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Contested result

ChromBPNet and Borzoi: Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants reverses the order

  • Low severity
  • Not yet reviewed
  • Ordering reversal
  • Different tasks across studies

Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants ranks ChromBPNet above Borzoi in 4 of 4 comparisons; other studies rank Borzoi above ChromBPNet in 12 of 15.

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

2 papers rank Borzoi above ChromBPNet in 12 of their 17 comparisons and ChromBPNet higher in 3; 2 are ties. Comparative Analysis of Deep Learning… ranks ChromBPNet above Borzoi in all 4 of its comparisons.

Papers grouped by which system they rank higher: Borzoi or ChromBPNetRank Borzoi higher: AlphaGenome Nature 2026, GUANinE v1.1 reveals complementarity…. Rank ChromBPNet higher: Comparative Analysis of Deep Learning… (disagrees). Mixed: none.Rank Borzoi higherMixedRank ChromBPNet higherAlphaGenome Nature 2026AlphaGenome Nature 2026 supplementary comparison tablesGUANinE v1.1 revealscomplementarity…GUANinE v1.1 reveals complementarity of supervised and genomic language modelsComparative Analysisof Deep Learning…Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants
  • Rank Borzoi higher (2): AlphaGenome Nature 2026, GUANinE v1.1 reveals complementarity…
  • Rank ChromBPNet higher (1): Comparative Analysis of Deep Learning… (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.

Comparisons in which each paper scores Borzoi or ChromBPNet higherComparative Analysis of Deep Learning… (disagrees): Borzoi higher in 0, ChromBPNet higher in 4, 0 ties. AlphaGenome Nature 2026: Borzoi higher in 7, ChromBPNet higher in 3, 0 ties. GUANinE v1.1 reveals complementarity…: Borzoi higher in 5, ChromBPNet higher in 0, 2 ties.Disagrees: Comparative Analysis of Deep Learning…Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants4 ChromBPNet higher4AlphaGenome Nature 2026AlphaGenome Nature 2026 supplementary comparison tables7 Borzoi higher73 ChromBPNet higher3GUANinE v1.1 reveals complementarity…GUANinE v1.1 reveals complementarity of supervised and genomic language models5 Borzoi higher52 tie2
Borzoi higherTieChromBPNet higherNumbers are counts of comparisons; the outlined bar is the paper that disagrees.
Comparisons in Comparative Analysis of Deep Learning… (4)
ProtocolDatasetMetricBorzoiChromBPNetHigher
Allelic reporter effect correlation in HeLa (Manzo et al. 2025 Table 1)HeLa regulatory variant reporter data (Manzo et al. 2025)Pearson (predicted versus measured log2 fold-change of variant effect), unitless-0.0660.289ChromBPNet
Allelic reporter effect correlation in HepG2 (Manzo et al. 2025 Table 1)HepG2 regulatory variant reporter data (Manzo et al. 2025)Pearson (predicted versus measured log2 fold-change of variant effect), unitless0.1250.295ChromBPNet
Allelic reporter effect correlation in K562 (Manzo et al. 2025 Table 1)K562 regulatory variant reporter data (Manzo et al. 2025)Pearson (predicted versus measured log2 fold-change of variant effect), unitless0.0330.287ChromBPNet
Allelic reporter effect correlation in NPC (Manzo et al. 2025 Table 1)NPC regulatory variant reporter data (Manzo et al. 2025)Pearson (predicted versus measured log2 fold-change of variant effect), unitless0.0230.08ChromBPNet
Comparisons in AlphaGenome Nature 2026 (10)
ProtocolDatasetMetricBorzoiChromBPNetHigher
African-ancestry LCL caQTL classification (AlphaGenome paper)African-ancestry LCL caQTL classification: evaluated data subsetAUPRC, unitless0.4620.415Borzoi
European-ancestry LCL caQTL classification (AlphaGenome paper)European-ancestry LCL caQTL classification: evaluated data subsetAUPRC, unitless0.3120.279Borzoi
Yoruba LCL dsQTL classification (AlphaGenome paper)Yoruba LCL dsQTL classification: evaluated data subsetAUPRC, unitless0.6060.543Borzoi
African-ancestry LCL caQTL effect-size prediction (AlphaGenome paper)African-ancestry LCL caQTL effect-size prediction: evaluated data subsetPearson, unitless0.6490.674ChromBPNet
European-ancestry LCL caQTL effect-size prediction (AlphaGenome paper)European-ancestry LCL caQTL effect-size prediction: evaluated data subsetPearson, unitless0.5110.525ChromBPNet
Yoruba LCL dsQTL effect-size prediction (AlphaGenome paper)Yoruba LCL dsQTL effect-size prediction: evaluated data subsetPearson, unitless0.7920.772Borzoi
Microglia caQTL effect-size prediction (AlphaGenome paper)Microglia caQTL effect-size prediction: evaluated data subsetPearson, unitless0.6160.61Borzoi
Coronary smooth-muscle caQTL effect-size prediction (AlphaGenome paper)Coronary smooth-muscle caQTL effect-size prediction: evaluated data subsetPearson, unitless0.630.658ChromBPNet
SPI1 binding QTL classification (AlphaGenome paper)SPI1 binding QTL classification: evaluated data subsetAUPRC, unitless0.4680.356Borzoi
SPI1 binding QTL effect-size prediction (AlphaGenome paper)SPI1 binding QTL effect-size prediction: evaluated data subsetPearson, unitless0.5350.52Borzoi
Comparisons in GUANinE v1.1 reveals complementarity… (7)

