A Web Portal to Calculate Codon Adaptation Index (CAI) with Organism Specific Reference Set of High Expression Genes for Diverse Bacteria Species

Sen, Piyali; Waris, Abdul; Ray, Suvendra Kumar; Satapathy, Siddhartha Sankar

doi:10.1007/978-981-15-0633-8_31

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Similar observation could be found in other Sti values. We compared Sti and Nti values in relation to gene expression represented with codon adaptation index (CAI) values (Sharp and Li 1987; Sen et al 2019). We studied coding sequences with size more than 500 bp.…”

Section: Resultsmentioning

confidence: 99%

Synonymous and non-synonymous transitions/transversions vividly disclose purifying selection inEscherichia colicoding sequences

Beura

Aziz

Sen

et al. 2022

Preprint

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Transition to transversion ratio (ti/tv) is important in the study of molecular evolution, which provides the understanding of mutational bias in an organism. Since, synonymous polymorphism for two-fold degenerate codons depends only on transitions, two-fold degenerate codons and four-fold degenerate codons are different regarding (Ti/Tv) ratios both for synonymous as well as non-synonymous mutations. Therefore, various coding sequences in any organism having difference in degenerate codon composition is likely to affects the ti/tv ratio. In addition to the above, compositional differences among the coding sequences regarding pretermination codon is also likely to influence the ti/tv ratio because purifying selection is stronger on non-synonymous substitutions leading to termination codons. In this study we have improve the method of finding the ratio between transition to transversion, synonymous transition to synonymous transversion and non-synonymous transition to non-synonymous transversion and also developed a computer program to calculate the values. Our analyses in 2516 coding sequences in the E. coli genome suggested a strong correlation between codon degeneracy with synonymous transition to transversion ratio, also we have observed a moderate correlation between pretermination codons and non-synonymous transition to transversion. The percentage change between the conventional and improved method was the most striking part of our observation.

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Section: Resultsmentioning

confidence: 99%

Synonymous and non-synonymous transitions/transversions vividly disclose purifying selection inEscherichia colicoding sequences

Beura

Aziz

Sen

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…As gene expression was not available for these bacteria, we estimated HEG and LEG gene sets for each bacterium, considering codon adaptation index (CAI) values (Sharp & Li, 1987). Considering organism-specific ribosomal protein-coding genes as the reference set, we calculated CAI values of the genes in each bacterium using a web-based tool (Sen et al, 2020). For codon usage analysis, we have considered genes with size greater than 100aa in each bacterium.…”

Section: Methodsmentioning

confidence: 99%

Machine learning approach identifies prominent codons from different degenerate groups influencing gene expression in bacteria

et al. 2022

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Unequal usage of synonymous codons is known as codon usage bias (CUB), which is generally different between the high-expression genes (HEG) and low-expression genes (LEG) in organisms is not yet adequately reported across different bacteria. In this study, a machine learning-based approach was implemented initially to find out codons that are significantly different between the HEG and LEG in Escherichia coli. It identified Cys codons such as UGU and UGC, Lys codons such as AAA and AAG that were least influenced by gene expression. Codons such as UCU (Ser), CUG (Leu), GGG (Gly), CGG (Arg) etc. were identified to be influenced maximum by the gene expression. The study was extended to analyze codon usage in 683 other bacterial species. Cys (UGU/UGC) and Ser (AGU/AGC) codons were identified being the least different between the two groups of genes across these bacterial species. Codons such as CGA, CUG, GGG, GCC, ACC, AUA, and AUC were identified to be influenced by the gene expression across majority of these species. This study supports the role of CUB on gene expression across bacteria and demonstrates a commonality among bacteria regarding behavior of certain codons with regard to gene expression.

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Salmonella pathogenesis-based In-silico design and immunoinformatic analysis of multi-epitope vaccine constructs in broiler veterinary medicine

Mora-Ochoa,

Ramirez-Cando

2024

The Veterinary Journal

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A Web Portal to Calculate Codon Adaptation Index (CAI) with Organism Specific Reference Set of High Expression Genes for Diverse Bacteria Species

Cited by 3 publications

References 26 publications

Synonymous and non-synonymous transitions/transversions vividly disclose purifying selection inEscherichia colicoding sequences

Synonymous and non-synonymous transitions/transversions vividly disclose purifying selection inEscherichia colicoding sequences

Machine learning approach identifies prominent codons from different degenerate groups influencing gene expression in bacteria

Salmonella pathogenesis-based In-silico design and immunoinformatic analysis of multi-epitope vaccine constructs in broiler veterinary medicine

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