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Nucleosome occupancy in other regions of the promoter and within the transcribed region do not differ between genes with different epistatic fitness effects.

We observed variations in the 3′ codon context among the groups of genes with different expression levels, and we detected major differences between genes with different expression levels.

Therefore, differences in energy-cost between genes with different expression levels may be most pronounced in extreme values, just as the cases of gene structure.

We have shown above that our composition profiles are caused by CpG islands, so we can expect to see differences in base composition between genes with different expression patterns.

To identify differences in sequence composition between genes with different expression levels, all 1024 possible 5-mers were divided into five groups based on their genome-wide frequency in intergenic or coding regions.

Next, we compare the above effect between genes with different expression level and codon usage bias, respectively.

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Finally, we select the maximum correlation between two genes with different time delays or time leads as their correlation and rank them in Figure 2 for all regulatory genes.

Read-through fusion transcripts between adjacent genes with different biochemical functions represent a new type of recurrent molecular defect in breast cancer that warrant further investigation as potential biomarkers and therapeutic targets.

CV was used to compare the degree of variation between the genes with different means of expression [ 57], and the Maximum Fold Change (MFC, ratio of the maximum and minimum values) was used to reflect the minor variations of those candidates within all biological samples [ 58].

In particular, we have analyzed base composition, periodicity and information content in sets of aligned promoters clustered according to functional information in order to obtain an insight on the main structural differences between promoters regulating genes with different functions.

Our objective was to compare the performance of the 1.5K panel to the more recent 24K panel using the DASL platform to determine whether genes behave similarly between gene panels with different densities.

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