A biobank (biorepository or tissue bank) is a repository of human biological material — organs, tissue, blood, cells and other body fluids — that contains at least traces of DNA or RNA that would allow genetic analysis. It originally referred to large population banks of human tissue and related data. 
Biobanks are a new frontier for biomolecular research, clinical genomics and personal medicine that seeks to integrate collections of bio-specimens (blood, DNA, tissue, biopsy specimens, etc) with corresponding patient data such as genetic profiles, medical histories, and lifestyle information.
By combining and comparing biological tissue samples with genetic and historical patient information, researchers will be able to investigate the fundamental mechanisms of diseases in rich new ways. New insights into molecular and genetic processes will lead to better techniques for predicting who may be susceptible to particular illnesses, as well as to more targeted and innovative ways to treat many diseases.
The storage of tissue samples and data linked to them needs to be clearly distinguished. These data comprise information about the donor of the material, such as demographic characteristics, the type of disease associated with the sample, the outcome of the disease, etc.
As medicine and information technologies continue to converge, biobanking offers new abilities to study the complex interaction between genes, the environment and social factors. One element of the movement toward “information-based medicine” and computational biology, biobanking promises to be an essential tool for translating new biomedical knowledge into new clinical practices, diagnostic techniques and preventative treatments.
While biobanking is still in its infancy, some critics question whether behavioral and lifestyle data can be tracked and measured against genetic data in ways that will lead to major breakthroughs. Others challenge the assumption that genetics plays a decisive role in most disease processes.
Guidelines developed by the Ethics Committee of the Swedish Medical Research Council stress the importance of protecting individual data. It advises that codes linking data held in a biobank to an individual should be kept within a public institution such as a university or medical authority. Unauthorized individuals can access electronically stored information for illicit purposes. As a result, people want stricter control over who has access to their information and under what conditions.
Issues of privacy have become entangled with bioinformatics as, increasingly, we rely on technology rather than on human beings to resolve privacy issues. Fears of discrimination by employers and insurers are definitely of increasing importance for participants in genetic research. Many may fear that their genetic information could be shared with third parties (insurers, employers), who sometimes require that the individual provide a general release of his medical records or information relating to his participation in research projects.
It is essential that operational rules be established by Research ethics board (REBs) so that the conditions of access to biobanks are clearly determined and are acceptable to the research participants.
Consent and anonymization in research involving biobanks
Differing terms and norms present serious barriers to an international framework
Bernice S Elger1 (Author photo) and Arthur L Caplan2 (Author photo)
EMBO Rep. 2006 July; 7(7): 661–666.
A biobank management model applicable to biomedical research Christiane Auray-Blais1 and Johane Patenaude Service of Genetics, Department of Pediatrics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, 3001, 12th Avenue North, Sherbrooke, Qc, J1H 5N4, Canada, BMC Med Ethics. 2006; 7: 4.
Nitika Gupta BMI512 F 2007
The necessity of informed consent within the context of research is an absolute imperative. The two main principles to be taken care of for the benefit of participants are:
- obtaining his informed consent
- determining the risk/benefit balance by the REB
It is essential for REBs to put in place means for the logistics involved in the uses of donor's specimens stored in biobanks for the purposes of medical research.
The handling of identifiers, physical and other kinds of security and transfer of information. The breach is a serious issue in pharmagenomics. This informational risk is due to the personal, familial, and social nature of genetic information as well as its potential to discriminate and stigmatize. Points to consider by researchers and Institutional Review Boards (IRBs) have recently been suggested in determining various levels of confidentiality, within the framework of pharmacogenomics research where researchers decide the level of protection best suited for their research protocols.
Key Challenges to Biobanking
- Anonymizing, or de-identifying, samples to protect patient privacy
- Standardizing sample preparation, storage protocols
- Enabling interoperability and data exchange between biobanks
- Resolving issues such as who owns and controls specimens and refining informed consent practices as biobanking expands
Biobanking Related Companies
- Achiever Medical - Advanced Tissue Banking
- BioStorage Technologies
- Integrated Laboratory Services - Biotech (ILSbio)
- LabVantage Solutions
- SeraCare Life Sciences (Genomics Collaborative unit)
Q. What types of technology are used to store all of this data. How much data is it? What kind of processors/equipment is used to analyze the data? How does it capture the data?
A. The spectrum of biobanks varies widely depending on their scope and the types of research they are supporting. The two critical factors that determine how much data they generate depends largely on two factors: 1) how many samples are part of the biobank, and 2) what kinds of molecular data is being generated to support the research endeavor.
Biobanks can range from as few as 10 or 50 samples for a very specific study, to hundreds of thousands of samples for a prospective, large-scale biobanking initiative.
Biobanking research focused on molecular data can generate multiple terabytes daily. For example, some proteomics labs are generating 2-4 TB a day already, and this data intensity is expected to grow as the number of samples increase with high-throughput processing