Cloud Computing Tools and Analyses

The intensely data-driven and integrative nature of research in genomic medicine in the post-genomic era presents significant challenges in formulating and testing important translational hypotheses. Advances in high-throughput experimental technologies continue to drive the exponential growth in publicly available genomic data, and the integration and interpretation of these immense volumes of data towards direct, measureable improvements in patient health and clinical outcomes is a grand challenge in genomic medicine. Consequently, genomic medicine has become rooted in and enabled by bioinformatics, engendering the notion of translational bioinformatics. Translational bioinformatics is characterized by the challenge of integrating molecular and clinical data to enable novel translational hypotheses bi-directionally between the domains of biology and medicine. In addition to the scientific challenges, the dimensionality and scale of genomic data sets presents statistical challenges, and also technical hurdles in gaining access to the computational power necessary to test even simple translational hypotheses using genomic data. For example, public data repositories such as the NCBI Gene Expression Omnibus (GEO) enable researchers to ask novel and important translational questions such as, 'Which genes are most likely to be up-regulated specifically in cancers compared to all other human diseases? Given that GEO contains hundreds of thousands of clinical microarray samples, each with tens of thousands of gene abundance measurements, even a straightforward analysis of these data could require many billions or even trillions of comparisons.

A collection of articles on the topic of cloud computing and translational bioinformatics.