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Updated: Jun 19, 2025

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers
Published on: June 24, 2019
Differentially used codons among essential genes in bacteria identified by machine learning-based analysis
Annushree Kurmi1,2, Piyali Sen1, Madhusmita Dash3
1Department of Computer Science and Engineering, Tezpur University, Napaam, Assam, 784028, India.
Gene essentiality influences codon usage bias (CUB) in bacteria. Machine learning identified distinct codon patterns between essential and non-essential genes across 35 bacterial genomes, revealing conserved patterns.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Codon usage bias (CUB) describes the non-uniform selection of synonymous codons for the same amino acid, varying across bacterial genomes.
- Gene expression levels are known to influence CUB, leading to differences between high- and low-expression genes in bacteria.
- The role of gene essentiality in shaping CUB patterns remains an underexplored area in bacterial genomics.
Purpose of the Study:
- To investigate codon usage patterns in bacteria by incorporating gene essentiality as a key feature.
- To analyze differences in Relative Synonymous Codon Usage (RSCU) between essential and non-essential genes across multiple bacterial species.
- To apply machine learning models to classify genes based on essentiality using CUB features and identify conserved codon usage patterns.
Main Methods:
- Analysis of Relative Synonymous Codon Usage (RSCU) values for essential and non-essential genes.
- Application of machine learning (ML) approaches for gene classification based on codon usage.
- Comparative analysis across 35 bacterial genomes with available gene essentiality data.
Main Results:
- Significant differences in codon usage patterns were observed between essential and non-essential genes in the majority of the studied bacterial genomes.
- Machine learning classifiers achieved high Area Under Curve (AUC) scores (minimum 70.0) for classifying essential genes across 28 organisms.
- Specific codons like CGT (Arg) and GGT (Gly) were highly preferred in essential genes in E. coli, with CGT, ATA, GGT, and GGG showing consistent importance across genomes; TGY (Cys) and CAY (His) codons contributed least.
Conclusions:
- Gene essentiality is a significant factor driving synonymous codon usage differences in bacterial genomes.
- Machine learning effectively distinguishes essential from non-essential genes based on codon usage patterns.
- A common codon usage pattern associated with essential genes exists across diverse bacterial species.
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