Next, as a cleansing against different sequencing depths from different runs and other factors that can hinder the fragment length distribution sanity, Bayesian normalization via the Dirichlet-multinomial model should be done. Per every sample, based on the fragment lengths observed in that sample, a multinomial maximum likelihood estimation-based fragment length distribution is generated.  Two intervals of 250 base pair length are used, located between -1000th base pair and -750th base pair, and between 750th base pair and 1000th base pair locations to the centre of TSS. This is done due to the prevention of the impact of gene expression on the generated distribution, as the selected intervals are relatively far away from TSS. Then, the fragment length densities from that distribution are sampled for each 201-fragment size and used as a parameter for Dirichlet distribution generation.
The initial parameter for Dirichlet distribution is set to 20. From the obtained Dirichlet distribution, 2000 fragments are sampled, and Shannon's entropy is calculated for those. The Shannon entropies are subsequently compared with the Shannon entropy values of five randomly selected background sets ( where ).