Dataset Information

Aether: leveraging linear programming for optimal cloud computing in genomics.

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.

SUBMITTER: Luber JM

PROVIDER: S-EPMC5925767 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Aether: leveraging linear programming for optimal cloud computing in genomics.

Luber Jacob M JM Tierney Braden T BT Cofer Evan M EM Patel Chirag J CJ Kostic Aleksandar D AD

Bioinformatics (Oxford, England) 20180501 9

<h4>Motivation</h4>Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities.<h4>Results</h4>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 tra ...[more]

PMID: 29228186

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Project description:The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound's efficacy.

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Dataset Information

Aether: leveraging linear programming for optimal cloud computing in genomics.

Publications

Aether: leveraging linear programming for optimal cloud computing in genomics.

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