| Title: | Design gene based on both mRNA secondary structure and codon usage bias using Genetic algorithm |
|---|---|
| Description: | R based Genetic algorithm for gene expression optimization by considering both mRNA secondary structure and codon usage bias, GeneGA includes the information of highly expressed genes of almost 200 genomes. Meanwhile, Vienna RNA Package is needed to ensure GeneGA to function properly. |
| Authors: | Zhenpeng Li and Haixiu Huang |
| Maintainer: | Zhenpeng Li <[email protected]> |
| License: | GPL version 2 |
| Version: | 1.55.0 |
| Built: | 2024-07-17 11:56:51 UTC |
| Source: | https://github.com/bioc/GeneGA |
R based Genetic algorithm for gene expression optimization considering mRNA secondary structure and codon usage bias
| Package: | GeneGA |
| Type: | Package |
| Version: | 1.1.2 |
| Date: | 2010-11-19 |
| License: | GPL version 2 |
| LazyLoad: | yes |
Zhenpeng Li, Fei Li, Xiaochen Bo and Shengqi Wang
Maintainer: Zhenpeng Li <[email protected]>
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
GeneGA, GeneFoldGA, GeneCodon, GeneGA-class, GeneFoldGA-class,
show-methods, plotGeneGA-methods, wSet
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)) plotGeneGA(GeneGA.result) show(GeneGA.result)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)) plotGeneGA(GeneGA.result) show(GeneGA.result)
The function adjusts the codon usage of gene by replacing less usage codons(the most preference codons) with the most preference codons(less usage codons).
GeneCodon(seq, organism = "ec", max=TRUE, scale=0.5, numcode= 1)GeneCodon(seq, organism = "ec", max=TRUE, scale=0.5, numcode= 1)
seq |
the sequence to optimize |
organism |
the organism that gene due to express, GeneGA contains codon usage bias information of almost 200 genomes |
max |
if max is TRUE, less usage codons will be replaced with the most preference codons, and vice versa. |
scale |
When max is FALSE, scale is used to assign the range of less used synonymous codons of each amino acid to sample. The default value is 0.5, it means each codon will be sampled from its 50% less used synonymous codons. |
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 |
This function returns the optimized sequence as string. If max is True, sequence consisting of most preference used codons will be returned,
Zhenpeng Li, Fei Li, Xiaochen Bo and Shengqi Wang
seqfile=system.file("sequence","EGFP.fasta",package="GeneGA") seq=unlist(getSequence(read.fasta(seqfile),as.string=TRUE)) GeneCodon(seq)seqfile=system.file("sequence","EGFP.fasta",package="GeneGA") seq=unlist(getSequence(read.fasta(seqfile),as.string=TRUE)) GeneCodon(seq)
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.
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)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)
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. |
A GeneFoldGA instance is returned.
Zhenpeng Li, Fei Li, Xiaochen Bo and Shengqi Wang
Liu L.,Kang L.S., Chen Y.P. (1993)Non-numerical parallel algorithms(The second volume)–genetic algorithms,Science Press(In Chinese)
GeneFoldGA-class, GeneGA,
show-methods, plotGeneGA-methods
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))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))
GeneFoldGA class for representing the GeneFoldGA results
seq:Object of class "character". The mRNA sequence to optimize.
iters:Object of class "integer". The number of iteration.
popSize:Object of class "numeric". The population size.
crossoverRate:Object of class "numeric". The crossover rate of each generation. By default 0.2.
mutationChance:Object of class "numeric". Mutation chance of the gene in the unit of codon.
region:Object of class "ANY". The region of sequence to optimize, the other part of sequence outside the region is optimized by only considering codon usage bias.
organism:Object of class "character". The organism that gene due to express,the package contains codon usage bias information of almost 200 genomes.
eval_value:Object of class "numeric". The evaluation function values of all final population.
free_en:Object of class "numeric". The minimux free energy values of all final population.
eval_value_set:Object of class "numeric". The mean of evaluation function values of the population.
eval_value_set02:Object of class "numeric". The maximum of evaluation function values of the population.
population:Object of class "character". The final population undergone the genetic algorithm.
ramp:Object of class "ANY". 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 to the reference for more detailed description.
signature(object = "GeneFoldGA"): Displaying the results of GeneFoldGA, the first three distinctive and optimum sequences can be returned, as well as the corresponding minimum free energys.
signature(object = "GeneFoldGA"): Visualizing the variation of optimized and mean variable values during the progress that genetic algorithm performed. Furthermore, the plot also can be used to check the results whether well converged.