What disagrees

Other studies
rank Borzoi above ChromBPNet in 12 of 15 comparisons
This study
ranks ChromBPNet above Borzoi in 4 of 4 comparisons
Compared
ChromBPNet, Borzoi
Study that disagrees
Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants
Other studies
GUANinE v1.1 reveals complementarity of supervised and genomic language models, AlphaGenome Nature 2026 supplementary comparison tables
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 (8)
StudyToolProtocol or benchmarkMetricValue
Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory VariantsBorzoi (Manzo et al. 2025)Allelic reporter effect correlation in HeLa (Manzo et al. 2025 Table 1)Pearson (predicted versus measured log2 fold-change of variant effect)−0.066
Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory VariantsChromBPNet (Manzo et al. 2025)Allelic reporter effect correlation in HeLa (Manzo et al. 2025 Table 1)Pearson (predicted versus measured log2 fold-change of variant effect)0.289
Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory VariantsBorzoi (Manzo et al. 2025)Allelic reporter effect correlation in HepG2 (Manzo et al. 2025 Table 1)Pearson (predicted versus measured log2 fold-change of variant effect)0.125
Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory VariantsChromBPNet (Manzo et al. 2025)Allelic reporter effect correlation in HepG2 (Manzo et al. 2025 Table 1)Pearson (predicted versus measured log2 fold-change of variant effect)0.295
Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory VariantsBorzoi (Manzo et al. 2025)Allelic reporter effect correlation in K562 (Manzo et al. 2025 Table 1)Pearson (predicted versus measured log2 fold-change of variant effect)0.033
Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory VariantsChromBPNet (Manzo et al. 2025)Allelic reporter effect correlation in K562 (Manzo et al. 2025 Table 1)Pearson (predicted versus measured log2 fold-change of variant effect)0.287
Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory VariantsBorzoi (Manzo et al. 2025)Allelic reporter effect correlation in NPC (Manzo et al. 2025 Table 1)Pearson (predicted versus measured log2 fold-change of variant effect)0.023
Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory VariantsChromBPNet (Manzo et al. 2025)Allelic reporter effect correlation in NPC (Manzo et al. 2025 Table 1)Pearson (predicted versus measured log2 fold-change of variant effect)0.08
In the other studies (30)
StudyToolProtocol or benchmarkMetricValue
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLchrombpnet (paper Table 4)SPI1 binding QTL classification (AlphaGenome paper)AUPRC0.356
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLchrombpnet (paper Table 4)European-ancestry LCL caQTL classification (AlphaGenome paper)AUPRC0.279
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLchrombpnet (paper Table 4)African-ancestry LCL caQTL classification (AlphaGenome paper)AUPRC0.415
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLchrombpnet (paper Table 4)Coronary smooth-muscle caQTL effect-size prediction (AlphaGenome paper)Pearson0.658
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLchrombpnet (paper Table 4)SPI1 binding QTL effect-size prediction (AlphaGenome paper)Pearson0.52
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLborzoi-ensemble (paper Table 4)Yoruba LCL dsQTL effect-size prediction (AlphaGenome paper)Pearson0.792
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLborzoi-ensemble (paper Table 4)African-ancestry LCL caQTL classification (AlphaGenome paper)AUPRC0.462
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLchrombpnet (paper Table 4)Microglia caQTL effect-size prediction (AlphaGenome paper)Pearson0.61
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLborzoi-ensemble (paper Table 4)Microglia caQTL effect-size prediction (AlphaGenome paper)Pearson0.616
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLborzoi-ensemble (paper Table 4)European-ancestry LCL caQTL effect-size prediction (AlphaGenome paper)Pearson0.511
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLborzoi-ensemble (paper Table 4)European-ancestry LCL caQTL classification (AlphaGenome paper)AUPRC0.312
