Table 1 List of tools providing good SV calling results for both the simulated and NA12878 real datasets
From: Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing
SV type | Tools | Simulated data | Real data | nF*1 | ||
|---|---|---|---|---|---|---|
Precision | Recall | Precision | Recall | |||
DEL | GRIDSS | 98.9 (5) | 86.6 (2) | 87.6 (7) | 28.9 (2) | 3.57 (1) |
Lumpy | 99.1 (4) | 81.4 (6) | 87.1 (8) | 26.1 (4) | 3.41 (2) | |
SVseq2 | 96.2 (11) | 86.1 (3) | 75.7 (17) | 24.9 (5) | 3.28 (3) | |
SoftSV | 96.8 (10) | 83.6 (4) | 80.2 (13) | 23.2 (8) | 3.25 (7) | |
Manta | 95.9 (12) | 83.1 (5) | 74.2 (20) | 24.3 (6) | 3.21 (5) | |
MATCHCLIP | 99.4 (2) | 71.7 (10) | 91.6 (4) | 20.9 (11) | 3.12 (6) | |
inGAP-sv | 91.1 (18) | 78.6 (7) | 78.3 (14) | 22.5 (8) | 3.10 (7) | |
DUP | Wham | 96.9 (4) | 81.7 (4) | 57.1 (4) | 10.2 (5) | 3.92 (1) |
SoftSV | 84.2 (14) | 67.8 (13) | 47.3 (6) | 14.3 (3) | 3.91 (2) | |
MATCHCLIP | 87.6 (11) | 77.5 (8) | 58.0 (3) | 9.9 (6) | 3.79 (3) | |
GRIDSS | 91.1 (9) | 77.9 (7) | 58.4 (2) | 9.6 (7) | 3.78 (4) | |
Manta | 99.0 (1) | 83.2 (1) | 40.4 (9) | 6.5 (11) | 3.35 (5) | |
SvABA | 82.6 (15) | 69.6 (11) | 42.7 (8) | 7.2 (9) | 3.02 (6) | |
INS [Unspecified] | pbsv | 89.7 (3) | 38.2 (5) | 72.7 (8) | 27.5 (2) | 6.68 (1) |
inGAP-sv | 99.7 (1) | 58.5 (2) | 85.5 (2) | 11.8 (3) | 6.27 (2) | |
Sniffles | 74.8 (5) | 52.5 (3) | 65.9 (10) | 9.0 (5) | 5.08 (3) | |
SVseq2 | 70.4 (8) | 64.2 (1) | 38.5 (19) | 7.1 (9) | 4.87 (4) | |
INS [MEI] | MELT | 99.7 (3) | 68.9 (3) | 88.9 (1) | 85.6 *2 (1) | 3.21 (1) |
Mobster | 100 (1) | 67.1 (4) | 88.3 (2) | 71.9 *2 (2) | 3.04 (2) | |
INV | DELLY | 94.7 (8) | 81.8 (4) | 38.9 (4) | 15.6 (2) | 3.07 (1) |
TIDDIT | 89.2 (14) | 77.9 (8) | 49.1 (1) | 11.7 (5) | 2.89 (2) | |
1–2-3-SV | 70.7 (19) | 81.2 (5) | 31.8 (9) | 14.8 (3) | 2.67 (3) | |
GRIDSS | 96.6 (6) | 84.7 (3) | 34.2 (8) | 10.4 (7) | 2.67 (4) | |