The development of large-scale de-identified biomedical databases in the age of genomics-principles and challenges

Abstract

Contemporary biomedical databases include a wide range of information types from various observational and instrumental sources. Among the most important features that unite biomedical databases across the field are high volume of information and high potential to cause damage through data corruption, loss of performance, and loss of patient privacy. Thus, issues of data governance and privacy protection are essential for the construction of data depositories for biomedical research and healthcare. In this paper, we discuss various challenges of data governance in the context of population genome projects. The various challenges along with best practices and current research efforts are discussed through the steps of data collection, storage, sharing, analysis, and knowledge dissemination.

Keywords: Biomedical database; Data governance; Data privacy; Whole genome sequencing.

Fig. 1

Secure storage strategy for a large-scale population sequencing project. All data is stored in a secure data center with partial mirroring for research on site, partial archival mirroring for backup at geographically distant remote sites within the country, and additional mirror copy for protection against unforeseeable rare catastrophic (aka “Black Swan”) events.

Fig. 2

De-identification of clinical data

Fig. 3

Framework for the secure multiparty computation