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Shanahan HP, Owen AM, Harrison AP. Bioinformatics on the cloud computing platform Azure. PLoS One. 2014;9(7):e102642doi: 10.1371/journal.pone.0102642.
Shanahan, H. P., Owen, A. M., & Harrison, A. P. (2014). Bioinformatics on the cloud computing platform Azure. PloS one, 9(7), e102642. https://doi.org/10.1371/journal.pone.0102642
Shanahan, Hugh P, et al. "Bioinformatics on the cloud computing platform Azure." PloS one vol. 9,7 (2014): e102642. doi: https://doi.org/10.1371/journal.pone.0102642
Shanahan HP, Owen AM, Harrison AP. Bioinformatics on the cloud computing platform Azure. PLoS One. 2014 Jul 22;9(7):e102642. doi: 10.1371/journal.pone.0102642. eCollection 2014. PMID: 25050811; PMCID: PMC4106841.
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PLoS One. 2014 Jul 22;9(7):e102642. doi: 10.1371/journal.pone.0102642. eCollection 2014.

Bioinformatics on the cloud computing platform Azure.

PloS one

Hugh P Shanahan, Anne M Owen, Andrew P Harrison

Affiliations

  1. Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom.
  2. Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, United Kingdom.
  3. Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, United Kingdom; Department of Biological Sciences, University of Essex, Wivenhoe Park, Colchester, United Kingdom.

PMID: 25050811 PMCID: PMC4106841 DOI: 10.1371/journal.pone.0102642

Abstract

We discuss the applicability of the Microsoft cloud computing platform, Azure, for bioinformatics. We focus on the usability of the resource rather than its performance. We provide an example of how R can be used on Azure to analyse a large amount of microarray expression data deposited at the public database ArrayExpress. We provide a walk through to demonstrate explicitly how Azure can be used to perform these analyses in Appendix S1 and we offer a comparison with a local computation. We note that the use of the Platform as a Service (PaaS) offering of Azure can represent a steep learning curve for bioinformatics developers who will usually have a Linux and scripting language background. On the other hand, the presence of an additional set of libraries makes it easier to deploy software in a parallel (scalable) fashion and explicitly manage such a production run with only a few hundred lines of code, most of which can be incorporated from a template. We propose that this environment is best suited for running stable bioinformatics software by users not involved with its development.

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