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High throughput profile-profile based fold recognition for the entire human proteome

McGuffin, L. J. ORCID:, Smith, R. T., Bryson, K., Sorensen, S. A. and Jones, D. T. (2006) High throughput profile-profile based fold recognition for the entire human proteome. BMC Bioinformatics, 7 (1). 288. ISSN 1471-2105

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To link to this item DOI: 10.1186/1471-2105-7-288


BACKGROUND: In order to maintain the most comprehensive structural annotation databases we must carry out regular updates for each proteome using the latest profile-profile fold recognition methods. The ability to carry out these updates on demand is necessary to keep pace with the regular updates of sequence and structure databases. Providing the highest quality structural models requires the most intensive profile-profile fold recognition methods running with the very latest available sequence databases and fold libraries. However, running these methods on such a regular basis for every sequenced proteome requires large amounts of processing power.In this paper we describe and benchmark the JYDE (Job Yield Distribution Environment) system, which is a meta-scheduler designed to work above cluster schedulers, such as Sun Grid Engine (SGE) or Condor. We demonstrate the ability of JYDE to distribute the load of genomic-scale fold recognition across multiple independent Grid domains. We use the most recent profile-profile version of our mGenTHREADER software in order to annotate the latest version of the Human proteome against the latest sequence and structure databases in as short a time as possible. RESULTS: We show that our JYDE system is able to scale to large numbers of intensive fold recognition jobs running across several independent computer clusters. Using our JYDE system we have been able to annotate 99.9% of the protein sequences within the Human proteome in less than 24 hours, by harnessing over 500 CPUs from 3 independent Grid domains. CONCLUSION: This study clearly demonstrates the feasibility of carrying out on demand high quality structural annotations for the proteomes of major eukaryotic organisms. Specifically, we have shown that it is now possible to provide complete regular updates of profile-profile based fold recognition models for entire eukaryotic proteomes, through the use of Grid middleware such as JYDE.

Item Type:Article
Divisions:Life Sciences > School of Biological Sciences
Interdisciplinary centres and themes > Institute for Cardiovascular and Metabolic Research (ICMR)
ID Code:10197
Uncontrolled Keywords:*Algorithms, Amino Acid Sequence, Artificial Intelligence, Humans, Molecular Sequence Data, Pattern Recognition, Automated/*methods, Protein Folding, Proteome/*chemistry/*classification, Research Support, Non-U.S. Gov't, Sequence Alignment/*methods, Sequence Analysis, Protein
Publisher:BioMed Central


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