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Codon Deviation Coefficient: a novel measure for estimating codon usage bias and its statistical significance.
Zhang Zhang1, Jun Li, Peng Cui
1Computational Bioscience Research Center (CBRC), King Abdullah Universitof Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
BMC Bioinformatics
|March 23, 2012
Summary
A new method, the Codon Deviation Coefficient (CDC), accurately measures codon usage bias (CUB) and its statistical significance. CDC accounts for nucleotide composition, outperforming existing measures for gene function and genome evolution studies.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Evolution
Background:
- Codon usage bias (CUB) is influenced by genetic mutation, selection for translation efficiency, gene expression levels, and protein function.
- Accurate measurement of CUB is crucial for understanding gene function and genome evolution.
- Existing CUB measures inadequately address background nucleotide composition and lack statistical significance evaluation.
Purpose of the Study:
- To introduce a novel, informative measure for codon usage bias (CUB) and its statistical significance.
- To develop a method that does not require prior knowledge for CUB assessment.
Main Methods:
- Proposed the Codon Deviation Coefficient (CDC), a new CUB measure.
- CDC accounts for background nucleotide compositions specific to codon positions.
- Utilized bootstrapping to statistically assess the significance of CUB in sequences.
Main Results:
- CDC provides an informative measurement of CUB and its statistical significance.
- Evaluated CDC's performance on simulated and empirical data.
- Demonstrated that CDC surpasses existing measures in CUB estimation and significance assessment.
Conclusions:
- CDC offers a highly informative quantification of CUB and its statistical significance.
- Validated through simulated and empirical data, CDC is effective across diverse gene and genome sequences.
- Useful for comparative analysis of codon usage patterns and magnitudes.
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