title: Bioinformatics on the cloud computing platform Azure.
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.
authors: Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom.
authors: Hugh P
authors: HP
authors: Shanahan
authors: Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, United Kingdom.
authors: Anne M
authors: AM
authors: Owen
authors: Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, United Kingdom; Department of Biological Sciences, University of Essex, Wivenhoe Park, Colchester, United Kingdom.
authors: Andrew P
authors: AP
authors: Harrison
mesh_terms: D019295:Computational Biology; D030541:Databases, Genetic; D006801:Humans; D020407:Internet; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software