Contested result

GERP and phastCons: Brandes et al. 2023 Supplementary Table 2: full benchmark evaluations of ESM1b and all other methods reverses the order

Low severity
Not yet reviewed
Ordering reversal
Different tasks across studies

Brandes et al. 2023 Supplementary Table 2: full benchmark evaluations of ESM1b and all other methods ranks phastCons above GERP in 3 of 3 comparisons; other studies rank GERP above phastCons in 13 of 16.

An automatic lead, not a finding of error. The results below are shown as published, everywhere on the site, until a reviewer decides otherwise.

Which studies agree

3 papers rank GERP above phastCons in 13 of their 19 comparisons and phastCons higher in 3; 3 are ties. Brandes et al. 2023 ranks phastCons above GERP in all 3 comparisons that are not ties (1 is a tie).

Rank GERP higher (3): Livesey and Marsh 2025, Clinical likelihood ratios and balanced…, An openly licensed benchmark…
Rank phastCons higher (1): Brandes et al. 2023 (disagrees)
Mixed (0): none

Each paper sits where at least two thirds of its comparisons point; Mixed means neither model reaches two thirds. The larger, outlined dot is the paper that disagrees.

GERP higherTiephastCons higherNumbers are counts of comparisons; the outlined bar is the paper that disagrees.

Papers compared (4)
Paper	Role	Ranks GERP higher	Ranks phastCons higher	Ties
Brandes et al. 2023 Supplementary Table 2: full benchmark evaluations of ESM1b and all other methods · DOI	Disagrees	0	3	1
Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) and 1 more record · DOI	Agrees	5	0	2
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes and 1 more record · DOI	Agrees	4	1	1
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers and 1 more record · DOI	Agrees	4	2	0

Comparisons in Brandes et al. 2023 (4)

Protocol	Dataset	Metric	GERP	phastCons	Higher
ClinVar missense pathogenicity, variants scored by all 19 eligible methods (Brandes et al. 2023 Fig. 2e-f, Supplementary Table 2)	ClinVar missense pathogenic vs benign, primary isoforms (Brandes et al. 2023, from the EVE portal)	AUPRC (balanced PRC-AUC: mean over 100 random downsamples to equal pathogenic and benign counts (80% of the minority class); zero-shot), unitless	0.641	0.794	phastCons
ClinVar missense pathogenicity, variants scored by all 19 eligible methods (Brandes et al. 2023 Fig. 2e-f, Supplementary Table 2)	ClinVar missense pathogenic vs benign, primary isoforms (Brandes et al. 2023, from the EVE portal)	AUROC (global ROC-AUC over all variants; zero-shot), unitless	0.704	0.699	Tie
HGMD/gnomAD missense pathogenicity, variants scored by all 19 eligible methods (Brandes et al. 2023 Fig. 2e-f, Supplementary Table 2)	HGMD disease-causing vs gnomAD common missense variants, primary isoforms (Brandes et al. 2023)	AUPRC (balanced PRC-AUC: mean over 100 random downsamples to equal pathogenic and benign counts (80% of the minority class); zero-shot), unitless	0.645	0.791	phastCons
HGMD/gnomAD missense pathogenicity, variants scored by all 19 eligible methods (Brandes et al. 2023 Fig. 2e-f, Supplementary Table 2)	HGMD disease-causing vs gnomAD common missense variants, primary isoforms (Brandes et al. 2023)	AUROC (global ROC-AUC over all variants; zero-shot), unitless	0.7	0.712	phastCons

Comparisons in Livesey and Marsh 2025 (7)

