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Open Access Highly Accessed Methodology article

eHive: An Artificial Intelligence workflow system for genomic analysis

Jessica Severin12, Kathryn Beal1, Albert J Vilella1, Stephen Fitzgerald1, Michael Schuster1, Leo Gordon1, Abel Ureta-Vidal13, Paul Flicek1 and Javier Herrero1*

Author affiliations

1 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK

2 RIKEN Yokohama Institute, Omics Sciences Center (OSC), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan

3 Eagle Genomics Ltd., Babraham Research Campus, Cambridge CB22 3AT, UK

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Citation and License

BMC Bioinformatics 2010, 11:240  doi:10.1186/1471-2105-11-240

Published: 11 May 2010

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/ webcite.