GeneFoldGA, GeneGA,
show-methods, plotGeneGA-methods
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.
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)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)
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. |
A GeneGA instance is returned.
Zhenpeng Li, Fei Li, Xiaochen Bo and Shengqi Wang
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.
GeneGA-class, GeneFoldGA, GeneCodon,
show-methods, plotGeneGA-methods
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))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 class for representing the GeneGA results
All the slots of the GeneFoldGA class, plus the following slots:
CAI_value:Object of class "numeric". If ramp and specified region are not intersecting, it denotes the CAI values of all final population, otherwise, it denotes the CAI values of the intersected region of all final population.
CAI_value_:Object of class "numeric". When ramp and specified region are intersecting, it denotes the CAI values of the specified region that not intersecting with the specified region of all final population.
free_en_set:Object of class "numeric". The mean of minimum free energy of the population.
CAI_value_set:Object of class "numeric". If ramp and specified region are not intersecting, it denotes the mean of CAI values of the population, otherwise, it denotes the mean CAI values of the the intersected region of the population.
CAI_value_set_:Object of class "numeric". When ramp and specified region are intersecting, it denotes the mean CAI values of the specified region that not intersecting with the specified region of the population.
free_en_set02:Object of class "numeric". The maximum of minimum free energy of the population.
CAI_value_set02:Object of class "numeric". If ramp and specified region are not intersecting, it denotes the maximum of CAI values of the population, otherwise, it denotes the maximum CAI values of the the intersected region of the population.
CAI_value_set02_:Object of class "numeric". When ramp and specified region are intersecting, it denotes the maximum CAI values of the specified region that not intersecting with the specified region of the population.
signature(object = "GeneGA"): Displaying the results of GeneGA and GeneFoldGA, the first three distinctive and optimum sequences can be returned, as well as their overall evaluation values, CAI values and minimum free energys.
signature(object = "GeneGA"): Visualizing the variation of optimized and mean overall evaluation values and variable values during the progress that genetic algorithm performed. Furthermore, the plot also can be used to check the results whether well converged.
GeneGA, GeneFoldGA, GeneCodon,
show-methods, plotGeneGA-methods
plotGeneGA implement plotGeneGA methods for GeneGA and GeneFoldGA objects respectively.The functions visualize the variation of optimized and mean overall evaluation values and variable values during the progress that genetic algorithm performed. Furthermore, the plot also can be used to check the results whether well converged.
plotGeneGA method for GeneFoldGA
plotGeneGA method for GeneGA, it also has a parameter "type", which can be assigned with one of the three values, the default value is "default", which show the variation of mean and optimized overall evaluation value along with the generation. while 1, 2 display the variation of mean and optimized overall CAI value and minimum free energy respectively, and 3 display the scatter plot of two variables–CAI and minimum free energy
Zhenpeng Li, Fei Li, Xiaochen Bo and Shengqi Wang
GeneGA-class, GeneFoldGA-class, GeneGA,
GeneFoldGA, show-methods
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)) plotGeneGA(GeneGA.result)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)) plotGeneGA(GeneGA.result)
Codon Adaptation Index (CAI) w tables of almost 200 genomes,the computation is based on the highly expressed genes of these genomes.
data(wSet)data(wSet)
A data frame with 200 observations on the following 64 variables, which denote the 64 codes.
Codons can be accessed by names(wSet) and the genome names can be accessed by row.names(wSet).
The data were combined from two sources: the first part is the first three rows,which contains w tables masked from the caitab dataset of seqinr package,while the other 197 rows were computed based the predicted highly expressed genes of 197 genomes from HEG-DB database.
Puigbo P, Romeu A, Garcia-Vallve S. HEG-DB: a database of predicted highly expressed genes in prokaryotic complete genomes under translational selection. Nucl. Acids Res. 2008, 36:D524-527.
Charif D, Lobry J. 2007. SeqinR 1.0-2. A Contributed Package to the R Project for Statistical Computing Devoted to Biological Sequences Retrieval and Analysis. In: Structural Approaches to Sequence Evolution (Bastolla U, Porto M, Roman E, Vendruscolo M, eds.), Berlin Heidelberg: Springer, p207-232.
data(wSet)data(wSet)