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

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
   Input sequence and Select species
Enter sequence in FASTA format (with only one sequence record )
or, upload file:

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

   Coding potential graph
   (not for multi FASTA)


Compress files
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