Extended Data Fig. 2: Performance evaluation of callers with HG002 truthset at different coverages and platforms.
From: SVision: a deep learning approach to resolve complex structural variants

a, F-score of callers on different platforms evaluated with Truvari. The boxplot for HiFi data was the F-score measured for each caller at 5X, 10X and 28X coverage, respectively. Each box contains three values, that is, SVision (0.83, 0.89 and 0.90), SVIM (0.83, 0.89 and 0.89), pbsv (0.65, 0.79 and 0.82), CuteSV (0.83, 0.89 and 0.89) and Sniffles (0.72, 0.79 and 0.85). The boxplot for ONT data was the F-score measured for each caller at 5X, 10X and 47X coverage, respectively. Each box also contains three values (n = 3), that is, SVision (0.76, 0.84 and 0.92), SVIM (0.74, 0.82 and 0.89), pbsv (0.67, 0.78 and 0.84), CuteSV (0.77, 0.85 and 0.91) and Sniffles (0.74, 0.82 and 0.90). The boxplot defines the median (Q2, 50th percentile), first quartile (Q1, 25th percentile) and third quartile (Q3, 75th percentile). The bounds of box, that is interquartile range (IQR), of the boxplot is between Q1 and Q3. The minima and maxima values are defined as Q1-1.5*IQR and Q3 + 1.5*IQR, respectively. The whiskers are values between minima and Q1 as well as between Q3 and maxima. b, The precision (x-axis), recall (y-axis) and F-score (F, dotted line) measurements of detecting SVs from HiFi data at different coverages. c, The precision and recall measurements of detecting SVs from ONT data at different coverages. It should be noted that this evaluation ignored SV genotype, but only evaluated on event level.