Table 4.
Software | Statistics | Human | Mouse (All) | Mouse (Distinct) | Rat |
ProMirII-g | # of predictions | 690 | 656 | 640 | 615 |
# of TPs | 215 | 199 | 183 | 127 | |
# of FPs | 475 | 457 | 457 | 485 | |
# of real miRNAs missed | 25 | 46 | 29 | 19 | |
SE | 89.58% | 81.22% | 85.92% | 86.99% | |
PPV | 31.16% | 30.34% | 28.59% | 20.65% | |
miR-abela | # of predictions | 1036 | 915 | 901 | 646 |
# of TPs | 149 | 140 | 126 | 86 | |
# of FPs | 887 | 775 | 775 | 560 | |
# of real miRNAs missed | 91 | 105 | 86 | 60 | |
SE | 62.08% | 57.14% | 59.43% | 58.90% | |
PPV | 14.38% | 15.30% | 13.98% | 13.31% |
It should be noted that in the mouse genome, 25 of the pre-miRNAs are duplicated. In other words, only 212 mouse pre-miRNAs are distinct in the genome. To avoid overestimation of the performance of the software, we identify the duplicated ones and conduct two measurements. #, number; FP, false positives; TP, true positives; SE, sensitivity; PPV, positive predictive value.