Codon Usage Domains over Bacterial Chromosomes

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Codon Usage Domains over Bacterial Chromosomes

The geography of codon bias distributions over prokaryotic genomes and its impact upon chromosomal organization are analyzed. To this aim, we introduce a clustering method based on information theory, specifically designed to cluster genes according to their codon usage and apply it to the coding sequences of Escherichia coli and Bacillus subtilis. One of the clusters identified in each of the ...

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Addendum to the paper “ Codon usage domains over bacterial chromosomes ”

An issue left unexplained in the paper [1] is the striking quantitative difference between E. coli and B. subtilis. This is clearly visible in Fig. 6 of [1], where it is shown the probability that two genes at distance l belong to the same cluster of codon usage. Clusters are characterized by a similar codon bias and were identified using a novel information-based clustering method. While both ...

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An entropy-based technique for classifying bacterial chromosomes according to synonymous codon usage.

We present a framework based on information theoretic concepts and the Dirichlet distribution for classifying chromosomes based on the degree to which they use synonymous codons uniformly or preferentially, that is, whether or not codons that code for an amino acid appear with the same relative frequency. At its core is a measure of codon usage bias we call the Kullback-Leibler codon informatio...

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Selection, mutations and codon usage in a bacterial model.

We present a statistical model of bacterial evolution based on the coupling between codon usage and tRNA abundance. Such a model interprets this aspect of the evolutionary process as a balance between the codon homogenization effect due to mutation process and the improvement of the translation phase due to natural selection. We develop a thermodynamical description of the asymptotic state of t...

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More Codon Usage Bias

χ measure. In statistics, the χ statstic computes how different the distribution of values is from a uniform distribution. Let d = c1 . . . cN be a DNA string in nucleotide alphabet, and let L be the total number of codons in d that are not Methinine or Tryptophan (the number does include the stop codons though). Let a be a k-degenerate amino acid, and let La be the total number of codons codin...

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ژورنال

عنوان ژورنال: PLoS Computational Biology

سال: 2006

ISSN: 1553-7358

DOI: 10.1371/journal.pcbi.0020037