Biomedical cloud computing with Amazon Web Services

In this overview to biomedical computing in the cloud, we discussed two primary ways to use the cloud (a single instance or cluster), provided a detailed example using NGS mapping, and highlighted the associated costs. While many users new to the cloud may assume that entry is as straightforward as...

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Bibliographic Details
Published in:PLoS computational biology 2011-08, Vol.7 (8), p.e1002147-e1002147
Main Authors: Fusaro, Vincent A, Patil, Prasad, Gafni, Erik, Wall, Dennis P, Tonellato, Peter J
Format: Article
Language:English
Subjects:
Application programming interface
Biology
Biomedical research
Cloud computing
Computational Biology
Computer centers
Computer Security
Costs
Data processing
Education
Genomes
Information Storage and Retrieval - economics
Information Storage and Retrieval - methods
Internet
Product introduction
Rapid prototyping
Software
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Summary:In this overview to biomedical computing in the cloud, we discussed two primary ways to use the cloud (a single instance or cluster), provided a detailed example using NGS mapping, and highlighted the associated costs. While many users new to the cloud may assume that entry is as straightforward as uploading an application and selecting an instance type and storage options, we illustrated that there is substantial up-front effort required before an application can make full use of the cloud's vast resources. Our intention was to provide a set of best practices and to illustrate how those apply to a typical application pipeline for biomedical informatics, but also general enough for extrapolation to other types of computational problems. Our mapping example was intended to illustrate how to develop a scalable project and not to compare and contrast alignment algorithms for read mapping and genome assembly. Indeed, with a newer aligner such as Bowtie, it is possible to map the entire African genome using one m2.2xlarge instance in 48 hours for a total cost of approximately $48 in computation time. In our example, we were not concerned with data transfer rates, which are heavily influenced by the amount of available bandwidth, connection latency, and network availability. When transferring large amounts of data to the cloud, bandwidth limitations can be a major bottleneck, and in some cases it is more efficient to simply mail a storage device containing the data to AWS (http://aws.amazon.com/importexport/). More information about cloud computing, detailed cost analysis, and security can be found in references.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1002147