SSCU: an R/Bioconductor package for analyzing selective profile in synonymous codon usage

TL;DR

The paper introduces SSCU, an R/Bioconductor package that automates analysis of selective profiles in synonymous codon usage, integrating multiple statistical tests and methods for identifying optimal codons and measuring selection.

Contribution

This work provides the first comprehensive R package that combines various statistical approaches for analyzing codon usage selection, facilitating research in molecular evolution.

Findings

The package includes tools for identifying optimal codons using comparative and correlative methods.

It implements key statistics like S index and Akashi's test for detecting codon selection.

Over 2000 downloads and top 50% ranking at Bioconductor demonstrate its utility.

Abstract

Background Synonymous codon choice is mainly affected by mutation and selection. For the majority of genes within a genome, mutational pressure is the major driving force, but selective strength can be strong and dominant for specific set of genes or codons. More specifically, the selective strength on translational efficiency and accuracy increases with the gene's expression level. Many statistical approaches have been developed to evaluate and quantify the selective profile in codon usage, including S index and Akashi's test, but no program or pipeline has been developed that includes these tests and automates the calculation. Results In this study, we release an R package SSCU (selective strength for codon usage, v2.4.0), which includes tools for codon usage analyses. The package identifies optimal codons using two approaches (comparative and correlative methods), implements well-established statistics for detecting codon selection, such as S index, Akashi's test, and estimates standard genomic statistics, such as genomic GC3, RSCU and Nc. Conclusions The package is useful for researchers working on the codon usage analysis, and thus has general interest to the biological research community. The package is deposited and curated at the Bioconductor site, and has currently been downloaded for more than 2000 times and ranked as top 50% packages.