Biobanking and omics

Abstract

BACKGROUND: The “Era of Big Data” and “Precision Medicine” is now upon us. That is, interrogation of large data sets obtained from groups of similar patients or from the patient themselves over time will now hypothetically permit therapies to be designed to provide maximal efficacy with minimal side effects. However, such discoveries depend upon recruitment of very large numbers of subjects (tens of thousands) along with their associated biospecimens and medical records. When considering the establishment of a biobank or the refocusing of an existing repository for the purpose of “omics” research (i.e., genomics, metabolomics, proteomics, microbiomics, etc.) and/or precision medicine, there are a number of considerations to ponder. Each of these facets is discussed.

OBJECTIVE: The objective of this review is to describe best practices for the establishment and operations of a biobank that will be used for omics (genomics, proteomics, metabolomics, microbiomics) analyses based on published literature and our own practical experiences.

METHODS: We describe the most commonly described approaches to a variety of biobanking issues, including our own practical experiences over the past 5 years.

RESULTS: Based on the particular biobanking situation and downstream application, we have described best practices based on the literature and own experience, taking into consideration ease of application and costs.

CONCLUSIONS: The banking of various types of clinical biospecimens has many valuable uses but often depends on overall costs versus sample utility. In addition, specimen flexibility is important but is influenced by the ease or difficulty of the application. It is always preferable to collect and stored a biospecimen in a format that allows for multiple types of downstream analyses, but that often requires additional expertise, equipment and reagents that can increase overall costs. We have described the methodologies most successfully applied to many situations.

Keywords

biobanking / genomics / metabolomics / microbiomics / methodology / precision medicine / big data

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