Fig. 4

FusorSV result of 27 deep-coverage samples. a The Jaccard Similarity against the truth set provides the evidence that FusorSV gets more overlaps with the truth set than any single SV-calling algorithm. b Precision-recall of all SV-calling algorithms against the truth set. Being closer to the upper right corner means better performance, with the solid dot depicting the values of a sample. FusorSV improves performance by utilizing multiple algorithms while making fewer total calls than integrative consensus methods like MetaSV. c Plot depicts number of 1000GP events per sample not called by the specific caller (dm) versus the number of called events not present in the 1000GP (dn). Being closer to the bottom left indicates higher performance. Vertical line denotes average number of calls per sample in 1000GP