traditional sample tracking systems are not designed to effectively handle such large amounts of complex data, making it difficult to manage information in a way that is sufficiently dynamic, accessible and reliable. Inconsistency is also an issue, as storage time variability can result from different collection protocols3 and inconsistencies in data entry can occur in “free text” formats.4 As biospecimens can be shared across different clinics and studies, it is critical that specimens are of consistent quality.5 The increasingly collaborative nature of research means that the ability to share data with other groups is becoming essential – yet not all systems offer the required flexibility with ease.
These inefficiencies have far-reaching consequences. In-house system maintenance can be demanding on resources, as well as a major source of frustration. Further afield, specimen misidentification can contribute to erroneous data and the reproducibility crisis. If biobanks are not easily “searchable”, researchers do not know what biospecimens and data might be accessible to them – creating an R&D bottleneck.
Biobanking organizations have a lot to coordinate, from specimen preparation, shipping, storage and retrieval, to inventory management and reporting. Compliance with data privacy laws needs to be ensured (e.g., the HIPAA in the US: Health Insurance Portability and Accountability Act) while continuing to distribute biological resources effectively.6 With the “-omics” revolution well underway, biobanks now need further support for their big data operations.
Digital solutions are transforming biobanking infrastructure
Data associated with stored biospecimens needs to be accessible both within an organization and to external partners, and organized in a way that allows researchers to find the high-quality specimens they require for their experiments. As biobanking workflows vary across organizations and change over time, biobanks now need data management support in a more personalized and long-term capacity. To meet this need, cloud-based platforms have been developed that can be tailored to specific requirements.
Rather than having an IT specialist develop a platform, more personalized cloud-based technology is available that incorporates a solid understanding of the end-user’s perspective. Using a more individualized approach to development enables the creation of streamlined systems that reflect the order of real-life workflows.
Cloud-based technology enables biobanks to scale quickly to demand, while providing the ability to capture all related data in one place. For example, information on reagents and instruments used, date and time of movement, and freeze/thaw processes can be stored with raw files detailing assay results, DNA sequencing and genotyping analysis files. This traceability makes it easy to see if other analyses have already been performed on a sample.
Data management software for biobanking now extends beyond managing biospecimens; cloud-based LIMS can be used to manage requests, track stock of standards and reagents, and streamline and prioritize analyses. Validated security parameters help to ensure adherence with HIPAA and data privacy rules. Unlike introducing new on-site solutions, shifting to new cloud platforms can happen quickly, critically avoiding gaps in database coverage.
Benefits of cloud-based technology in biobanking
Cloud-based technology can benefit biobanks on many levels – from improving the efficiency of day-to-day operations to supporting long-term growth and change. With more streamlined and consistent processes, data entry and access are easier, and the potential for error is greatly reduced. An intuitive interface also helps in this respect, as it reduces the risk of someone forgetting to log critical details. Backup checks can further help improve accuracy, either by requesting that critical data elements are entered in duplicate, or by linking barcode scanners to the database.4
Cloud-based technology can strengthen security by utilizing multiple encryption layers, making compliance with HIPAA and other privacy laws easier. Management of specimen chain-of-custody records is also simplified as aliquots and derivatives can be tracked with absolute certainty.4 Unlike many traditional systems, cloud computing employs automatic upgrades and data backups, ensuring no data loss.7 Another major benefit is that maintenance and overhead costs are reduced, as there is no need for physical on-site data servers.4 Importantly, cloud-based technology also provides the flexibility to scale up or down, according to the organization’s growth.
Imagine a seamless sample-tracking experience
● You’re inundated with requests for access to your samples. However, accepting and rejecting access is straightforward. Your request forms are standardized so you can quickly access everything you need to make your decisions.
● Your vital supplies and consumables never run out, because your team has a near-automated system for monitoring and ordering.
● One patient withdraws consent. A system administrator triggers the destruction of those samples, so you do not need to manage the request.
● An external research group requests access to a data set. You enable continuous sharing. There’s no second-guessing which files are the most recent. The researchers can see which reagents and equipment were used to attain the data they have in front of them.
● One client asks if their request can be fast-tracked. You simply adjust the priority of assays to be run that week.
● Your colleague inspects a system report, and identifies and corrects a workflow bottleneck, giving you more time to consider a new collaboration proposal.
Cloud storage is arguably the best solution for supporting the rapidly growing biobanking needs. Streamlined data management software extends beyond sample tracking to reduce costs, shorten timelines and provide an unprecedented level of security and flexibility. Biobanking teams can have room to grow and adapt, and the possibilities for customized data management solutions are endless.
- UK Biobank Ethics and Governance Council. Report on Public meeting of the UK Biobank Ethics and Governance Council, 11th June 2007 (2007).
- Shaw, D. M., Elger, B. S., & Colledge, F. (2014). What is a biobank? Differing definitions among biobank stakeholders. Clinical Genetics, 85(3), 223–227. https://doi.org/10.1111/cge.12268
- McLerran, D., Grizzle, W. E., Feng, Z., Bigbee, W. L., Banez, L. L., Cazares, L. H., Chan, D. W., Diaz, J., Izbicka, E., Kagan, J., Malehorn, D. E., Malik, G., Oelschlager, D., Partin, A., Randolph, T., Rosenzweig, N., Srivastava, S., Srivastava, S., Thompson, I. M., … Semmes, O. J. (2008). Analytical Validation of Serum Proteomic Profiling for Diagnosis of Prostate Cancer: Sources of Sample Bias. Clinical Chemistry, 54(1), 44–52. https://doi.org/10.1373/clinchem.2007.091470
- Im, K., Gui, D., & Yong, W. H. (2019). An Introduction to Hardware, Software, and Other Information Technology Needs of Biomedical Biobanks. In W. H. Yong (Ed.), Biobanking (Vol. 1897, pp. 17–29). Springer New York. https://doi.org/10.1007/978-1-4939-8935-5_3
- Vaught, J., & Lockhart, N. C. (2012). The evolution of biobanking best practices. Clinica Chimica Acta, 413(19–20), 1569–1575. https://doi.org/10.1016/j.cca.2012.04.030
- Takai-Igarashi, T., Kinoshita, K., Nagasaki, M., Ogishima, S., Nakamura, N., Nagase, S., Nagaie, S., Saito, T., Nagami, F., Minegishi, N., Suzuki, Y., Suzuki, K., Hashizume, H., Kuriyama, S., Hozawa, A., Yaegashi, N., Kure, S., Tamiya, G., Kawaguchi, Y., … Yamamoto, M. (2017). Security controls in an integrated Biobank to protect privacy in data sharing: Rationale and study design. BMC Medical Informatics and Decision Making, 17(1), 100. https://doi.org/10.1186/s12911-017-0494-5
- Paul, S., Gade, A., & Mallipeddi, S. (2017). The State of Cloud-Based Biospecimen and Biobank Data Management Tools. Biopreservation and Biobanking, 15(2), 169–172.
Nicole Rose is a senior manager at Thermo Fisher Scientific