Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2010 Nov;28(11):1181-5.
doi: 10.1038/nbt1110-1181.

In silico research in the era of cloud computing

Joel T Dudley  1 , Atul J Butte
Affiliations

In silico research in the era of cloud computing

Joel T Dudley et al. Nat Biotechnol. 2010 Nov.
No abstract available

PubMed Disclaimer

Conflict of interest statement

Competing financial interests

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1
Layers of reproducible computing in the cloud The reproducibility of scientific compuitng in the cloud can be understood at three layers of scientific computation. (A) Data Layer: Generators of large scientific data sets can publish their data to the cloud as large data volumes (1) and substantial updates to these data volumes can exist in parallel without loss or modification of the previous volume (2). Primary investigators can clone entire data volumes within the cloud (3) and apply custom scripts or software computations (4) to derive published results (5). An indepdendent investigator can obtain digital replicates of the original primary data set, software, and published results within the cloud to replicate a published analysis and compare with published results (6). (B) System Layer: Investigators can set up and conduct scientific computations using cloud-based virtual machine images, incorporating all the software, configuration, and scripts necessary to execute the analysis. The customized machine image can be cloned wholesale and shared with other investigators within the cloud for replicate analyses. (C) Service Layer: Instead of making in-place modifications or updates to the systems or data comprising the underlying infrastructure of a scientific computing service, the entire infrastructure can be virrtualized in the cloud and cloned prior to update or modification to retain the state and characteristics of the previous version of the service. Requests made by external tools or applications through the external service interface could incorporate a version parameter into requests to the service, so that published results citing previous versions of the service can be evaluated for reproducibility.
Figure 1
Figure 1
Layers of reproducible computing in the cloud The reproducibility of scientific compuitng in the cloud can be understood at three layers of scientific computation. (A) Data Layer: Generators of large scientific data sets can publish their data to the cloud as large data volumes (1) and substantial updates to these data volumes can exist in parallel without loss or modification of the previous volume (2). Primary investigators can clone entire data volumes within the cloud (3) and apply custom scripts or software computations (4) to derive published results (5). An indepdendent investigator can obtain digital replicates of the original primary data set, software, and published results within the cloud to replicate a published analysis and compare with published results (6). (B) System Layer: Investigators can set up and conduct scientific computations using cloud-based virtual machine images, incorporating all the software, configuration, and scripts necessary to execute the analysis. The customized machine image can be cloned wholesale and shared with other investigators within the cloud for replicate analyses. (C) Service Layer: Instead of making in-place modifications or updates to the systems or data comprising the underlying infrastructure of a scientific computing service, the entire infrastructure can be virrtualized in the cloud and cloned prior to update or modification to retain the state and characteristics of the previous version of the service. Requests made by external tools or applications through the external service interface could incorporate a version parameter into requests to the service, so that published results citing previous versions of the service can be evaluated for reproducibility.
Figure 1
Figure 1
Layers of reproducible computing in the cloud The reproducibility of scientific compuitng in the cloud can be understood at three layers of scientific computation. (A) Data Layer: Generators of large scientific data sets can publish their data to the cloud as large data volumes (1) and substantial updates to these data volumes can exist in parallel without loss or modification of the previous volume (2). Primary investigators can clone entire data volumes within the cloud (3) and apply custom scripts or software computations (4) to derive published results (5). An indepdendent investigator can obtain digital replicates of the original primary data set, software, and published results within the cloud to replicate a published analysis and compare with published results (6). (B) System Layer: Investigators can set up and conduct scientific computations using cloud-based virtual machine images, incorporating all the software, configuration, and scripts necessary to execute the analysis. The customized machine image can be cloned wholesale and shared with other investigators within the cloud for replicate analyses. (C) Service Layer: Instead of making in-place modifications or updates to the systems or data comprising the underlying infrastructure of a scientific computing service, the entire infrastructure can be virrtualized in the cloud and cloned prior to update or modification to retain the state and characteristics of the previous version of the service. Requests made by external tools or applications through the external service interface could incorporate a version parameter into requests to the service, so that published results citing previous versions of the service can be evaluated for reproducibility.
See this image and copyright information in PMC

References

    1. Gentleman R. Reproducible research: a bioinformatics case study. Stat Appl Genet Mol Biol. 2005;4:Article2. - PubMed
    1. Gil Y, et al. Examining the Challenges of Scientific Workflows. Computer. 2007;40:24–32.
    1. Barker A, van Hemert J. Parallel Processing and Applied Mathematics. 2008:746–753.
    1. Reich M, et al. GenePattern 2.0. Nat Genet. 2006;38:500–501. - PubMed
    1. Hull D, et al. Taverna: a tool for building and running workflows of services. Nucleic Acids Res. 2006;34:W729–732. - PMC - PubMed

Publication types

MeSH terms