Table 1. Results and performance comparison of in silico predictors with the optimal threshold based on MCC.

#PV: number of predicted variants; Ac: accuracy; Se: sensitivity; Sp: specificity; MCC: Matthews correlation coefficient; LR+: positive likelihood ratio; LR-: negative likelihood ratio; 95% CI: 95% confidence interval.

Tool	Decision threshold	#PV	TP	FN	FP	TN	Ac	Se	Sp	MCC	LR+	LR +95% CI	LR-	LR- 95% CI
BayesDel_addAF	≥0.39	531	250	164	22	95	0.65	0.6	0.81	0.34	3.21	[2.19, 4.72]	0.49	[0.42, 0.57]
CADD	>10.44	886	655	39	101	91	0.84	0.94	0.47	0.49	1.79	[1.57, 2.05]	0.12	[0.08, 0.17]
ClinPred	>0.95	481	265	99	43	74	0.7	0.73	0.63	0.32	1.98	[1.55, 2.53]	0.43	[0.35, 0.53]
Condel	>0.3	481	331	33	76	41	0.77	0.91	0.35	0.31	1.4	[1.22, 1.61]	0.26	[0.17, 0.39]
DANN	>0.96	531	372	42	71	46	0.79	0.9	0.39	0.33	1.48	[1.28, 1.72]	0.26	[0.18, 0.37]
Eigen-PC	>1.87	531	329	85	35	82	0.77	0.79	0.7	0.44	2.66	[2, 3.52]	0.29	[0.23, 0.37]
FATHMM	≤–3.39	481	150	214	23	94	0.51	0.41	0.8	0.19	2.1	[1.42, 3.08]	0.73	[0.65, 0.83]
fathmm-MKL	>0.7	531	328	86	39	78	0.76	0.79	0.67	0.41	2.38	[1.83, 3.09]	0.31	[0.25, 0.39]
GERP++	>3.49	531	248	166	26	91	0.64	0.6	0.78	0.31	2.7	[1.9, 3.82]	0.52	[0.44, 0.6]
integrated_fitCons	>0.05	531	414	1	117	1	0.78	1	0.01	0.04	1.01	[0.99, 1.02]	0.28	[0.02, 4.51]
LIST-S2	≥0.75	344	246	28	39	31	0.81	0.9	0.44	0.36	1.61	[1.3, 1.99]	0.23	[0.15, 0.36]
LRT	<0.3	270	169	7	84	10	0.66	0.96	0.11	0.13	1.07	[1, 1.16]	0.37	[0.15, 0.95]
MetaLR_score	>0.8	481	251	113	42	75	0.68	0.69	0.64	0.29	1.92	[1.49, 2.47]	0.48	[0.39, 0.59]
MetaSVM_score	>0.6	481	260	104	39	78	0.7	0.71	0.67	0.34	2.14	[1.65, 2.79]	0.43	[0.35, 0.53]
MutationAssessor	>2.53	359	249	36	41	33	0.79	0.87	0.45	0.33	1.58	[1.28, 1.94]	0.28	[0.19, 0.42]
MutationTaster	>0.95	531	386	28	102	15	0.76	0.93	0.13	0.09	1.07	[0.99, 1.15]	0.53	[0.29, 0.95]
MutPred	>0.5	467	343	12	96	16	0.77	0.97	0.14	0.2	1.13	[1.04, 1.22]	0.24	[0.12, 0.49]
phastCons17way	>0.17	531	357	57	57	60	0.79	0.86	0.51	0.38	1.77	[1.46, 2.14]	0.27	[0.2, 0.36]
phastCons30way	>0.28	531	329	85	51	66	0.74	0.79	0.56	0.33	1.82	[1.48, 2.25]	0.36	[0.28, 0.47]
phyloP100way	>0.42	531	349	65	56	61	0.77	0.84	0.52	0.35	1.76	[1.45, 2.14]	0.3	[0.23, 0.4]
phyloP30way	>0.51	531	307	107	63	54	0.68	0.74	0.46	0.18	1.38	[1.15, 1.64]	0.56	[0.43, 0.72]
PolyPhen-2	>0.65	481	243	121	37	80	0.67	0.67	0.68	0.31	2.11	[1.6, 2.78]	0.49	[0.4, 0.59]
PROVEAN	≤–1.03	481	358	6	106	11	0.77	0.98	0.09	0.18	1.09	[1.02, 1.15]	0.18	[0.07, 0.46]
REVEL	>0.65	481	294	70	46	71	0.76	0.81	0.61	0.39	2.05	[1.63, 2.59]	0.32	[0.25, 0.41]
SIFT	<0.1	481	325	39	74	43	0.77	0.89	0.37	0.3	1.41	[1.22, 1.63]	0.29	[0.2, 0.43]
SiPhy_29way	>10.62	531	233	181	33	84	0.6	0.56	0.72	0.23	2	[1.48, 2.7]	0.61	[0.52, 0.71]
VEST4	>0.7	531	273	141	33	84	0.67	0.66	0.72	0.32	2.34	[1.74, 3.15]	0.47	[0.4, 0.57]
Splicing prediction
ada	>0.5	56	47	3	1	5	0.93	0.94	0.83	0.68	5.64	[0.94, 33.8]	0.07	[0.02, 0.23]
MaxEntScan	Diff >2 and Per >5	54	50	2	1	2	0.95	0.96	0.67	0.55	2.88	[0.58, 14.31]	0.06	[0.01, 0.28]
rf	>0.6	56	47	3	1	5	0.93	0.94	0.83	0.68	5.64	[0.94, 33.8]	0.07	[0.02, 0.23]
SpliceAI	>0.65	663	35	23	1	604	0.96	0.6	1	0.75	365.09	[50.94, 2616.41]	0.4	[0.29, 0.55]