Short answer: SpliceAI is one of the strongest general-purpose splice-effect predictors, but no tool is a universal winner. Pangolin is its closest published comparator, while MMSplice, SQUIRLS, OpenSpliceAI and CI-SpliceAI can add complementary evidence. The best choice depends on whether you are locating splice sites, predicting the effect of a canonical or exonic variant, triaging deep-intronic changes, or building a clinical pipeline.
Published comparisons also show why a single leaderboard is misleading. Studies used different assays, genes, variant classes, transcript annotations, score thresholds and evaluation metrics. A high score from one model is therefore evidence to investigate—not proof that a variant is pathogenic or that another model is wrong.
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What splice-variant prediction tools actually predict
Splicing predictors estimate how a DNA change could alter RNA processing. Depending on the model, the output may be a probability for an acceptor or donor site, a predicted change in splice-site strength, a classification of whether a variant disrupts splicing, or a ranking of alternative splice events.
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These are different prediction tasks. A model that accurately identifies the position of a splice site is not automatically the best model for classifying the effect of a particular patient variant. Likewise, a benchmark based on measured minigene or massively parallel splicing assays cannot be interpreted as a direct measure of clinical pathogenicity.
#1 Best Overall
SpliceAI’s delta scores
SpliceAI is a deep-learning sequence model. Its commonly reported delta scores represent the largest predicted change in acceptor or donor probability around a variant. Analysts normally inspect the score type (acceptor gain, acceptor loss, donor gain or donor loss), the predicted position of the event, the reference assembly, transcript annotation and the sequence distance examined around the variant.
The score is not a probability that a patient is affected, and it is not a substitute for RNA evidence. The same numeric value can have different practical meaning when generated with a different transcript set, assembly, masking distance or threshold.
At-a-glance comparison
| Tool or model family | Primary use in the cited evidence | What the evidence supports | Important qualification |
|---|---|---|---|
| SpliceAI | Variant-level splice-effect prediction from sequence | Among the strongest performers in a 2023 massively parallel splicing benchmark; best result in a 2026 deep-intronic comparison at study-optimized thresholds | Performance depends on annotation, assembly and cutoff; standard cutoffs were not well calibrated for the deep-intronic set |
| Pangolin | Splice-site prediction from DNA sequence | Among the most frequently top-performing tools in the 2023 benchmark; its 2022 study reported higher average splice-site metrics than SpliceAI | The 2022 figures came from Pangolin’s own evaluation and are not a universal clinical ranking |
| MMSplice | Modular prediction of splice-related sequence effects | Useful complementary model assessed in the multi-tool benchmark | Annotation and transcript choice affect results; no evidence of universal superiority |
| SQUIRLS | Variant prioritization for possible splicing effects | Included as a complementary alternative in the multi-tool benchmark | Cross-tool outputs were not interchangeable, especially for exonic variants |
| OpenSpliceAI | Open reimplementation of the SpliceAI approach | Similar to original SpliceAI on larger, canonical-heavy benchmarks in the 2026 comparison | Used PyTorch and RefSeq MANE v1.3 transcripts in that study; diverged from original SpliceAI on deep-intronic data |
| CI-SpliceAI | Retrained or reimplemented SpliceAI-style model | Similar to original SpliceAI on canonical-heavy sets in the 2026 comparison | Used curated GENCODE HAVANA splice-site annotations; deep-intronic behavior differed |
What the strongest head-to-head studies found
2023 massively parallel splicing assay benchmark
Riepe and colleagues compared eight predictors against experimental outcomes for 3,616 variants in five genes. The benchmark used massively parallel splicing assays, so the labels represented measured effects in the tested experimental systems rather than clinical diagnoses.
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The study also found substantial variation in agreement among tools. Median pairwise Pearson correlation was 0.58, with values ranging from 0.04 to 0.97. Pangolin and SpliceAI were highly aligned with one another in the reported analysis, with a correlation of 0.97, while agreement within exonic variants fell to 0.43. Lower agreement and lower prediction-to-assay concordance for exonic changes are a practical warning: a disagreement on an exonic variant may reflect model limitations rather than a simple error by one predictor.
2022 Pangolin evaluation
The Pangolin study evaluated splice-site prediction across tissues and reported an average top-1 accuracy of 79% and an area under the precision-recall curve (AUPRC) of 0.85. SpliceAI scored 75% and 0.77, respectively, in that evaluation. Those numbers concern splice-site localization under the Pangolin paper’s data and definitions. They should not be compared directly with a variant-level balanced-accuracy result from another paper.
2026 deep-intronic comparison
Fortier, Rudy and Scherer compared original SpliceAI with OpenSpliceAI and CI-SpliceAI across several datasets, including functional assays, curated deep-intronic cases and ClinVar-derived sets. On their curated deep-intronic benchmark, original SpliceAI reached a balanced accuracy of 0.940 at the study’s optimized threshold and outperformed the two alternatives.
