The PSEM model aims to predict the position specific error rates associated with various genomic sequence contexts, under which the specific sequencing technology is prone to error. Based on publications evaluating features associated with sequencing errors and experiences from our previous effort, 9 types of significant features are considered. With the features fixed, using GLM, we evaluated the appropriateness of distributions with different mean – variance relationships and the ability to consider zero-inflation. Consistent with the computational tool EdgeR [23] for RNA-Seq data, we found the ability to model over-dispersion by NB distribution necessary for DNA-Seq data as well. Additionally, for DNA-Seq erroneous read counts modeling, zero-inflation is also a key factor for accurate prediction and inference. The much-elevated F1 score for 0.5 % allele frequency SNVs as well as the highest overall performance by ZINB GLM highlighted the importance of choosing suitable statistical models. Moreover, comparing with VarScan2, which conducts the Fisher’s exact test for each targeted location on paired normal-tumor sequencing data, the significance of applying the correct reference error model is exemplified by higher recalls as well
