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The advent of high-throughput sequencing, coupled with advances in computational methods, has enabled genome-wide dissection of genetics, evolution, and disease, with nucleotide resolution. The discoveries derived from genomics promise benefits to basic research, biotechnology, and medicine; however, the speed and affordability of sequencing has resulted in a flood of “big data” in the life sciences. In addition, the current heterogeneity of sequencing platforms and diversity of applications complicate the development of tools for analysis, and this has slowed widespread adoption of the technology. Making sense of the data and delivering actionable insight requires improved computational infrastructure, new methods for interpreting the data, and unique collaborative approaches. Here we review the role of big data in genomics, its impact on the development of tools for collaborative analysis of genomes, and successes and ongoing challenges in coping with big data.
Ward et al. (Tue,) studied this question.