Codon usage module

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Assumes the standard genetic code (opens new window), which is true for the majority of bacterial genomes/genes.

How it works

To summarize the genome codon usage bias MAGpurify2 uses the the Relative Synonymous Codon Usage (RSCU)

and the codon usage adaptation of each gene relative to the rest of the genome is measured using the Codon Adaptation Index (CAI)

described by Sharp PM and Li WH ^[1].

$\mathit{RSCU}_{i,j} = \frac{x_{i,j}}{\frac{1}{n_i}\sum_{j=1}^{n_i}x_{i,j}\:}$

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Codon usage bias in secondary chromosomes

Genes on secondary chromosomes exhibited significantly weaker codon usage bias than those on primary chromosomes. (opens new window)

$w_{i,j} = \frac{\mathit{RSCU}_{i,j}}{\max_{1\le j\le n}\mathit{RSCU}_{i,j}}$

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$\mathit{CAI} = \left(\prod \:_{k=1}^Lw_k\right)^{\frac{1}{L}}$

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The ΔCAI values of genes within a same contig are averaged to obtain contig-level ΔCAI. Next, a kernel density estimation (KDE) is computed from the data in order to

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Alternative codon usage bias encodings

The RSCU is not the only method to summarize the codon usage bias of an organism. Hughes AL and Langley KJ (opens new window) proposed an alternative representation that encodes codon usage bias into five variables and this approach was succesfully applied to metagenomic binning by Yu G et al (opens new window).

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1. Sharp, Paul M., and Wen-Hsiung Li. "The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications." (opens new window) Nucleic Acids Research 15.3 (1987): 1281-1295. ↩︎