GeneGA: the function optimizes gene expression based on both mRNA...

In GeneGA: Design gene based on both mRNA secondary structure and codon usage bias using Genetic algorithm

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.

Author(s)

Zhenpeng Li, Fei Li, Xiaochen Bo and Shengqi Wang

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))

GeneGA documentation built on Nov. 8, 2020, 8:17 p.m.