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. 2020 Jun 26;10:10493. doi: 10.1038/s41598-020-64353-1

Table 1.

Performance of CNV detection in the experimental data analysis for NA12156 and NA12878.

Sample(s) Algorithm CNV bases CNV regions Sample(s) Algorithm CNV bases CNV regions
bpa Recall FPR Precision Regionb Detection rate bpa Recall FPR Precision Regionb Detection rate
Single-sampleanalysis (NA12156) CONY 371,984 91.34% 9.71% 1.68% 33 91.67% Single-sample analysis (NA12878) CONY 202,607 91.43% 0.51% 1.65% 25 83.33%
CNVnator 343,308 84.30% 13.07% 1.15% 25 69.44% CNVnator 168,094 75.86% 0.54% 1.31% 11 36.67%
FREEC 86,204 21.17% 11.19% 0.34% 2 5.56% FREEC 124,272 56.08% 0.46% 1.13% 2 6.67%
rdxplorer 284,865 69.95% 2.28% 5.27% 11 30.56% rdxplorer 119,034 53.72% 2.17% 0.23% 7 23.33%
DGV 407,253 36 DGV 221,597 30
Paired-samples analysis (Case:NA12156/ Control:NA12878) CONY 376,510 73.10% 0.74% 18.55% 23 63.89% Paired- samples analysis (Case:NA12878/ Control:NA12156) CONY 355,947 69.11% 4.57% 0.32% 25 69.44%
CNVSeq 163,695 31.78% 15.44% 0.47% 29 80.56% CNVSeq 175,150 34.00% 0.50% 1.48% 33 91.67%
FREEC 178,282 34.61% 6.63% 1.18% 3 8.33% FREEC 230,142 44.68% 1.62% 0.59% 6 16.67%
DGV 515,073 36 DGV 515,073 36

aThe number of CNV basepairs in the DGV that are also identified by the algorithm.

bThe number of CNV regions in the DGV that have any position identified as a CNV via the algorithm.