INCA: synonymous codon usage analysis and clustering by means of self-organizing map

Area of Science:

• Genomics
• Bioinformatics

Background:

• Synonymous codon usage analysis is crucial for understanding gene expression and genome evolution.
• Existing tools may lack comprehensive features for interactive visualization and advanced analysis.

Purpose of the Study:

• To introduce INteractive Codon usage Analysis (INCA), a software tool for analyzing synonymous codon usage.
• To provide researchers with advanced features for visualizing codon usage trends and performing gene clustering.

Main Methods:

• INCA computes codon frequencies and various usage indices, including codon bias, effective number of codons (Nc), and Codon Adaptation Index (CAI).
• The software incorporates an unsupervised neural network algorithm, the self-organizing map (SOM), for gene clustering based on codon preferences.
• Interactive graphical displays allow for visual detection of codon usage patterns.

Main Results:

• INCA facilitates the computation and visualization of multiple codon usage metrics.
• The self-organizing map algorithm enables effective gene clustering according to codon usage bias.
• Visual analysis aids in identifying trends and patterns in synonymous codon usage across genomes.

Conclusions:

• INCA is a valuable, free software tool for academic researchers studying synonymous codon usage.
• Its interactive features and advanced algorithms enhance the ability to analyze and interpret codon usage data.
• The software supports visual detection of trends and facilitates gene clustering for deeper genomic insights.