GeneMark.hmm eukaryotic
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.
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.

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
   Browse GeneMark.hmm eukaryotic manual
   Input sequence and Select species
Enter sequence in FASTA format (with only one sequence record )
or, upload file:

Select species
   Action
 
   Options
Output format
for gene prediction
Output options Optional: results
by E-mail
LST
GFF
Protein sequence
Gene nucleotide sequence

   Coding potential graph
   (not for multi FASTA)
PDF
PostScript
E-mail

Subject

Compress files
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