Title:
Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training
Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training
| dc.contributor.author | Ter-Hovhannisyan,Vardges | en_US |
| dc.contributor.author | Lomsadze, Alexandre | en_US |
| dc.contributor.author | Chernoff, Yury O. | en_US |
| dc.contributor.author | Borodovsky, Mark | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Biology | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. Dept. of Biomedical Engineering | en_US |
| dc.contributor.corporatename | Emory University. Dept. of Biomedical Engineering | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | en_US |
| dc.date.accessioned | 2013-10-03T20:24:02Z | |
| dc.date.available | 2013-10-03T20:24:02Z | |
| dc.date.issued | 2008-12 | |
| dc.description | ©2008 by Cold Spring Harbor Laboratory | en_US |
| dc.description | DOI: 10.1101/gr.081612.108 | en_US |
| dc.description.abstract | We describe a new ab initio algorithm, GeneMark-ES version 2, that identifies protein-coding genes in fungal genomes. The algorithm does not require a predetermined training set to estimate parameters of the underlying hidden Markov model (HMM). Instead, the anonymous genomic sequence in question is used as an input for iterative unsupervised training. The algorithm extends our previously developed method tested on genomes of Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. To better reflect features of fungal gene organization, we enhanced the intron submodel to accommodate sequences with and without branch point sites. This design enables the algorithm to work equally well for species with the kinds of variations in splicing mechanisms seen in the fungal phyla Ascomycota, Basidiomycota, and Zygomycota. Upon self-training, the intron submodel switches on in several steps to reach its full complexity. We demonstrate that the algorithm accuracy, both at the exon and the whole gene level, is favorably compared to the accuracy of gene finders that employ supervised training. Application of the new method to known fungal genomes indicates substantial improvement over existing annotations. By eliminating the effort necessary to build comprehensive training sets, the new algorithm can streamline and accelerate the process of annotation in a large number of fungal genome sequencing projects | en_US |
| dc.identifier.citation | Vardges Ter-Hovhannisyan, Alexandre Lomsadze,Yury O. Chernoff, and Mark Borodovsky, "Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training," Genome Research, (December 2008) 18:1979–1990. | en_US |
| dc.identifier.doi | 10.1101/gr.081612.108 | |
| dc.identifier.issn | 1088-9051 | |
| dc.identifier.uri | http://hdl.handle.net/1853/49178 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Cold Spring Harbor Laboratory Press | en_US |
| dc.subject | Gene prediction | en_US |
| dc.subject | Ab initio algorithm | en_US |
| dc.subject | Fungal genomes | en_US |
| dc.subject | Enhanced intron submodel | en_US |
| dc.title | Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training | en_US |
| dc.type | Text | |
| dc.type.genre | Article | |
| dspace.entity.type | Publication | |
| local.contributor.author | Chernoff, Yury O. | |
| local.contributor.author | Borodovsky, Mark | |
| local.contributor.corporatename | College of Sciences | |
| local.contributor.corporatename | School of Biological Sciences | |
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| relation.isOrgUnitOfPublication | 85042be6-2d68-4e07-b384-e1f908fae48a | |
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