Protocol	Dataset	Metric	GERP	phastCons	Higher
Livesey and Marsh 2025 clinical benchmark: pairwise AUROC win rate, ClinVar pathogenic versus gnomAD missense	Livesey and Marsh 2025 clinical set: ClinVar pathogenic versus gnomAD v4.1 missense (819 proteins)	Pairwise win rate (average pairwise win rate against all other VEPs, per-protein AUROC on shared variants, ClinVar P/LP versus gnomAD v4.1 missense, proteins with at least 10 of each), percent	12.4	8.73	GERP
Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, all 36 DMS sets	Livesey and Marsh 2025 human DMS benchmark set (36 proteins)	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, 36 human DMS sets), percent	8.8	7.01	GERP
Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, all 36 DMS sets	Livesey and Marsh 2025 human DMS benchmark set (36 proteins)	Pairwise win rate (average pairwise win rate against the five top-ranked VEPs (column top_5_winrate; the five are not listed in the table), \|Spearman\| with DMS on shared variants, 36 human DMS sets), percent	0.667	0.667	Tie
Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, direct assays only	Livesey and Marsh 2025 human DMS benchmark set (36 proteins)	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, direct DMS assays only), percent	9.13	9.13	Tie
Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, indirect assays only	Livesey and Marsh 2025 human DMS benchmark set (36 proteins)	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, indirect DMS assays only), percent	8.55	5.5	GERP
Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, SNV-accessible substitutions only	Livesey and Marsh 2025 human DMS benchmark set (36 proteins)	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, SNV-accessible substitutions only), percent	8.8	7.01	GERP
Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, SNV-accessible substitutions with gaps filled	Livesey and Marsh 2025 human DMS benchmark set (36 proteins)	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS, SNV-accessible substitutions, missing predictions filled with the VEP's most benign score), percent	9.43	7.54	GERP

Comparisons in Clinical likelihood ratios and balanced… (6)

Protocol	Dataset	Metric	GERP	phastCons	Higher
BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)	BRCA1 functional truth set: 1641 missense variants classed deleterious or tolerated by HAP1 cell survival after saturation genome editing	Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA1 functional truth set), unitless	7.59	5.4	GERP
BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)	BRCA1 functional truth set: 1641 missense variants classed deleterious or tolerated by HAP1 cell survival after saturation genome editing	False negatives (Functionally deleterious BRCA1 variants called tolerated), count	6	24	GERP
BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)	BRCA1 functional truth set: 1641 missense variants classed deleterious or tolerated by HAP1 cell survival after saturation genome editing	Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA1 functional truth set), unitless	1.78	1.47	GERP
BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)	BRCA2 functional truth set: 188 missense variants classed deleterious or tolerated by Homology-directed repair	Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA2 functional truth set), unitless	5.98	1.95	GERP
BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)	BRCA2 functional truth set: 188 missense variants classed deleterious or tolerated by Homology-directed repair	False negatives (Functionally deleterious BRCA2 variants called tolerated), count	0	0	Tie
BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)	BRCA2 functional truth set: 188 missense variants classed deleterious or tolerated by Homology-directed repair	Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA2 functional truth set), unitless	1.26	1.53	phastCons

Comparisons in An openly licensed benchmark… (6)

Protocol	Dataset	Metric	GERP	phastCons	Higher
CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv)	Missense variants in 768 Cancer Gene Census genes with open oncogenicity labels	AUROC (CGC missense, all genes), unitless	0.691	0.678	GERP
CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv)	Missense variants in 768 Cancer Gene Census genes with open oncogenicity labels	Average precision (CGC missense, all genes), unitless	0.285	0.268	GERP
CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv)	Missense variants in 768 Cancer Gene Census genes with open oncogenicity labels	AUROC (CGC missense, oncogenes (gain of function)), unitless	0.662	0.68	phastCons
CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv)	Missense variants in 768 Cancer Gene Census genes with open oncogenicity labels	Average precision (CGC missense, oncogenes (gain of function)), unitless	0.349	0.405	phastCons
CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv)	Missense variants in 768 Cancer Gene Census genes with open oncogenicity labels	AUROC (CGC missense, tumour suppressors (loss of function)), unitless	0.757	0.72	GERP
CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv)	Missense variants in 768 Cancer Gene Census genes with open oncogenicity labels	Average precision (CGC missense, tumour suppressors (loss of function)), unitless	0.253	0.206	GERP

What disagrees

Other studies: rank GERP above phastCons in 13 of 16 comparisons
This study: ranks phastCons above GERP in 3 of 3 comparisons

Compared: GERP, phastCons
Study that disagrees: Brandes et al. 2023 Supplementary Table 2: full benchmark evaluations of ESM1b and all other methods
Other studies: 6 studies
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes, Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx), An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers, OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role), Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML), Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8)
Rule: Ordering reversal; different tasks across studies
Model identity: Matched across studies partly through links not yet reviewed

The compared numbers

Each value links to its result record and source. Compare values within one protocol; the rule compares rankings, not raw values across protocols.

