eHive: an artificial intelligence workflow system for genomic analysis.
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/.
Severin
Jessica
J
European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.
Beal
Kathryn
K
Vilella
Albert J
AJ
Fitzgerald
Stephen
S
Schuster
Michael
M
Gordon
Leo
L
Ureta-Vidal
Abel
A
Flicek
Paul
P
Herrero
Javier
J
D030541:Databases, Genetic; D016678:Genome; D023281:Genomics / Q000379:methods*; D012984:Software
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