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Figure 1 | BMC Systems Biology

Figure 1

From: A condition-specific codon optimization approach for improved heterologous gene expression in Saccharomyces cerevisiae

Figure 1

Condition-specific codon optimization utilizing systems level information and codon context. a. A generic workflow to enable a condition-specific codon optimization algorithm in any organism from gene expression data. b. The control codon matrix is compiled from all 6,666 protein-coding genes in S. cerevisiae and serves as a point of comparison for condition-specific matrices. The first amino acid is indicated by the first column, and the second amino acid by the first row. The color indicates probability between 0 (red) and 1 (blue). c. The high expression codon optimization matrix is compiled from the 100 most highly expressed protein-coding genes in S. cerevisiae[35]. d. The stationary phase codon optimization matrix is compiled from the 50 most highly expressed protein-coding genes in S. cerevisiae grown for 3 days, compared to an exponential population [38]. e. The matrix drift from the control matrix (as indicated by Frobenius matrix norm) versus number of genes used to generate the codon usage matrices was plotted for codon usage matrices generated from a random sampling of genes (red squares) and the most highly expressed genes [35] (green triangles). The random data sets were fit with a power regression model. Standard deviations from five independent samples were used to generate error bars.

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