Fig. 1
Assessment of conventional algorithms for detecting mutations with low-allele frequency. a Schematic of experimental design for test-base sequencing data. Four distinct sample mixtures (A, B, C, and D) were prepared and sequenced with three different sequencing platforms (ILH, ILA, and ITA). Constructed libraries from each platform were sequenced twice to produce sequencing replicates (X11 and X12). For samples A and B, two independent sets of sequencing library were additionally prepared to sequence data from library replicates (X21 and X31). Each set of sequencing data was sequentially downsampled ten times to evaluate the effects of read depth. All generated datasets were analyzed, and average performances were reported for each depth and platform. b Sensitivity and FPR of conventional methods (MuTect with adjusted parameters, others in Supplementary Figs. 1 and 2) by sequencing depth and VAF for each sequencing platform. Points are depicted within the maximum depth of the sequencing data (Supplementary Table 1). Error bars, 95% confidence intervals. Source data are provided as a Source Data file. c Distribution of allele frequencies and probabilistic odd-ratio scores (LODT) for true-positive and false-positive calls for each sample mixture (colored by blue and red, respectively). ILH hybrid-capture-based Illumina sequencing, ILA amplicon-based Illumina sequencing, ITA amplicon-based Ion Torrent sequencing, VAF variant allele frequency, FPR false-positive rate
