Aether: leveraging linear programming for optimal cloud computing in genomics.
Bioinformatics 2019;
34:1565-1567. [PMID:
29228186 PMCID:
PMC5925767 DOI:
10.1093/bioinformatics/btx787]
[Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 12/07/2017] [Indexed: 01/30/2023] Open
Abstract
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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