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Fig. 2 | Genome Medicine

Fig. 2

From: Optimizing Nanopore sequencing-based detection of structural variants enables individualized circulating tumor DNA-based disease monitoring in cancer patients

Fig. 2

Detection of somatic SVs with the SHARC pipeline based on high and low coverage nanopore data. High coverage nanopore sequencing data from COLO829 (melanoma cell line) and HGS-3 (ovarian cancer organoid) were subsampled to low coverages. Outer circles represent the high coverage sets (59x for COLO829 and 56x for HGS-3) and inner circles represent low coverage subsets (4x, 3x, 2x). The following filtering steps were applied in a cumulative manner in the order displayed. a Median percentage of non-somatic (red) and somatic (blue) breakpoints in the raw NanoSV calls for COLO829 (top) and HGS-3 (bottom). b Median percentage of non-somatic (left) and somatic (right) SV calls kept (green) or removed (brown) in the pre-filtering step for COLO829 and HGS-3. c Median percentage of non-somatic (left) and somatic (right) SV calls kept (green) or removed (brown) by the random forest SV classifier for COLO829 and HGS-3. d Median percentage of non-somatic (left) and somatic (right) SV calls kept (green) or removed (brown) by the database filtering for COLO829 and HGS-3. e Median percentage of non-somatic (red) and somatic SV (blue) calls in the complete SHARC output (left) and top 20 largest SVs (right) for COLO829 and HGS-3. f Total number of non-somatic (red) and somatic (blue) SV calls at each step of the pipeline for both COLO829 and HGS-3. In low coverage subsets, all data points are shown and the square box represents the median value. RF, random forest; DBFilter, database filter

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