GeneGA (version 1.22.0)

GeneFoldGA: the function optimizes gene merely considering mRNA secondary structure.

Description

Optimizing gene by only considering mRNA secondary structure using genetic algorithm. The function has the default evaluation function with minimum free energy as variable. The optimum is the gene for which the minimum free energy is maximum. Results can be visualized with plotGeneGA and displayed with show.

Usage

GeneFoldGA(sequence = NULL, popSize = 50, iters = 100, crossoverRate = 0.2,  mutationChance = 0.05, region = NULL, showGeneration = TRUE,  frontSeq = NULL, organism="ec", ramp = FALSE,numcode=1)

Arguments

sequence

the mRNA sequence to optimize

popSize

the population size

iters

the number of iteration

crossoverRate

the crossover rate of each generation. By default 0.2

mutationChance

mutation chance of the gene in the unit of codons

region

the region of sequence to optimize, the other part of sequence outside the region is optimized only considering codon usage bias as well.

showGeneration

show the generation the genetic algorithm progressing, the default value is TRUE

frontSeq

frontSeq denotes the regulatory segment before the start codon. If frontSeq is specified, frontSeq will be considered when compute the minimum free energy. The default value is NULL.

organism

the organism that gene due to express, the package contains codon usage bias information of almost 200 genomes.

ramp

the organism that gene due to express, the package contains codon usage bias information of almost 200 genomes.

numcode

The ncbi genetic code number for translation. By default the standard genetic code is used. Referring to the help page of "translate" function in seqinr package for details.

Value

A GeneFoldGA instance is returned.

References

Liu L.,Kang L.S., Chen Y.P. (1993)Non-numerical parallel algorithms(The second volume)--genetic algorithms,Science Press(In Chinese)

See Also

GeneFoldGA-class, GeneGA, show-methods, plotGeneGA-methods

Examples

seqfile=system.file("sequence","EGFP.fasta",package="GeneGA")
seq=unlist(getSequence(read.fasta(seqfile),as.string=TRUE))
GeneGA.result=GeneFoldGA(sequence=seq,popSize=40,iters=100,crossoverRate=0.3,
         mutationChance=0.05,region=c(1,42))