Skip to main content

Table 7 Comparison of three existing tools used to identify exonic SAVs with MutPred Splice

From: MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing

Method

ANNOVAR

Human splicing finder

Skippy

MutPred splice

Splicing focus

Splice site disruption

All exonic and intronic

ESE/ESS disruption and cryptic splice site

All exonic

Prediction output

Binary label

Multiple output scores

Multiple output scores

Probabilistic, with additional hypothesis of splicing mechanism disrupted

TP

41

65

68 (61)

121

FP

4

33

15

7

TN

79

50

68

76

FN

140 (0)

116

113 (57)

60

FPR%

4.8

39.8

18.1

8.4

Sensitivity (%)

22.7 (100.0)

35.9

37.6 (51.7)

66.9

Specificity (%)

95.2

60.2

81.9

91.6

Accuracy (%)

58.9 (97.6)

48.1

59.7 (66.8)

79.2

MCC

0.22 (0.93)

-0.04

0.19 (0.34)

0.54

  1. Evaluation was based on 264 exonic variants (181 positive, 83 negative). Performance metrics are given for guidance only as not all tools may be directly comparable (due to different applications or limitations). Performance scores in parentheses reflect adjusted performance based upon the evaluation of only specific categories of splicing mutation (for example, splice site disruption) relevant to the respective tool. For methods that output multiple scores for a variant (HSF and Skippy), performance metrics may differ depending upon the features and thresholds applied. TP, true positives; FP, false positives; TN, true negatives; FN, false negatives; FPR, false positive rate; MCC, Matthews correlation coefficient.