Supporting data for "Evaluation of computational genotyping of Structural Variations for clinical diagnoses"

In recent years, Structural Variation (SV) has been identified as having a pivotal role in causing genetic disease. The discovery of SVs based on short DNA sequence reads from next-generation DNA sequence methods is error-prone, suffering from low sensitivity and high false discovery. These shortcomings can be partially overcome with extensive orthogonal validation methods, or use of long reads, but currently the cost of either precludes their application for routine clinical diagnostics. In contrast, SV genotyping of known sites of SV occurrence is relatively robust. Structural Variant genotyping therefore offers a cost-effective clinical diagnostic tool, with potentially few false positives and low occurrence of false negatives, even when applied to short-read DNA sequence data. We assess five state- of-the- art SV genotyping software methods, applied to short read sequence data. The methods are characterized based on their ability to genotype different SV types, spanning different size ranges. Furthermore, we analyze their ability to parse different VCF file sub-formats and assess their reliance on specific metadata. We compare the SV genotyping methods across a range of simulated and real data including SVs that were not found with Illumina data alone. We assess sensitivity and the ability to filter initial false discovery calls. Our results indicate that, although SV genotyping software methods have superior performance to SV callers, there are limitations that suggest the need for further innovation.