The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

cubar

Comprehensive Codon Usage Bias Analysis in R

CRAN status DOI Lifecycle: stable

Table of Contents

Overview

Codon usage bias refers to the non-uniform usage of synonymous codons (codons that encode the same amino acid) across different organisms, genes, and functional categories. cubar is a comprehensive R package for analyzing codon usage bias in coding sequences. It provides a unified framework for calculating established codon usage metrics, conducting sliding-window analyses or differential usage analyses, and optimizing sequences for heterologous expression.

Features

🧬 Codon-Level Analysis

📊 Gene-Level Metrics

🛠️ Utilities & Tools

Why Choose cubar?

Installation

Install the latest stable version from CRAN:

install.packages("cubar")

Development Version

Install the latest development version from GitHub:

# Install devtools if not already installed
if (!requireNamespace("devtools", quietly = TRUE)) {
    install.packages("devtools")
}

# Install cubar from GitHub
devtools::install_github("mt1022/cubar", dependencies = TRUE)

Dependencies

System Requirements: - R (≥ 4.1.0)

Required Packages: - Biostrings (≥ 2.60.0) - Bioconductor package for sequence manipulation - IRanges (≥ 2.34.0) - Bioconductor infrastructure for range operations
- data.table (≥ 1.14.0) - High-performance data manipulation - ggplot2 (≥ 3.3.5) - Data visualization - rlang (≥ 0.4.11) - Language tools

Note: Bioconductor packages will be installed automatically, but you may need to update your R installation if you encounter compatibility issues.

Documentation & Tutorials

📖 Complete documentation is available within R (?function_name) and on our package website.

🎯 Getting Started

📚 Advanced Topics

Example Workflow

Here’s a typical analysis workflow demonstrating key functionality:

library(cubar)
library(ggplot2)

# 1. Load and quality-check sequences
data(yeast_cds)
clean_cds <- check_cds(yeast_cds)

# 2. Calculate codon frequencies
codon_freq <- count_codons(clean_cds)

# 3. Calculate multiple metrics
enc <- get_enc(codon_freq)           # Effective number of codons
gc3s <- get_gc3s(codon_freq)         # GC content at 3rd positions

# 4. Analyze highly expressed genes
data(yeast_exp)
yeast_exp <- yeast_exp[yeast_exp$gene_id %in% rownames(codon_freq), ]
high_expr <- head(yeast_exp[order(-yeast_exp$fpkm), ], 500)
rscu_high <- est_rscu(codon_freq[high_expr$gene_id, ])
cai <- get_cai(codon_freq, rscu_high)

# 5. Visualize results
df <- data.frame(ENC = enc, CAI = cai, GC3s = gc3s)
ggplot(df, aes(color = GC3s, x = ENC, y = CAI)) + 
  geom_point(alpha = 0.6) + 
  scale_color_viridis_c() +
  labs(title = "Codon Usage Bias Relationships",
       x = "Effective Number of Codons", y = "Codon Adaptation Index")

🆘 Getting Help

For complementary analysis, consider these R packages:

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

📚 Documentation • 🐛 Report Bug • 💡 Request Feature

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.