| GeneMark.hmm eukaryotic |
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Eukaryotic GeneMark.hmm with supervised training was not described in any publication as a stand alone algorithm.
However, it was used and evaluated in several projects e.g. in Pavy et al. "Evaluation of gene prediction software using a genomic data set: application to Arabidopsis thaliana sequences" Bioinformatics 1999, 15, 887-99. |
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Eukaryotic GeneMark.hmm software can be accessed through this particular web page - this software requires selection
of model parameters that are given here only for 4 species.
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However, further developments of GeneMark.hmm led to algorithms that did not require pre-defined model parameters such as GeneMark-ES
Alexandre Lomsadze et al Gene identification in novel eukaryotic genomes by self-training algorithm Nucleic Acids Research (2005) 33, pp 6494-6506. GeneMark-ES for fungal genomes Ter-Hovhannisyan et al Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training Genome Research (2008) 18, pp 1979-1090. as well as GeneMark-ET that uses RNA-Seq reads to improve self-training Lomsadze et al. "Integration of mapped RNA-Seq reads into automatic training of eukaryotic gene finding algorithm." Nucleic Acids Research, 2014, doi: 10.1093/nar/gku557 and GeneMark-EP+ that uses cross-species proteins to improve self-training Bruna et al. "GeneMark-EP+: eukaryotic gene prediction with self-training in the space of genes and proteins" NAR Genomics and Bioinformatics, Volume 2, Issue 2, 2020 |
| Browse GeneMark.hmm eukaryotic manual |
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