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The authors also reported that all three models behaved similarly on larger benchmarks dominated by canonical splice-site variants, while differences became material for deep-intronic variants. Their paper notes limitations from dataset size, clinically classified labels and threshold optimization. It also discloses that all authors work for Golden Helix, which develops VarSeq and includes legacy ensemble tools in its comparison. That disclosure is relevant context when interpreting the paper’s comparative results.
Why rankings change from one study to another
Prediction task
Before comparing scores, identify the endpoint. Splice-site localization asks whether a model can place a donor or acceptor at the correct position. Variant-effect prediction asks whether a specific allele changes splicing. Prioritization asks which variants should be tested first. These tasks use different labels and metrics, so an AUPRC for site localization and balanced accuracy for variant classification are not interchangeable.
Variant class
Canonical splice-site substitutions are usually easier than changes several introns away, and exonic variants can be difficult because they may affect both coding sequence and regulatory splicing elements. Treat canonical, near-splice, exonic and deep-intronic variants as separate analysis groups. A tool’s performance in one group should not be generalized to another.
Validation set and class balance
Record the genes, number of variants, assay type and positive-to-negative ratio. The 2023 benchmark’s 3,616 variants came from five genes and functional assays. The 2026 study combined datasets with different construction methods, including a Riepe functional set containing only 213 variants from two genes, curated deep-intronic cases and ClinVar-derived labels. A model can appear stronger when the benchmark contains easier variant classes or when labels are derived from clinical assertions rather than direct RNA measurements.
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Gene-model annotations determine which exons and splice junctions a predictor sees. The 2023 comparison explicitly noted that annotation files and transcript selection affect tools requiring gene models. In the 2026 comparison, original SpliceAI used GENCODE v24, OpenSpliceAI used RefSeq MANE v1.3 transcripts and CI-SpliceAI used curated GENCODE HAVANA splice-site annotations. A result generated on one transcript set may not reproduce on another, even with the same DNA variant.
Rank #3
Calibration and score cutoffs
A cutoff converts a continuous score into a decision. Optimizing that cutoff on the test set can improve reported accuracy while making the result look better than it will be on new cases. Always state whether a threshold was fixed in advance or tuned on the benchmark.
Thresholds: why the default SpliceAI rule can mislead
Common SpliceAI guidance describes 0.2 as a high-recall cutoff, 0.5 as a balanced-accuracy cutoff and 0.8 as a high-precision cutoff. These are general operating points, not universal laws.
In the 2026 deep-intronic comparison, the best study-specific threshold was approximately 0.04 for original SpliceAI and 0.01 for both CI-SpliceAI and OpenSpliceAI. Those values are much lower than 0.5. Applying the usual 0.5 rule to deep-intronic screening could therefore miss candidates. Conversely, using a very low threshold increases the number of variants requiring review and may reduce positive predictive value. The authors caution that separately optimized thresholds can overestimate real-world performance.
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How to choose a tool for a real analysis
Step 1: define the biological question
- Known or suspected splice-site change: prioritize a model evaluated for splice-site localization and inspect the predicted junction position.
- Single-variant effect prediction: use SpliceAI or Pangolin as primary predictors and consider a second model for independent evidence.
- Exonic variant triage: expect lower agreement and review transcript context carefully; do not treat a single score as decisive.
- Deep-intronic search: compare model outputs at a sensitivity-oriented cutoff and validate promising candidates experimentally or with patient RNA when possible.
Step 2: normalize the variant and transcript
Use a consistent genome assembly and representation, verify the reference and alternate alleles, and document the transcript accession and version. For genes with multiple clinically relevant isoforms, run the analysis on each justified transcript rather than silently accepting whichever transcript a package selects by default.
Step 3: run at least one strong baseline and one complementary model
SpliceAI provides a strong baseline. Pangolin is a serious comparator, not merely a backup. MMSplice and SQUIRLS can add different modeling assumptions, while OpenSpliceAI and CI-SpliceAI are useful when an open implementation or alternative training annotation is operationally important. Disagreement is informative when the models were run on documented, comparable inputs.
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Step 4: preserve metadata with every score
- Reference assembly and chromosome naming convention
- Transcript annotation source and release
- Transcript identifier and version
- Model name, release and weight file
- Sequence window or masking distance
- Raw score components and predicted event position
- Threshold and whether it was prespecified or optimized
- Software environment and command-line options
Step 5: seek orthogonal evidence
Use RNA sequencing, targeted transcript assays, minigene experiments, segregation, population data and clinical phenotype review as appropriate. A computational score can prioritize a test; it cannot by itself confirm pathogenicity, establish penetrance or replace laboratory and clinical assessment.
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SpliceAI
SpliceAI is a strong general baseline and performed particularly well in the cited deep-intronic comparison. Its main operational risks are treating the delta score as a clinical probability, hiding transcript or assembly choices, and applying a canonical-site cutoff to a deep-intronic cohort. Report whether the predicted change is an acceptor gain, acceptor loss, donor gain or donor loss, and inspect the predicted distance from the variant.
