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Table 2 Pipeline performance evaluation on a synthetic dataset containing two mutations

From: Analysis of amplicon-based NGS data from neurological disease gene panels: a new method for allele drop-out management

% of reads from amplicon A SD1 SD2
Trimming pipeline MiSeq pipeline Trimming pipeline MiSeq pipeline
First step of variant calling Second step of variant calling First step of variant calling Second step of variant calling
36.75 1 2 1 1 1 1
100 2 2 2 2 2 2
90 2 2 2 2 2 2
80 2 2 2 2 2 2
70 2 2 2 2 2 2
60 2 2 2 2 2 2
50 1 2 2 1 2 2
40 1 2 1 1 2 2
30 1 2 1 1 2 2
20 1 2 1 1 2 1
10 1 2 1 1 2 1
0 0 0 0 0 0 0
  1. Results for both SD1 (two single nucleotide mutations) and SD2 (a single nucleotide insertion and a single nucleotide mutation) synthetic datasets are reported. The number of mutations found with trimming pipeline (during the first and second variant calling step) is reported. MiSeq pipeline performances for SD1 are comparable with the first step of variant calling of trimming pipeline and can identify the second mutation only if the percentage of reads from amplicon A (not affected by ADO) is above 50 %. For lower percentages, only trimming pipeline with the second step of variant calling can correctly identify the second mutation, even if amplicon A reads percentage lowers to 10 %. In SD2, trimming pipeline performances are identical to SD1, while MiSeq performances slightly improve, being able to identify the second mutation in two additional configurations (30 % and 40 %)
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