BioSimGrid: grid-enabled biomolecular simulation data storage and analysis

Authors:
Muan Hong Ng;Steven Johnston;Bing Wu;Stuart E. Murdock;Kaihsu Tai;Hans Fangohr;Simon J. Cox;Jonathan W. Essex;Mark S. P. Sansom;Paul Jeffreys
Affiliations:
Southampton e-Science Centre, School of Engineering Sciences, University of Southampton, Southampton, Hants, UK;Southampton e-Science Centre, Southampton, UK;Oxford e-Science Centre, Oxford, UK;School of Chemistry, University of Southampton, Southampton, UK;Department of Biochemistry, University of Oxford, Oxford, UK;Southampton e-Science Centre, Southampton, UK;Southampton e-Science Centre, Southampton, UK;School of Chemistry, University of Southampton, Southampton, UK;Department of Biochemistry, University of Oxford, Oxford, UK;Oxford e-Science Centre, Oxford, UK
Venue:
Future Generation Computer Systems - Collaborative and learning applications of grid technology
Year:
2006

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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 using a 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.