In the study that disagrees (6)
Study	Tool	Protocol or benchmark	Metric	Value
Brandes et al. 2023 Supplementary Table 2: full benchmark evaluations of ESM1b and all other methods; Genome-wide prediction of disease variant effects with a deep protein language model (Brandes et al., Nature Genetics 2023)	GERP++ (Brandes et al. 2023 clinical benchmarks)	ClinVar missense pathogenicity, variants scored by all 19 eligible methods (Brandes et al. 2023 Fig. 2e-f, Supplementary Table 2)	AUPRC (balanced PRC-AUC: mean over 100 random downsamples to equal pathogenic and benign counts (80% of the minority class); zero-shot)	0.641
Brandes et al. 2023 Supplementary Table 2: full benchmark evaluations of ESM1b and all other methods; Genome-wide prediction of disease variant effects with a deep protein language model (Brandes et al., Nature Genetics 2023)	phastCons (vertebrate) (Brandes et al. 2023 clinical benchmarks)	ClinVar missense pathogenicity, variants scored by all 19 eligible methods (Brandes et al. 2023 Fig. 2e-f, Supplementary Table 2)	AUPRC (balanced PRC-AUC: mean over 100 random downsamples to equal pathogenic and benign counts (80% of the minority class); zero-shot)	0.794
Brandes et al. 2023 Supplementary Table 2: full benchmark evaluations of ESM1b and all other methods; Genome-wide prediction of disease variant effects with a deep protein language model (Brandes et al., Nature Genetics 2023)	GERP++ (Brandes et al. 2023 clinical benchmarks)	HGMD/gnomAD missense pathogenicity, variants scored by all 19 eligible methods (Brandes et al. 2023 Fig. 2e-f, Supplementary Table 2)	AUPRC (balanced PRC-AUC: mean over 100 random downsamples to equal pathogenic and benign counts (80% of the minority class); zero-shot)	0.645
Brandes et al. 2023 Supplementary Table 2: full benchmark evaluations of ESM1b and all other methods; Genome-wide prediction of disease variant effects with a deep protein language model (Brandes et al., Nature Genetics 2023)	GERP++ (Brandes et al. 2023 clinical benchmarks)	HGMD/gnomAD missense pathogenicity, variants scored by all 19 eligible methods (Brandes et al. 2023 Fig. 2e-f, Supplementary Table 2)	AUROC (global ROC-AUC over all variants; zero-shot)	0.7
Brandes et al. 2023 Supplementary Table 2: full benchmark evaluations of ESM1b and all other methods; Genome-wide prediction of disease variant effects with a deep protein language model (Brandes et al., Nature Genetics 2023)	phastCons (vertebrate) (Brandes et al. 2023 clinical benchmarks)	HGMD/gnomAD missense pathogenicity, variants scored by all 19 eligible methods (Brandes et al. 2023 Fig. 2e-f, Supplementary Table 2)	AUPRC (balanced PRC-AUC: mean over 100 random downsamples to equal pathogenic and benign counts (80% of the minority class); zero-shot)	0.791
Brandes et al. 2023 Supplementary Table 2: full benchmark evaluations of ESM1b and all other methods; Genome-wide prediction of disease variant effects with a deep protein language model (Brandes et al., Nature Genetics 2023)	phastCons (vertebrate) (Brandes et al. 2023 clinical benchmarks)	HGMD/gnomAD missense pathogenicity, variants scored by all 19 eligible methods (Brandes et al. 2023 Fig. 2e-f, Supplementary Table 2)	AUROC (global ROC-AUC over all variants; zero-shot)	0.712

