Big Data access and infrastructure for modern biology: case studies in data repository utility

• Published in: Annals of the New York Academy of Sciences Vol. 1387; no. 1; pp. 112 - 123
• Main Authors: Boles, Nathan C., Stone, Tyler, Bergeron, Charles, Kiehl, Thomas R.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.01.2017
• Subjects: Access to Information; Animals; Big Data; Biomedical Research - methods; Biomedical Research - trends; Case studies; cloud computing; Cloud Computing - trends; Computer Communication Networks - instrumentation; Computer Communication Networks - trends; data analysis; Data Mining - methods; Data Mining - trends; Decision Making, Computer-Assisted; electrophysiology; gene expression; Genomics - methods; Genomics - trends; Humans; Image Processing, Computer-Assisted; Internet; Researchers; Software; Systems Biology - instrumentation; Systems Biology - methods; Systems Biology - trends; Big Data; data analysis; electrophysiology; cloud computing; gene expression
• ISSN: 0077-8923; 1749-6632
• DOI: 10.1111/nyas.13281

Big Data is no longer solely the purview of big organizations with big resources. Today's routine tools and experimental methods can generate large slices of data. For example, high‐throughput sequencing can quickly interrogate biological systems for the expression levels of thousands of different RNAs, examine epigenetic marks throughout the genome, and detect differences in the genomes of individuals. Multichannel electrophysiology platforms produce gigabytes of data in just a few minutes of recording. Imaging systems generate videos capturing biological behaviors over the course of days. Thus, any researcher now has access to a veritable wealth of data. However, the ability of any given researcher to utilize that data is limited by her/his own resources and skills for downloading, storing, and analyzing the data. In this paper, we examine the necessary resources required to engage Big Data, survey the state of modern data analysis pipelines, present a few data repository case studies, and touch on current institutions and programs supporting the work that relies on Big Data.