sscu-package {sscu} | R Documentation

## Strength of Selected Codon Usage

### Description

The package can calculate the indexes for selective stength in codon usage in bacteria species. (1) The package can calculate the strength of selected codon usage bias (sscu, also named as s_index) based on Paul Sharp's method. The method take into account of background mutation rate, and focus only on four pairs of codons with universal translational advantages in all bacterial species. Thus the sscu index is comparable among different species. (2) Translational accuracy selection can be inferred from Akashi's test. The test tabulating all codons into four categories with the feature as conserved/variable amino acids and optimal/non-optimal codons. (3) Optimal codon lists (selected codons) can be calculated by either op_highly function (by using the highly expressed genes compared with all genes to identify optimal codons biased used in the highly expressed genes), or op_corre_CodonW/op_corre_NCprime function (by correlative method developed by Hershberg & Petrov). Users will have a list of optimal codons for further analysis, such as input to the Akashi's test. (4) The detailed codon usage information, such as RSCU value, number of optimal codons in the highly/all gene set, as well as the genomic gc3 value, can be calculate by the optimal_codon_statistics and genomic_gc3 function. (5) Furthermore, we added one test function proportion_index in the package. The function focus on the proportion of optimal codon against its corresponding non-optimal codons for the the four and six codon boxes.

### Details

The DESCRIPTION file:

Package: | sscu
Type: | Package
Title: | Strength of Selected Codon Usage
Version: | 2.24.0
Date: | 2016-12-1
Author: | Yu Sun
Maintainer: | Yu Sun <sunyu1357@gmail.com>
Description: | The package calculates the indexes for selective stength in codon usage in bacteria species. (1) The package can calculate the strength of selected codon usage bias (sscu, also named as s_index) based on Paul Sharp's method. The method take into account of background mutation rate, and focus only on four pairs of codons with universal translational advantages in all bacterial species. Thus the sscu index is comparable among different species. (2) The package can detect the strength of translational accuracy selection by Akashi's test. The test tabulating all codons into four categories with the feature as conserved/variable amino acids and optimal/non-optimal codons. (3) Optimal codon lists (selected codons) can be calculated by either op_highly function (by using the highly expressed genes compared with all genes to identify optimal codons), or op_corre_CodonW/op_corre_NCprime function (by correlative method developed by Hershberg & Petrov). Users will have a list of optimal codons for further analysis, such as input to the Akashi's test. (4) The detailed codon usage information, such as RSCU value, number of optimal codons in the highly/all gene set, as well as the genomic gc3 value, can be calculate by the optimal_codon_statistics and genomic_gc3 function. (5) Furthermore, we added one test function low_frequency_op in the package. The function try to find the low frequency optimal codons, among all the optimal codons identified by the op_highly function.
Depends: | R (>= 3.3)
Imports: | Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1)
Suggests: | knitr, rmarkdown
VignetteBuilder: | knitr
LazyLoad: | yes
License: | GPL (>= 2)
biocViews: | Genetics, GeneExpression, WholeGenome
git_url: | https://git.bioconductor.org/packages/sscu
git_branch: | RELEASE_3_14
git_last_commit: | 16126f9
git_last_commit_date: | 2021-10-26
Date/Publication: | 2021-10-26

Index of help topics:

    akashi_test             akashi test for codon usage
    genomic_gc3             genomic gc3 for an multifasta genomic file
    low_frequency_op        the function identify low frequency optimal
                            codons
    op_corre_CodonW         Identify optimal codons by using the
                            correlative method from Hershberg & Petrov, the
                            input file is from CodonW
    op_corre_NCprime        Identify optimal codons by using the
                            correlative method from Hershberg & Petrov, the
                            input file is from NCprime
    op_highly               Identify optimal codons by using the highly
                            expressed genes method
    op_highly_stats         statistics for the optimal codons
    s_index                 S index (Strength of Selected Codon Usage)
    sscu-package            Strength of Selected Codon Usage

### Author(s)

Yu Sun

Maintainer: Yu Sun <sunyu1357@gmail.com>

### References

Sharp PM, Bailes E, Grocock RJ, Peden JF, Sockett RE (2005). Variation in the strength of selected codon usage bias among bacteria. Nucleic Acids Research. Sharp PM, Emery LR, Zeng K. 2010. Forces that influence the evolution of codon bias. Philos Trans R Soc Lond B Sci. 365:1203-1212. Hershberg R, Petrov DA. 2009. General rules for optimal codon choice. Plos Genet. 5:e1001115. Akashi H. Synonymous codon usage in Drosophila melanogaster: natural selection and translational accuracy. Genetics 1994 Mar;136(3):927-35. http://drummond.openwetware.org/Akashi's_Test.html Novembre JA. 2002. Accounting for background necleotide composition when measuring codon usage bias. Mol Biol Evol. 19: 1390-1394. https://github.com/jnovembre/ENCprime http://codonw.sourceforge.net/

[Package sscu version 2.24.0 Index]