This is a hacky way to run/install genemark with docker. Why? Well I develop on Mac OSX and GeneMark is no longer released for OSX, so this little repo will allow you to build a working GeneMark docker image and then "spoof" the gmes_petap.pl and gmhmme3 scripts so that you can put this in your PATH and it can be picked up by some other software, ie funannotate.
You will need to download 64-bit GeneMark-ETP along with the license key to the same directory that you clone this repo from http://topaz.gatech.edu/GeneMark/license_download.cgi. [GeneMark-ES/ET/EP+ ver 4.71_lic and LINUX 64 kernel 3.10 - 5 version]. Then from that location you should just be able run:
docker build -t genemark .
This command will copy genemark and install inside the docker image called genemark. Then using the bash wrapper scripts of gmes_petap.pl and gmhmme3 will call the appropriate genemark execulatble via docker, ie:
$ gmes_petap.pl
# -------------------
Usage: /opt/gmes_linux_64_4/gmes_petap.pl [options] --sequence [filename]
GeneMark-ES Suite version 4.71_lic
Suite includes GeneMark.hmm, GeneMark-ES, GeneMark-ET and GeneMark-EP algorithms.
Input sequence/s should be in FASTA format.
Select one of the gene prediction algorithms:
To run GeneMark-ES self-training algorithm
--ES
To run GeneMark-ET with hints from transcriptome splice alignments
--ET [filename]; file with intron coordinates from RNA-Seq read splice alignment in GFF format
--et_score [number]; default 10; minimum score of intron in initiation of the ET algorithm
To run GeneMark-EP with hints from protein splice alignments
--EP
--dbep [filename]; file with protein database in FASTA format
--ep_score [number,number]; default 4,0.25; minimum score of intron in initiation of the EP algorithm
or
--EP [filename]; file with intron coordinates from protein splice alignment in GFF format
To run GeneMark.hmm predictions using previously derived model
--predict_with [filename]; file with species specific gene prediction parameters
To run ES, ET or EP with branch point model. This option is most useful for fungal genomes
--fungus
To run hmm, ES, ET or EP in PLUS mode (prediction with hints)
--evidence [filename]; file with hints in GFF format
To run algorithms with alternative genetic codes
--gcode [number]; default 1; supported 1 and 6/26
Output formatting options:
--format [label]; default GTF; output gene prediction in GTF of GFF3 format
--work_dir [folder name]; default current working directory .;
Masking option
--soft_mask [number] or [auto]; default auto; to indicate that lowercase letters stand for repeats;
algorithm hard masks only lowercase repeats longer than specified length
In 'auto' mode hard masking threshold is selected by algorithm based on the size of the input genome
--mask_penalty [number] or [auto]; default 0.03;
Run options
--cores [number]; default 1; to run program with multiple threads
--pbs to run on cluster with PBS support
--v verbose
Optional sequence pre-processing parameters
--max_contig [number]; default 5000000; will split input genomic sequence into contigs shorter then max_contig
--min_contig [number]; default 50000 (10000 fungi);
will ignore contigs shorter than min_contig in training
--max_gap [number]; default 5000; will split sequence at gaps longer than max_gap
Letters 'n' and 'N' are interpreted as standing within gaps
--max_mask [number]; default 5000; will split sequence at repeats longer then max_mask
Letters 'x' and 'X' are interpreted as results of hard masking of repeats
Optional parameters
--max_intron [number]; default 10000 (3000 fungi); maximum length of intron
--max_intergenic [number]; default 50000 (10000 fungi); maximum length of intergenic regions
--min_contig_in_predict [number]; default 500; minimum allowed length of contig in prediction step
--min_gene_in_predict [number]; default 300 (120 fungi); minimum allowed gene length in prediction step
--gc_donor [value]; default 0.001; transition probability to GC donor in the range 0..1;
'off' switches GC donor model OFF
--gc3 [number]; GC3 cutoff in training for grasses
Developer options
--training to run only training step of algorithm; applicable to ES, ET or EP
--prediction to run only prediction step of algorithms using species parameters from previously executed training; applicable to ES, ET or EP
--usr_cfg [filename]; use custom configuration from this file
--ini_mod [filename]; use this file with parameters for algorithm initiation
--key_bin
--debug
# -------------------