In the other studies (32)
Study	Tool	Protocol or benchmark	Metric	Value
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)	GERP++_RS_a (Cubuk et al. 2021)	BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)	Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA1 functional truth set)	7.59
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)	GERP++_RS_a (Cubuk et al. 2021)	BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)	False negatives (Functionally deleterious BRCA1 variants called tolerated)	6
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)	GERP++_RS_b (Cubuk et al. 2021)	BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)	Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA2 functional truth set)	5.98
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)	GERP++_RS_c (Cubuk et al. 2021)	BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)	Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA1 functional truth set)	1.78
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)	GERP++_RS_c (Cubuk et al. 2021)	BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)	Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA2 functional truth set)	1.26
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)	phastCons100way_vertebrate (Cubuk et al. 2021)	BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)	Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA1 functional truth set)	5.4
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)	phastCons100way_vertebrate (Cubuk et al. 2021)	BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)	False negatives (Functionally deleterious BRCA1 variants called tolerated)	24
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)	phastCons20way_mammalian (Cubuk et al. 2021)	BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)	Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA1 functional truth set)	1.47
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)	phastCons20way_mammalian (Cubuk et al. 2021)	BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)	Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA2 functional truth set)	1.95
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)	phastCons20way_mammalian (Cubuk et al. 2021)	BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)	Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA2 functional truth set)	1.53
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role)	GERP_92_mammals rank score, dbNSFP 5.3.1a (Lee 2026)	CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv)	AUROC (CGC missense, all genes)	0.691
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role)	GERP_92_mammals rank score, dbNSFP 5.3.1a (Lee 2026)	CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv)	AUROC (CGC missense, oncogenes (gain of function))	0.662
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role)	GERPpp_RS rank score, dbNSFP 5.3.1a (Lee 2026)	CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv)	Average precision (CGC missense, all genes)	0.285
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role)	GERPpp_RS rank score, dbNSFP 5.3.1a (Lee 2026)	CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv)	Average precision (CGC missense, oncogenes (gain of function))	0.349
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role)	GERPpp_RS rank score, dbNSFP 5.3.1a (Lee 2026)	CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv)	Average precision (CGC missense, tumour suppressors (loss of function))	0.253
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role)	GERPpp_RS rank score, dbNSFP 5.3.1a (Lee 2026)	CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv)	AUROC (CGC missense, tumour suppressors (loss of function))	0.757
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role)	phastCons100way_vertebrate rank score, dbNSFP 5.3.1a (Lee 2026)	CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv)	Average precision (CGC missense, all genes)	0.268
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role)	phastCons100way_vertebrate rank score, dbNSFP 5.3.1a (Lee 2026)	CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv)	AUROC (CGC missense, all genes)	0.678
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role)	phastCons100way_vertebrate rank score, dbNSFP 5.3.1a (Lee 2026)	CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv)	Average precision (CGC missense, tumour suppressors (loss of function))	0.206
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role)	phastCons100way_vertebrate rank score, dbNSFP 5.3.1a (Lee 2026)	CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv)	AUROC (CGC missense, tumour suppressors (loss of function))	0.72
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role)	phastCons17way_primate rank score, dbNSFP 5.3.1a (Lee 2026)	CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv)	Average precision (CGC missense, oncogenes (gain of function))	0.405
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role)	phastCons17way_primate rank score, dbNSFP 5.3.1a (Lee 2026)	CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv)	AUROC (CGC missense, oncogenes (gain of function))	0.68
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	GERP++ (Livesey and Marsh 2025)	Livesey and Marsh 2025 clinical benchmark: pairwise AUROC win rate, ClinVar pathogenic versus gnomAD missense	Pairwise win rate (average pairwise win rate against all other VEPs, per-protein AUROC on shared variants, ClinVar P/LP versus gnomAD v4.1 missense, proteins with at least 10 of each)	12.4
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	phastCons (Livesey and Marsh 2025)	Livesey and Marsh 2025 clinical benchmark: pairwise AUROC win rate, ClinVar pathogenic versus gnomAD missense	Pairwise win rate (average pairwise win rate against all other VEPs, per-protein AUROC on shared variants, ClinVar P/LP versus gnomAD v4.1 missense, proteins with at least 10 of each)	8.73
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	GERP++ (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, all 36 DMS sets	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, 36 human DMS sets)	8.8
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	phastCons (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, all 36 DMS sets	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, 36 human DMS sets)	7.01
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	GERP++ (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, indirect assays only	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, indirect DMS assays only)	8.55
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	phastCons (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, indirect assays only	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, indirect DMS assays only)	5.5
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	GERP++ (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, SNV-accessible substitutions with gaps filled	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS, SNV-accessible substitutions, missing predictions filled with the VEP's most benign score)	9.43
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	phastCons (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, SNV-accessible substitutions with gaps filled	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS, SNV-accessible substitutions, missing predictions filled with the VEP's most benign score)	7.54
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	GERP++ (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, SNV-accessible substitutions only	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, SNV-accessible substitutions only)	8.8
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	phastCons (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, SNV-accessible substitutions only	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, SNV-accessible substitutions only)	7.01

Follow-up

Current state: Not yet reviewed.

No one has reviewed this finding yet.

How to help resolve this

If you know why these studies disagree, for example a different population, metric or preprocessing step, or you have a corrected value, tell us through the contribution form. A reviewer checks every submission against its source before anything changes, and the step is added to the follow-up above.

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