ArticleGenome research2008
Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training.
Article in Genome research, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 586 papers.
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Who cites it
586 citing papers in PubMed.
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- A high-quality bread wheat genome unravels the adaptive evolution of wheat end-use quality.Nature plants · 2026Article
- Draft genome sequence and annotation of fungal pathogen Cladosporium tenuissimum SHK1100 causing postharvest sooty spot of fig fruit.BMC genomic data · 2026Article
- Helixer: ab initio prediction of primary eukaryotic gene models combining deep learning and a hidden Markov model.Nature methods · 2026Article
- First high-quality genome assembly of Umbelopsis nana isolated from forest soil.G3 (Bethesda, Md.) · 2026Article
526 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.