Fig. 2

FusorSV Framework (see “Methods”). (1) VCF files are first converted to an internal callset representation and then are (2) partitioned using discriminating features. (3) For every partition, a pooled pairwise distance matrix is computed from all observations and then is incorporated into the additive group expectation for every possible combination of callers with Eq. 1 in “Methods.” Partitioned callsets for each sample are projected back into a coordinate single space, where the weight of each disjoint segment is given its previously estimated expectation value by lookup. (4) A partition is fit to the data by returning the value for the proposal expectation cutoff that is the closest to the truth. (5) Given new data during discovery, filtered partitions are merged back together from smallest to largest size, discarding the lesser of overlapping calls by their expectation value and then finally clustered to yield a genotyped VCF output (6)