Microbase2.0: A Generic Framework for Computationally Intensive Bioinformatics Workflows in the Cloud

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doi doi:10.2390/biecoll-jib-2012-212
submission May 16, 2012
published September 24, 2012
NCBI PubMed PubMed ID 23001322

Keith Flanagan, Sirintra Nakjang, Jennifer Hallinan, Colin Harwood, Robert P. Hirt, Matthew R. Pocock and Anil Wipat

Correspondence should be addressed to:
Anil Wipat
School of Computing Science, Newcastle University, Newcastle upon Tyne, NE7 4RU, UK
ku.ca.eltsacwen@nulltapiW.linA


Abstract

As bioinformatics datasets grow ever larger, and analyses become increasingly complex, there is a need for data handling infrastructures to keep pace with developing technology. One solution is to apply Grid and Cloud technologies to address the computational requirements of analysing high throughput datasets. We present an approach for writing new, or wrapping existing applications, and a reference implementation of a framework, Microbase2.0, for executing those applications using Grid and Cloud technologies. We used Microbase2.0 to develop an automated Cloud-based bioinformatics workflow executing simultaneously on two different Amazon EC2 data centres and the Newcastle University Condor Grid. Several CPU years’ worth of computational work was performed by this system in less than two months. The workflow produced a detailed dataset characterising the cellular localisation of 3,021,490 proteins from 867 taxa, including bacteria, archaea and unicellular eukaryotes. Microbase2.0 is freely available from http://www.microbase.org.uk/.

Reference

Keith Flanagan, Sirintra Nakjang, Jennifer Hallinan, Colin Harwood, Robert P. Hirt, Matthew R. Pocock and Anil Wipat. Microbase2.0: A Generic Framework for Computationally Intensive Bioinformatics Workflows in the Cloud. Journal of Integrative Bioinformatics, 9(2):212, 2012. Online Journal: http://journal.imbio.de/index.php?paper_id=212
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