HSP-HMMER: a tool for protein domain identification on a large scale

Authors:
Bhanu Rekapalli;Christian Halloy;Igor B. Zhulin
Affiliations:
Joint Institute of Computational Sciences (UTK-ORNL), Oak Ridge, TN;Joint Institute of Computational Sciences (UTK-ORNL), Oak Ridge, TN;Joint Institute of Computational Sciences (UTK-ORNL), Oak Ridge, TN
Venue:
Proceedings of the 2009 ACM symposium on Applied Computing
Year:
2009

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Abstract

HMMER is arguably the best tool for protein domain identification, which is essential for biological function prediction. There are many software and hardware enhancements of HMMER; however, most of them are not scalable to a large number of processors. The exponential growth of the number of protein sequences in public databases, which is currently set at more than 13 million, demands the use of HMMER on a very large scale. We have developed a highly scalable parallel (HSP) HMMER approach that enables identification of conserved functional domains in millions of proteins in less than a day using thousands of processing nodes on a supercomputer.