frequency, whereas the majority of previous studies focused on SNVs of > = 5 % allele frequency; 2. one popular paradigm of SNV calling is a two-step procedure, first generating SNV candidates and then applying multiple sequencing quality filters to refine the SNV call. The PSEM aims to efficiently recover high quality SNV candidates to facilitate the filtering step, thus it is only fair to compare the performance of PSEM with other candidate generating methods. The result from VarScan2 before applying sequencing quality filters was included in Table 2. It is evident that except for Poisson GLM, the other methods outperformed VarScan2 in both recall and precision. Therefore, choosing appropriate statistical modeling method enables us to recover more true SNVs without any loss of precision in candidate generating step.Table 2Overall performance comparison for Ion Proton testing benchmarkPoissonNBZIPZINBVarScan2Recall0.980.890.950.900.83Precision0.250.620.540.710.53F1 Score0.400.730.690.790.65
