It is simple codon optimiser by using codon bias and codon usage
Use requiremnts.txt file for required packages To create codon bias dict:
frequencer.py [-h] [-ref REFERENCE] [-out OUTPUT]
to create codon bias dictionary from reference nucleotide itself
optional arguments: -h, --help show this help message and exit
-ref REFERENCE, --reference REFERENCE reference file in nucleotide fasta
-out OUTPUT, --output OUTPUT output file in dict.py
Output of this should be used in case for different expression system in initiator.py and whenever codon_freq is used.
Usage: main.py [-h] [-in INPUTNUCL] [-ip INPUTPROT] [-on OUTPUTNUCL] [-op OUTPUTPROT] [-ref REFERENCE] [-repeat REPEAT]
to optimise codon either from protein or nucleotide itself
optional arguments: -h, --help show this help message and exit
-in INPUTNUCL, --inputnucl INPUTNUCL input file in nucleotide fasta
-ip INPUTPROT, --inputprot INPUTPROT input file in protein fasta
-on OUTPUTNUCL, --outputnucl OUTPUTNUCL output file in nucleotide fasta
-op OUTPUTPROT, --outputprot OUTPUTPROT output file in protein fasta
-ref REFERENCE, --reference REFERENCE reference file in nucleotide fasta
-repeat REPEAT, --repeat REPEAT to access repeat
example: python main.py -in references.fasta -on ref_70.fasta -ref references.fasta -repeat True
Reference file is from http://genomes.urv.cat/HEG-DB/ which is highly expressed gene repository