Motivation: Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities.
Results: Here, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective and scalable framework that uses linear programming to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users' existing HPC pipelines.
Availability And Implementation: Data utilized are available at https://pubs.broadinstitute.org/diabimmune and with EBI SRA accession ERP005989. Source code is available at (https://github.com/kosticlab/aether). Examples, documentation and a tutorial are available at http://aether.kosticlab.org.
Contact: chirag_patel@hms.harvard.edu or aleksandar.kostic@joslin.harvard.edu.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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| http://dx.doi.org/10.1093/bioinformatics/btx787 | DOI Listing |
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