**3.5. Count normalization**

There are several methods such as quantile-based normalization, GC-content-based normali‐ zation, Poisson model with variable rates for different positions, available to normalize and correct the biasness in the count data for the improved detection of differentially expressed genes [91, 127, 128]. The increasing number of normalization methods requires a state-of-theart technique for comparing these methods. In the absence of such technique, there is no consensus on the best method for normalization. For example, Zyprych-Walczak et al. [99] found that TMM method worked poorly for them while Dillies et al. [98] found TMM and median of ratio methods to be the best as compared to other methods. The transcript length is another source of bias and leads to detection of more differential expression in longer tran‐ scripts compared to shorter transcripts [88].