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLchrombpnet (paper Table 4)Yoruba LCL dsQTL classification (AlphaGenome paper)AUPRC0.543
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLchrombpnet (paper Table 4)African-ancestry LCL caQTL effect-size prediction (AlphaGenome paper)Pearson0.674
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLborzoi-ensemble (paper Table 4)African-ancestry LCL caQTL effect-size prediction (AlphaGenome paper)Pearson0.649
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLchrombpnet (paper Table 4)Yoruba LCL dsQTL effect-size prediction (AlphaGenome paper)Pearson0.772
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLborzoi-ensemble (paper Table 4)SPI1 binding QTL effect-size prediction (AlphaGenome paper)Pearson0.535
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLborzoi-ensemble (paper Table 4)SPI1 binding QTL classification (AlphaGenome paper)AUPRC0.468
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLborzoi-ensemble (paper Table 4)Yoruba LCL dsQTL classification (AlphaGenome paper)AUPRC0.606
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLborzoi-ensemble (paper Table 4)Coronary smooth-muscle caQTL effect-size prediction (AlphaGenome paper)Pearson0.63
AlphaGenome Nature 2026 supplementary comparison tables; AlphaGenome Nature 2026 supplementary methods; alphagenome: Journal full-text XMLchrombpnet (paper Table 4)European-ancestry LCL caQTL effect-size prediction (AlphaGenome paper)Pearson0.525
GUANinE v1.1 reveals complementarity of supervised and genomic language modelsBorzoi (1% eval (evaluated with 1% of the training set), GUANinE v1.1)GUANinE v1.1 cadd-snv, Spearman rho x100 (Table 3)Spearman (GUANinE v1.1 test set)21
GUANinE v1.1 reveals complementarity of supervised and genomic language modelsBorzoi (1% eval (evaluated with 1% of the training set), GUANinE v1.1)GUANinE v1.1 ccre-propensity, Spearman rho x100 (Table 2)Spearman (GUANinE v1.1 test set)57.7
GUANinE v1.1 reveals complementarity of supervised and genomic language modelsBorzoi (1% eval (evaluated with 1% of the training set), GUANinE v1.1)GUANinE v1.1 cons100, Spearman rho x100 (Table 2)Spearman (GUANinE v1.1 test set)59.7
GUANinE v1.1 reveals complementarity of supervised and genomic language modelsBorzoi (1% eval (evaluated with 1% of the training set), GUANinE v1.1)GUANinE v1.1 cons30, Spearman rho x100 (Table 2)Spearman (GUANinE v1.1 test set)71.5
GUANinE v1.1 reveals complementarity of supervised and genomic language modelsBorzoi (1% eval (evaluated with 1% of the training set), GUANinE v1.1)GUANinE v1.1 dnase-propensity, Spearman rho x100 (Table 2)Spearman (GUANinE v1.1 test set)70.7
GUANinE v1.1 reveals complementarity of supervised and genomic language modelsChromBPNet-ATAC-F0 (1% eval (evaluated with 1% of the training set), GUANinE v1.1)GUANinE v1.1 cadd-snv, Spearman rho x100 (Table 3)Spearman (GUANinE v1.1 test set)17.3
GUANinE v1.1 reveals complementarity of supervised and genomic language modelsChromBPNet-ATAC-F0 (1% eval (evaluated with 1% of the training set), GUANinE v1.1)GUANinE v1.1 cons100, Spearman rho x100 (Table 2)Spearman (GUANinE v1.1 test set)41.5
GUANinE v1.1 reveals complementarity of supervised and genomic language modelsChromBPNet-ATAC-F0 (1% eval (evaluated with 1% of the training set), GUANinE v1.1)GUANinE v1.1 cons30, Spearman rho x100 (Table 2)Spearman (GUANinE v1.1 test set)47.8
GUANinE v1.1 reveals complementarity of supervised and genomic language modelsChromBPNet-ATAC-F0 (1% eval (evaluated with 1% of the training set), GUANinE v1.1)GUANinE v1.1 dnase-propensity, Spearman rho x100 (Table 2)Spearman (GUANinE v1.1 test set)41.5
GUANinE v1.1 reveals complementarity of supervised and genomic language modelsChromBPNet-ATAC-F3 (1% eval (evaluated with 1% of the training set), GUANinE v1.1)GUANinE v1.1 ccre-propensity, Spearman rho x100 (Table 2)Spearman (GUANinE v1.1 test set)40.5

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.

Finding discrepancy-5769cc09506e7e20 · All contested results · How findings are made and followed up