Fig. 5
From: Samplot: a platform for structural variant visual validation and automated filtering
Model performance in data sets that differ from the training set. a The number of true-positive and false-positive SVs from different SV calling and filtering methods considering the same sample (HG002), sequenced using two libraries with different coverages, read lengths, and insert sizes. b, c The percent increase in true-positive SVs that Samplot-ML recovers versus duphold (b) and SV2 (c) for SVs in simulated mixtures of samples (CHM13 and CHM1 cell lines) at different rates