PT  - JOURNAL ARTICLE
AU  - Jacob M. Luber
AU  - Braden T. Tierney
AU  - Evan M. Cofer
AU  - Chirag J. Patel
AU  - Aleksandar D. Kostic
TI  - Aether: Leveraging Linear Programming for Optimal Cloud Computing In Genomics
AID  - 10.1101/162883
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 162883
4099  - http://biorxiv.org/content/early/2017/07/13/162883.short
4100  - http://biorxiv.org/content/early/2017/07/13/162883.full
AB  - Across biology we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Here, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective, and scalable framework that uses linear programming (LP) to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis while maximizing its efficiency and speed. As a test, we used Aether to de novo assemble 1572 metagenomic samples, a task it completed in merely 13 hours with cost savings of approximately 80% relative to comparable methods.