Big data challenges and opportunities in high-throughput sequencing

  • Ward R
  • Schmieder R
  • Highnam G
  • et al.
N/ACitations
Citations of this article
70Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

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

Cite

CITATION STYLE

APA

Ward, R. M., Schmieder, R., Highnam, G., & Mittelman, D. (2013). Big data challenges and opportunities in high-throughput sequencing. Systems Biomedicine, 1(1), 29–34. https://doi.org/10.4161/sysb.24470

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free