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cubar

Comprehensive Codon Usage Bias Analysis in R

Table of Contents

• Overview
• Features
  • 🧬 Codon-Level Analysis
  • 📊 Gene-Level Metrics
  • 🛠️ Utilities & Tools
• Why Choose cubar?
• Installation
  • Stable Release(Recommended)
  • Development Version
  • Dependencies
• Documentation &Tutorials
  • 🎯 Getting Started
  • 📚 Advanced Topics
• Example Workflow
• 🆘 Getting Help
• Related Packages
• License
• Acknowledgments

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 acomprehensive R package for analyzing codon usage bias in codingsequences. It provides a unified framework for calculating establishedcodon usage metrics, conducting sliding-window analyses or differentialusage analyses, and optimizing sequences for heterologousexpression.

Features

🧬 Codon-Level Analysis

• RSCU calculation: Relative synonymous codon usageanalysis
• Amino acid usage: Frequency of each amino acid insequences
• Codon weights: Calculate weights based on geneexpression, tRNA availability, and mRNA stability
• Optimal codon inference: Machine learning-basedidentification of optimal codons
• Codon-anticodon visualization: Visualization ofcodon-tRNA pairing relationships

📊 Gene-Level Metrics

• Codon frequency tabulation: Count codon occurrencesacross sequences
• CAI (Codon Adaptation Index): Measure similarity tohighly expressed genes
• ENC (Effective Number of Codons): Assess codonusage bias strength
• Fop (Fraction of Optimal codons): Calculateproportion of optimal codons
• tAI (tRNA Adaptation Index): Match codon usage totRNA availability
• CSCg (Codon Stabilization Coefficients): QuantifymRNA stability effects
• Dp (Deviation from Proportionality): Analyzevirus-host codon usage relationships
• GC content metrics: Overall GC, GC3s (3rd codonpositions), GC4d (4-fold degenerate sites)

🛠️ Utilities & Tools

• Sliding window analysis: Positional codon usagepatterns within genes
• Sequence optimization: Redesign sequences foroptimal expression
• Differential codon usage: Statistical comparisonbetween sequence sets
• Quality control: Comprehensive CDS validation andpreprocessing

Why Choose cubar?

• 🚀 High Performance: Process large datasets(>100,000 sequences) efficiently using optimizedBiostrings anddata.table backends
• 🧬 Flexible Genetic Codes: Support for all NCBIgenetic codes plus custom genetic code tables
• 🔗 R Ecosystem Integration: Seamlessly integratewith other bioinformatics and data analysis packages
• 📚 Comprehensive Documentation: Extensivetutorials, examples, and theoretical background
• 🔬 Research Ready: Implements established metricswith proper citations and validation

Installation

Stable Release (Recommended)

Install the latest stable version from CRAN:

install.packages("cubar")

Development Version

Install the latest development version from GitHub:

# Install devtools if not already installedif (!requireNamespace("devtools",quietly =TRUE)) {install.packages("devtools")}# Install cubar from GitHubdevtools::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 rangeoperations
-data.table (≥ 1.14.0) - High-performance datamanipulation -ggplot2 (≥ 3.3.5) - Data visualization -rlang (≥ 0.4.11) - Language tools

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

Documentation & Tutorials

📖Complete documentation is available within R(?function_name) and on ourpackagewebsite.

🎯 Getting Started

• Introductionto cubar - Basic usage and core functionality
• Non-standardGenetic Codes - Working with alternative genetic codes
• CodonOptimization - Sequence optimization strategies

📚 Advanced Topics

• MathematicalFoundations - Detailed theory behind the metrics
• FunctionReference - Complete function documentation

Example Workflow

Here’s a typical analysis workflow demonstrating keyfunctionality:

library(cubar)library(ggplot2)# 1. Load and quality-check sequencesdata(yeast_cds)clean_cds<-check_cds(yeast_cds)# 2. Calculate codon frequenciescodon_freq<-count_codons(clean_cds)# 3. Calculate multiple metricsenc<-get_enc(codon_freq)# Effective number of codonsgc3s<-get_gc3s(codon_freq)# GC content at 3rd positions# 4. Analyze highly expressed genesdata(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 resultsdf<-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

• 📋 GitHub Issues:Report bugs, requestfeatures, or ask questions
• 📖 Documentation: Check function help(?function_name) andonline docs

Related Packages

For complementary analysis, consider these R packages:

• Biostrings- Sequence input/output and manipulation
• Peptides -Peptide and protein property calculations

License

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

Acknowledgments

• GitHub Copilot was used to suggest code snippetsduring development
• GitHubEducation for providing free access to developmenttools
• The R and Bioconductor communities for excellent foundationalpackages
• Contributors and users who have provided feedback andimprovements