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Low cost, high performance processing of single particle cryo-electron microscopy data in the cloud.

Cianfrocco MA, Leschziner AE - Elife (2015)

Bottom Line: The advent of a new generation of electron microscopes and direct electron detectors has realized the potential of single particle cryo-electron microscopy (cryo-EM) as a technique to generate high-resolution structures.We tested our computing environment using a publicly available 80S yeast ribosome dataset and estimate that laboratories could determine high-resolution cryo-EM structures for $50 to $1500 per structure within a timeframe comparable to local clusters.Our analysis shows that Amazon's cloud computing environment may offer a viable computing environment for cryo-EM.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular and Cellular Biology, Harvard University, Cambridge, United States.

ABSTRACT
The advent of a new generation of electron microscopes and direct electron detectors has realized the potential of single particle cryo-electron microscopy (cryo-EM) as a technique to generate high-resolution structures. Calculating these structures requires high performance computing clusters, a resource that may be limiting to many likely cryo-EM users. To address this limitation and facilitate the spread of cryo-EM, we developed a publicly available 'off-the-shelf' computing environment on Amazon's elastic cloud computing infrastructure. This environment provides users with single particle cryo-EM software packages and the ability to create computing clusters with 16-480+ CPUs. We tested our computing environment using a publicly available 80S yeast ribosome dataset and estimate that laboratories could determine high-resolution cryo-EM structures for $50 to $1500 per structure within a timeframe comparable to local clusters. Our analysis shows that Amazon's cloud computing environment may offer a viable computing environment for cryo-EM.

No MeSH data available.


Workflow for analyzing cryo-EM data on Amazon's cloud computing infrastructure.After collecting cryo-EM data (Step 1), particles are extracted from the micrographs and prepared for further analysis (Step 2). After logging into an ‘instance’ (Step 3), data are uploaded to a storage server (elastic block storage) (Step 4). At this point, STARcluster can be configured to launch a cluster of 2–30 instances that is mounted with the data from the storage volume (Step 5). A detailed protocol can be found at an accompanying Google site: http://goo.gl/AIwZJz.DOI:http://dx.doi.org/10.7554/eLife.06664.003
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fig1: Workflow for analyzing cryo-EM data on Amazon's cloud computing infrastructure.After collecting cryo-EM data (Step 1), particles are extracted from the micrographs and prepared for further analysis (Step 2). After logging into an ‘instance’ (Step 3), data are uploaded to a storage server (elastic block storage) (Step 4). At this point, STARcluster can be configured to launch a cluster of 2–30 instances that is mounted with the data from the storage volume (Step 5). A detailed protocol can be found at an accompanying Google site: http://goo.gl/AIwZJz.DOI:http://dx.doi.org/10.7554/eLife.06664.003

Mentions: The overall workflow starts with users logging into a virtual machine (‘instance’) on AWS (Figure 1). AWS offers a variety of instance types that have been configured for different computing tasks. For example, instances have been optimized for computing performance, GPU-based calculations, or memory-intensive calculations. After logging onto an instance, storage drives are mounted onto it, allowing data, which can be encrypted for security, to be transferred onto the storage drives (Figure 1).10.7554/eLife.06664.003Figure 1.Workflow for analyzing cryo-EM data on Amazon's cloud computing infrastructure.


Low cost, high performance processing of single particle cryo-electron microscopy data in the cloud.

Cianfrocco MA, Leschziner AE - Elife (2015)

Workflow for analyzing cryo-EM data on Amazon's cloud computing infrastructure.After collecting cryo-EM data (Step 1), particles are extracted from the micrographs and prepared for further analysis (Step 2). After logging into an ‘instance’ (Step 3), data are uploaded to a storage server (elastic block storage) (Step 4). At this point, STARcluster can be configured to launch a cluster of 2–30 instances that is mounted with the data from the storage volume (Step 5). A detailed protocol can be found at an accompanying Google site: http://goo.gl/AIwZJz.DOI:http://dx.doi.org/10.7554/eLife.06664.003
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4440898&req=5

fig1: Workflow for analyzing cryo-EM data on Amazon's cloud computing infrastructure.After collecting cryo-EM data (Step 1), particles are extracted from the micrographs and prepared for further analysis (Step 2). After logging into an ‘instance’ (Step 3), data are uploaded to a storage server (elastic block storage) (Step 4). At this point, STARcluster can be configured to launch a cluster of 2–30 instances that is mounted with the data from the storage volume (Step 5). A detailed protocol can be found at an accompanying Google site: http://goo.gl/AIwZJz.DOI:http://dx.doi.org/10.7554/eLife.06664.003
Mentions: The overall workflow starts with users logging into a virtual machine (‘instance’) on AWS (Figure 1). AWS offers a variety of instance types that have been configured for different computing tasks. For example, instances have been optimized for computing performance, GPU-based calculations, or memory-intensive calculations. After logging onto an instance, storage drives are mounted onto it, allowing data, which can be encrypted for security, to be transferred onto the storage drives (Figure 1).10.7554/eLife.06664.003Figure 1.Workflow for analyzing cryo-EM data on Amazon's cloud computing infrastructure.

Bottom Line: The advent of a new generation of electron microscopes and direct electron detectors has realized the potential of single particle cryo-electron microscopy (cryo-EM) as a technique to generate high-resolution structures.We tested our computing environment using a publicly available 80S yeast ribosome dataset and estimate that laboratories could determine high-resolution cryo-EM structures for $50 to $1500 per structure within a timeframe comparable to local clusters.Our analysis shows that Amazon's cloud computing environment may offer a viable computing environment for cryo-EM.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular and Cellular Biology, Harvard University, Cambridge, United States.

ABSTRACT
The advent of a new generation of electron microscopes and direct electron detectors has realized the potential of single particle cryo-electron microscopy (cryo-EM) as a technique to generate high-resolution structures. Calculating these structures requires high performance computing clusters, a resource that may be limiting to many likely cryo-EM users. To address this limitation and facilitate the spread of cryo-EM, we developed a publicly available 'off-the-shelf' computing environment on Amazon's elastic cloud computing infrastructure. This environment provides users with single particle cryo-EM software packages and the ability to create computing clusters with 16-480+ CPUs. We tested our computing environment using a publicly available 80S yeast ribosome dataset and estimate that laboratories could determine high-resolution cryo-EM structures for $50 to $1500 per structure within a timeframe comparable to local clusters. Our analysis shows that Amazon's cloud computing environment may offer a viable computing environment for cryo-EM.

No MeSH data available.