The relation between codon usage, base correlation and gene expression level in Escherichia coli and yeast

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Synonymous codon usage is linked to gene expression levels.
• Understanding this relationship can improve gene expression prediction.

Purpose of the Study:

• To propose a novel method, Self-consistent Information Clustering (SCIC), for classifying and predicting gene expression levels.
• To investigate the relationship between base composition, base correlation, and gene expression in *Escherichia coli* and yeast.

Main Methods:

• Linear regression analysis using modified Codon Adaptation Index (CAI) values.
• Investigation of base composition and correlation patterns.
• Proposal and validation of the Expression-Enhancing-Network Site (EENS) concept.

Main Results:

• Identified specific base correlation modes in *E. coli* and yeast that significantly influence gene expression.
• Demonstrated the existence of EENS through linear equations linking gene expression to base correlations.
• Established a method (SCIC) for gene expression classification and prediction.

Conclusions:

• Synonymous codon usage and base correlations are critical determinants of gene expression.
• The SCIC method provides a new approach for analyzing gene expression patterns.
• The EENS concept offers insights into regulatory mechanisms of gene expression.