The emergence of high throughput sequencing (HTS) platforms increased the amount of data making feasible to obtaining complete genomes. Despite the advantages and the throughput produced by these platforms, the high or low genomic coverage in the
regions of the genome can be related to GC content. This GC bias may affect genomic analyzes and the genomic/transcriptomic analysis based on de novo and reference approach. In addition, the ways to evaluate the GC bias should be fit to data with different profiles of the GC vs coverage relationship, such as linear and quadratic. Thus, this work proposes the use of Pearson's Correlation Coefficient (r) to analyze
the correlation between GC content and coverage, allowing to identify the strength of linear correlation and detect nonlinear associations, beyond identify a relationship between GC bias and sequencing platforms. The positive and negative signs of r also allow us to
infer directly and inversely proportional relationships, respectively. To evaluate the bias, we used the data of Corynebacterium pseudotuberculosis obtained from different
sequencing technologies to identify if the CG bias is related to used platforms.
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