Top Data Management Challenges in Genomics Research
With the successful completion of the Human Genome Project on April 14, 2003, the mapping of the human genome took approximately 13 years and 3 billion dollars to accomplish. Today, next-generation sequencing (NGS) technology allows a human genome to be sequenced in just a few days for a few hundred dollars.
With ultra-high throughput, speed and scalability, NGS allows researchers to study biological systems in ways never before possible. Combined with clinical data, readily accessible genomic data is driving a revolution in drug discovery by accelerating the development of personalized, targeted treatments in a medical model known as Precision Medicine.
The new field of genomics research, combined with the potential of Precision Medicine to drive improved patient care, has spawned significant growth in the biotechnology industry, with a recent report by Grand View Research projecting that the global biotechnology market will reach 727.1 billion USD by 2025. The promise of Precision Medicine that was envisioned decades ago with the mapping of the human genome is fast becoming a reality.
While genomic research is paving the way for significant advances in healthcare, its practice has led to new challenges for biotechnology firms in terms of increasing data volume, variety and complexity.Given that the data representing a single human genome takes up to 100 gigabytes of storage space, biotech companies engaged in genomic research must develop the computing infrastructure and skills to manage, store, analyze and interpret massive quantities of highly complex data. In this blog, we will explore challenges and best practices involved in addressing data management challenges in genomics research.
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