eHive: An Artificial Intelligence workflow system for genomic analysis
<p>Abstract</p> <p>Background</p> <p>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 pro...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2010-05-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/11/240 |
| _version_ | 1811296260308598784 |
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| author | Gordon Leo Schuster Michael Fitzgerald Stephen Vilella Albert J Beal Kathryn Severin Jessica Ureta-Vidal Abel Flicek Paul Herrero Javier |
| author_facet | Gordon Leo Schuster Michael Fitzgerald Stephen Vilella Albert J Beal Kathryn Severin Jessica Ureta-Vidal Abel Flicek Paul Herrero Javier |
| author_sort | Gordon Leo |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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: <url>http://www.ensembl.org/info/docs/eHive/</url>.</p> |
| first_indexed | 2024-04-13T05:45:54Z |
| format | Article |
| id | doaj.art-6558564710b14e748886b1d4fd3f56cc |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T05:45:54Z |
| publishDate | 2010-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-6558564710b14e748886b1d4fd3f56cc2022-12-22T02:59:57ZengBMCBMC Bioinformatics1471-21052010-05-0111124010.1186/1471-2105-11-240eHive: An Artificial Intelligence workflow system for genomic analysisGordon LeoSchuster MichaelFitzgerald StephenVilella Albert JBeal KathrynSeverin JessicaUreta-Vidal AbelFlicek PaulHerrero Javier<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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: <url>http://www.ensembl.org/info/docs/eHive/</url>.</p>http://www.biomedcentral.com/1471-2105/11/240 |
| spellingShingle | Gordon Leo Schuster Michael Fitzgerald Stephen Vilella Albert J Beal Kathryn Severin Jessica Ureta-Vidal Abel Flicek Paul Herrero Javier eHive: An Artificial Intelligence workflow system for genomic analysis BMC Bioinformatics |
| title | eHive: An Artificial Intelligence workflow system for genomic analysis |
| title_full | eHive: An Artificial Intelligence workflow system for genomic analysis |
| title_fullStr | eHive: An Artificial Intelligence workflow system for genomic analysis |
| title_full_unstemmed | eHive: An Artificial Intelligence workflow system for genomic analysis |
| title_short | eHive: An Artificial Intelligence workflow system for genomic analysis |
| title_sort | ehive an artificial intelligence workflow system for genomic analysis |
| url | http://www.biomedcentral.com/1471-2105/11/240 |
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