Data Management Challenges in Next Generation Sequencing

Abstract

Since the early days of the Human Genome Project, data management has been recognized as a key challenge for modern molecular biology research. By the end of the nineties, technologies had been established that adequately supported most ongoing projects, typically built upon relational database management systems. However, recent years have seen a dramatic increase in the amount of data produced by typical projects in this domain. While it took more than ten years, approximately three billion USD, and more than 200 groups worldwide to assemble the first human genome, today’s sequencing machines produce the same amount of raw data within a week, at a cost of approximately 2000 USD, and on a single device. Several national and international projects now deal with (tens of) thousands of genomes, and trends like personalized medicine call for efforts to sequence entire populations. In this paper, we highlight challenges that emerge from this flood of data, such as parallelization of algorithms, compression of genomic sequences, and cloud-based execution of complex scientific workflows. We also point to a number of further challenges that lie ahead due to the increasing demand for translational medicine, i.e., the accelerated transition of biomedical research results into medical practice.