GeneGA (version 1.22.0)

GeneGA: the function optimizes gene expression based on both mRNA secondary structure and codon usage bias

Description

Optimizing gene expression considering both mRNA secondary structure and codon usage bias using genetic algorithm. The function has the default evaluation function with variables CAI and minimum free energy, the sum of squares of ranks of which is used as an evaluation value to pilot the evoluation. The optimum is the gene for which the evaluation value is maximum. Results can be visualized with plotGeneGA and displayed with show.

Usage

GeneGA(sequence = NULL, popSize = 50, iters = 150, crossoverRate = 0.2, mutationChance = 0.05, region = NULL, organism = "ec",  showGeneration = TRUE, frontSeq = NULL, 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 codon

region

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

organism

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

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.

ramp

ramp specifies the region with low translation efficiency.Generally, the first 90 to 150 bases are the ramp region of gene, which are deemed to be an evolutionarily conserved mechanism for controlling the efficiency of protein translation.Referring the reference for more detailed description.

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 GeneGA 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).

Tuller T, Carmi A, Vestsigian K, et al. An Evolutionarily Conserved Mechanism for Controlling the Efficiency of Protein Translation. Cell 2010, 141:344-354.

See Also

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

Examples

seqfile=system.file("sequence","EGFP.fasta",package="GeneGA")
seq=unlist(getSequence(read.fasta(seqfile),as.string=TRUE))
GeneGA.result=GeneGA(sequence=seq,popSize=40,iters=100,crossoverRate=0.3,
         mutationChance=0.05,region=c(1,60))