http://hdl.handle.net/2384/3889 null Mon, 20 May 2013 02:30:34 GMT 2013-05-20T02:30:34Z http://hdl.handle.net/2384/196389 Title: xxx Wed, 07 Dec 2011 19:33:37 GMT http://hdl.handle.net/2384/196389 2011-12-07T19:33:37Z http://hdl.handle.net/2384/134474 Title: Bob Schatz the early years Authors: Schatz, Robert Sun, 26 Jun 2011 15:00:52 GMT http://hdl.handle.net/2384/134474 2011-06-26T15:00:52Z http://hdl.handle.net/2384/119826 Title: Anti-tumour activity of bisphosphonates in preclinical models of breast cancer Authors: Holen, Ingunn; Coleman, Robert E Wed, 19 Jan 2011 10:40:54 GMT http://hdl.handle.net/2384/119826 2011-01-19T10:40:54Z http://hdl.handle.net/2384/113895 Title: eHive: an artificial intelligence workflow system for genomic analysis. Authors: Severin, Jessica; Beal, Kathryn; Vilella, Albert J; Fitzgerald, Stephen; Schuster, Michael; Gordon, Leo; Ureta-Vidal, Abel; Flicek, Paul; Herrero, Javier Abstract: BACKGROUND: The Ensembl project produces updates to its comparative genomics resources with each of its several releases per year. During each release cycle approximately two weeks are allocated to generate all the genomic alignments and the protein homology predictions. The number of calculations required for this task grows approximately quadratically with the number of species. We currently support 50 species in Ensembl and we expect the number to continue to grow in the future. RESULTS: We present eHive, a new fault tolerant distributed processing system initially designed to support comparative genomic analysis, based on blackboard systems, network distributed autonomous agents, dataflow graphs and block-branch diagrams. In the eHive system a MySQL database serves as the central blackboard and the autonomous agent, a Perl script, queries the system and runs jobs as required. The system allows us to define dataflow and branching rules to suit all our production pipelines. We describe the implementation of three pipelines: (1) pairwise whole genome alignments, (2) multiple whole genome alignments and (3) gene trees with protein homology inference. Finally, we show the efficiency of the system in real case scenarios. CONCLUSIONS: eHive allows us to produce computationally demanding results in a reliable and efficient way with minimal supervision and high throughput. Further documentation is available at: http://www.ensembl.org/info/docs/eHive/. Fri, 01 Jan 2010 00:00:00 GMT http://hdl.handle.net/2384/113895 2010-01-01T00:00:00Z