Pangolin
Pangolin is the closest comparator in the published evidence. Its own study reported stronger average splice-site metrics than SpliceAI, and the independent 2023 assay benchmark placed both tools among the most frequent top performers. Because those studies used different endpoints, the numbers should be used to understand task-specific strengths rather than to declare Pangolin globally superior.
MMSplice and SQUIRLS
These tools are useful as complementary alternatives in a multi-model workflow. The benchmark evidence does not justify calling either universally inferior. Their dependence on transcript annotation means that a fair comparison requires matching annotation inputs and recording transcript selection.
OpenSpliceAI and CI-SpliceAI
These should not be treated as bit-for-bit substitutes for the original model. OpenSpliceAI used PyTorch and RefSeq MANE v1.3 transcripts in the 2026 study; CI-SpliceAI used curated GENCODE HAVANA splice-site annotations. Their close behavior on canonical-heavy datasets does not guarantee equivalent results for deep-intronic variants, where the study observed divergence.
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Deployment, licensing and reproducibility
Operational details are part of the scientific comparison. The 2026 paper characterizes original SpliceAI model weights and precomputed scores as limited to academic and non-commercial use. Verify the license attached to the exact model weights, precomputed data and software release before redistribution, commercial analysis or clinical deployment. Do not assume that an open-source reimplementation carries the same terms as the original weights or score files.
Best Value
Record software versions and container or environment definitions, archive the annotation files, and retain the raw output. If a vendor package wraps several predictors, identify the underlying model and version rather than citing only the wrapper. Re-run a small internal regression set after any upgrade because annotation changes can alter predictions without changing the variant list.
Common failure modes
Comparing incomparable metrics
Symptom: one paper reports a higher AUPRC and another reports a higher balanced accuracy. Fix: keep metrics attached to their task, labels, variant class and dataset; do not merge them into one ranking.
Using one transcript without checking alternatives
Symptom: a predicted splice event appears clinically implausible or disappears in another laboratory’s report. Fix: compare justified transcripts and document annotation release and transcript version.
Applying 0.5 to every variant
Symptom: a deep-intronic screen returns few candidates despite known positive controls. Fix: inspect score distributions, evaluate a sensitivity-oriented cutoff and validate the threshold on an independent set.
Treating model agreement as truth
Symptom: several tools agree, so the result is reported as confirmed pathogenic. Fix: use agreement for prioritization, then obtain RNA or other orthogonal evidence. Correlated models can share the same blind spots.
Ignoring exonic uncertainty
Symptom: a coding-region variant receives conflicting predictions and is dismissed as noise. Fix: treat exonic variants as a higher-uncertainty class, inspect local regulatory context and consider transcript or RNA testing.
A practical decision framework
| Your situation | Recommended starting approach | What to document |
|---|---|---|
| Routine screening of canonical and near-splice variants | Run SpliceAI; add Pangolin when a second model is useful | Assembly, transcript, model release and fixed cutoff |
| Deep-intronic variant discovery | Run SpliceAI and at least one comparator; evaluate lower, validated thresholds | Threshold-selection method, sensitivity target and independent validation set |
| Exonic variant with possible splicing effect | Use multiple predictors and prioritize RNA or functional testing | Exon context, transcript isoforms and discordant outputs |
| Open or reproducible computational stack | Evaluate OpenSpliceAI or CI-SpliceAI alongside original SpliceAI where licensing permits | Framework, training annotation and weight provenance |
| Clinical or commercial deployment | Perform local validation and licensing review before routine use | Intended-use statement, validation cohort, version lock and redistribution terms |
What these tools cannot establish
The cited studies evaluate predictive performance, not whether a score alone proves disease causation. A splice prediction does not establish that the altered transcript occurs in the relevant tissue, that the change is sufficiently large to cause disease, or that the patient’s phenotype is explained by the variant. Clinical interpretation still requires evidence such as population frequency, segregation, phenotype fit, laboratory data and, where feasible, RNA analysis.
Benchmark results also do not guarantee performance on every gene, ancestry group, tissue or sequencing context. Functional assay labels can be incomplete, ClinVar-derived labels can reflect prior interpretation, and small subsets can produce unstable estimates. Treat published numbers as context for choosing and validating a workflow, not as a warranty for a new cohort.
Bottom line for selecting between SpliceAI and alternatives
Use SpliceAI as a strong baseline, not as ground truth. Add Pangolin when you need a serious independent comparator, and use MMSplice, SQUIRLS, OpenSpliceAI or CI-SpliceAI when their modeling assumptions, annotation choices or licensing fit your pipeline. Separate canonical, exonic and deep-intronic analyses; preserve transcript and threshold metadata; and validate important predictions with RNA or other orthogonal evidence. The most defensible result is not the score from the model that wins a generic leaderboard, but a calibrated, reproducible prediction whose task and limitations are explicit.
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