RT Journal
A1 Ter-Hovhannisyan, Vardges
A1 Lomsadze, Alexandre
A1 Chernoff, Yury O.
A1 Borodovsky, Mark
T1 Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training
JF Genome Research 
JO Genome Research 
YR 2008 
FD December 01 
VO 18 
IS 12 
SP 1979 
OP 1990 
DO 10.1101/gr.081612.108 
UL http://genome.cshlp.org/content/18/12/1979.abstract 
AB 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.