%0 Journal Article %A Liu, Sophia S. %A J. Hockenberry, Adam %A Jewett, Michael C. %A Amaral, Luís A. N. %D 2018 %T Supplementary Figure 1; Supplementary Figure 2; Supplementary Figure 3; Supplementary Table 1; SI text from A novel framework for evaluating the performance of codon usage bias metrics %U https://rs.figshare.com/articles/journal_contribution/Supplementary_Figure_1_Supplementary_Figure_2_Supplementary_Figure_3_Supplementary_Table_1_SI_text_from_A_novel_framework_for_evaluating_the_performance_of_codon_usage_bias_metrics/5822619 %R 10.6084/m9.figshare.5822619.v1 %2 https://ndownloader.figshare.com/files/10302195 %K codon usage bias %K theoretical benchmarking %K translational regulation %X Spearman's ρ between the calculated CUB of 3740 genes in the E. coli genome using various metrics. All correlations are statistically significant with p < 10−10; Performance of individual pair-wise differentiation task for six different CUB metrics for one bootstrapped sample using E. coli -like genome. Each block in the heatmap represents the AUC when using that metric to differentiate sequences created using different number of codons.; Performance of individual pair-wise differentiation task for six different CUB metrics for one bootstrapped sample under the simplest case. Each block in the heatmap represents the AUC when using that metric to differentiate sequences created using different number of codons.; P-value of the Wilcoxon signed-rank test for gene expression correlations between different metrics. Bold face values indicate when the test statistic is positive (i.e. when the metric listed at the left is higher than the metric listed at the top).; Implementation of previous CUB metrics %I The Royal Society