BioSimGrid: grid-enabled biomolecular simulation data storage and analysis
BioSimGrid: grid-enabled biomolecular simulation data storage and analysis
In computational biomolecular research, large amounts of simulation data are generated to capture the motion of proteins. These massive simulation data can be analysed in a number of ways to reveal the biochemical properties of the proteins. However, the legacy way of storing these data (usually in the laboratory where the simulations have been run) often hinders a wider sharing and easier cross-comparison of simulation results. The data is commonly encoded in a way specific to the simulation package that produced the data and can only be analysed with tools developed specifically for that simulation package. The BioSimGrid platform seeks to provide a solution to these challenges by exploiting the potential of the Grid in facilitating data sharing. By using BioSimGrid either in a scripting or web environment, users can deposit their data and reuse it for analysis. BioSimGrid tools manage the multiple storage locations transparently to the users and provide a set of retrieval and analysis tools for processing the data in a convenient and efficient manner. This paper details the usage and implementation of BioSimGrid usinga combination of commercial databases, the Storage Resource Broker and Python scripts, gluing the building blocks together. It introduces a case study of how BioSimGrid can be used for better storage, retrieval and analysis of biomolecular simulation data.
biomolecular simulation, database, grid computing, storage resource broker, python
657-664
Ng, Muan Hong
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Johnston, Steven
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Wu, Bing
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Murdock, Stuart E.
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Tai, Kaihsu
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Fangohr, Hans
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Cox, Simon J.
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Essex, Jonathan W.
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Sansom, Mark S.P.
ed30b4fc-bc73-4ad7-8c56-f51a67136e4e
Jeffreys, Paul
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May 2006
Ng, Muan Hong
6cdc5c67-aaa2-4153-b64c-3491ad848fce
Johnston, Steven
6b903ec2-7bae-4a56-9c21-eea0a70bfa2b
Wu, Bing
68da5cb1-6a5f-46f9-8173-12d702625220
Murdock, Stuart E.
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Tai, Kaihsu
363c10d0-8583-4bea-bc0f-5be75142e5de
Fangohr, Hans
9b7cfab9-d5dc-45dc-947c-2eba5c81a160
Cox, Simon J.
0e62aaed-24ad-4a74-b996-f606e40e5c55
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Sansom, Mark S.P.
ed30b4fc-bc73-4ad7-8c56-f51a67136e4e
Jeffreys, Paul
a2376cab-ac2a-4905-8bc3-91993d8cd728
Ng, Muan Hong, Johnston, Steven, Wu, Bing, Murdock, Stuart E., Tai, Kaihsu, Fangohr, Hans, Cox, Simon J., Essex, Jonathan W., Sansom, Mark S.P. and Jeffreys, Paul
(2006)
BioSimGrid: grid-enabled biomolecular simulation data storage and analysis.
Future Generation Computer Systems, 6 (22), .
(doi:10.1016/j.future.2005.10.005).
Abstract
In computational biomolecular research, large amounts of simulation data are generated to capture the motion of proteins. These massive simulation data can be analysed in a number of ways to reveal the biochemical properties of the proteins. However, the legacy way of storing these data (usually in the laboratory where the simulations have been run) often hinders a wider sharing and easier cross-comparison of simulation results. The data is commonly encoded in a way specific to the simulation package that produced the data and can only be analysed with tools developed specifically for that simulation package. The BioSimGrid platform seeks to provide a solution to these challenges by exploiting the potential of the Grid in facilitating data sharing. By using BioSimGrid either in a scripting or web environment, users can deposit their data and reuse it for analysis. BioSimGrid tools manage the multiple storage locations transparently to the users and provide a set of retrieval and analysis tools for processing the data in a convenient and efficient manner. This paper details the usage and implementation of BioSimGrid usinga combination of commercial databases, the Storage Resource Broker and Python scripts, gluing the building blocks together. It introduces a case study of how BioSimGrid can be used for better storage, retrieval and analysis of biomolecular simulation data.
Text
Ng_06pp.pdf
- Accepted Manuscript
More information
Submitted date: 15 August 2005
Published date: May 2006
Keywords:
biomolecular simulation, database, grid computing, storage resource broker, python
Identifiers
Local EPrints ID: 65004
URI: http://eprints.soton.ac.uk/id/eprint/65004
PURE UUID: 0d4490b2-91b2-408c-8be2-839823bcb047
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Date deposited: 27 Jan 2009
Last modified: 16 Mar 2024 03:09
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Contributors
Author:
Muan Hong Ng
Author:
Bing Wu
Author:
Stuart E. Murdock
Author:
Kaihsu Tai
Author:
Mark S.P. Sansom
Author:
Paul Jeffreys
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