Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteiTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
SpliceAI is one of the strongest general-purpose predictors of splice-altering variants, but it is not a universal winner. Pangolin is its closest evidence-backed comparator, and the two tools repeatedly rank near the top in independent tests. The better choice depends on whether you are identifying splice sites, predicting variant effects, triaging deep-intronic variants, or building a clinical workflow.
Published comparisons also show why scores cannot be compared casually: variant class, transcript annotation, genome assembly, assay design, labels, and score thresholds materially change the result. Exonic variants are harder than intronic variants, and a threshold that works for canonical splice-site variants can miss deep-intronic candidates.
What is being compared?
Splice variant prediction is not one task. A model may be evaluated on at least four different questions:
Recommended Free Tools
- Splice-site identification: Does the model locate donor or acceptor sites in a sequence?
- Variant-effect prediction: Does a specific DNA change alter splicing relative to the reference sequence?
- Variant-class triage: How well does it find canonical, near-splice, exonic, or deep-intronic changes?
- Deployment performance: Can the exact model, annotation, score cutoff, and license be used reproducibly in a research or clinical pipeline?
A splice-site metric such as area under the precision-recall curve (AUPRC) is not interchangeable with variant-level balanced accuracy from a functional assay. Treating those numbers as if they were one league table produces a misleading ranking.
#1 Best Overall
Evidence at a glance
| Tool | What the published evidence shows | Best interpreted as | Important limitation |
|---|---|---|---|
| SpliceAI | Among the strongest performers in a 2023 massively parallel splicing assay benchmark; highest balanced accuracy in the 2026 deep-intronic comparison. | A strong baseline for splice-altering variant triage, including difficult intronic cases when calibrated for that class. | Its score depends on the model release, reference assembly, transcript annotation, masking distance, and cutoff. Standard cutoffs were too high for the 2026 deep-intronic benchmark. |
| Pangolin | Among the most frequent top performers in the 2023 assay benchmark. Its 2022 study reported average top-1 accuracy of 79% and AUPRC of 0.85 in its own splice-site evaluation. | A serious alternative for splice-site and variant-effect analyses. | The 79% and 0.85 figures come from a different task and dataset than SpliceAI results reported elsewhere, so they are not a universal head-to-head score. |
| MMSplice | Included as a complementary predictor in the 2023 assay benchmark. | Part of an ensemble or comparative analysis where annotation-dependent models are acceptable. | Outputs are not interchangeable with sequence-only or differently annotated models; exon-level agreement was weaker across tools. |
| SQUIRLS | Also included in the 2023 benchmark as an annotation-dependent alternative. | A complementary signal for ranking candidate splice variants. | A single benchmark does not establish that it is universally inferior or superior. |
| OpenSpliceAI | A PyTorch reimplementation evaluated in the 2026 comparison; similar to original SpliceAI on larger canonical-heavy benchmarks but divergent on deep-intronic data. | A reproducibility or deployment option when its exact annotation and release are validated. | It is not the same model artifact as the original. The 2026 study used RefSeq MANE v1.3 transcripts. |
| CI-SpliceAI | A retraining/reimplementation evaluated with curated GENCODE HAVANA splice-site annotations; close to original SpliceAI on some canonical-heavy sets but different on deep-intronic variants. | A separately validated model rather than a drop-in assumption of equivalence. | Its annotation and training choices differ from original SpliceAI, so score agreement is not guaranteed. |
What the major benchmarks found
The 2023 massively parallel splicing assay benchmark
Riepe and colleagues compared eight predictors against experimental outcomes for 3,616 variants in five genes using massively parallel splicing assays. Because the labels came from measured splicing behavior rather than only existing clinical classifications, the study tested whether computational scores tracked functional effects in that experiment.
Pangolin and SpliceAI were the tools that most often delivered the best performance in the benchmark. Pangolin ranked best across seven dataset or variant-class combinations, while SpliceAI ranked best across three. That result supports both as leading choices in the tested setting; it does not establish that Pangolin always wins or that SpliceAI is always second.
Agreement between predictors was uneven. The median pairwise Pearson correlation across tools was 0.58, with a very broad range from 0.04 to 0.97. Pangolin and SpliceAI were unusually aligned, with a correlation of 0.97 in that analysis. Within exonic variants, median pairwise correlation fell to 0.43, indicating that models often disagree more when a change lies inside an exon rather than near a canonical splice junction.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →The same benchmark found lower prediction-to-assay concordance for exonic variants than for intronic variants. An exonic score should therefore be treated as a prioritization signal, not as a substitute for RNA evidence or another functional assay.
The 2022 Pangolin evaluation
The Pangolin paper reported average top-1 accuracy of 79% and AUPRC of 0.85 for its splice-site prediction task. SpliceAI scored 75% and 0.77 in that paper’s comparison. Those numbers are useful evidence that Pangolin is competitive, but they describe splice-site prediction under that study’s data and evaluation protocol. They cannot be placed directly beside the 3,616-variant functional benchmark as though all studies measured the same outcome.
Deep-intronic variants change the answer
Deep-intronic variants are far from the canonical donor and acceptor positions that many pipelines prioritize. They can create or strengthen cryptic splice sites, but their effects are difficult to label and are often underrepresented in training and test data.
Fortier, Rudy, and Scherer’s comparison, published in PLOS ONE on May 13, 2026, evaluated original SpliceAI, OpenSpliceAI, and CI-SpliceAI across several datasets, including functional assays, curated deep-intronic cases, and ClinVar-derived sets. On the curated deep-intronic benchmark, original SpliceAI reached a balanced accuracy of 0.940 at the study’s optimized threshold, outperforming the two reimplementations in that setting.
Crashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minutePC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The threshold finding is as important as the ranking. The optimal threshold was approximately 0.04 for original SpliceAI and 0.01 for both OpenSpliceAI and CI-SpliceAI. Those values are much lower than the commonly cited SpliceAI cutoffs of 0.2 for high recall, 0.5 for a balance between recall and precision, and 0.8 for high precision. Applying 0.5 automatically to deep-intronic variants can therefore discard candidates that the benchmark considered useful.
The authors caution that thresholds optimized separately on each benchmark can overestimate real-world performance. A threshold selected after examining the test labels is not equivalent to a fixed cutoff chosen before deployment. If you change the threshold, report how it was selected and validate it on an independent set.
The 2026 paper also reports a smaller Riepe functional-validation subset containing only 213 variants from two genes. That limited scope, along with differences among the functional, curated, and ClinVar-derived datasets, means the deep-intronic result should guide calibration rather than serve as a universal performance guarantee.
How the tools differ in practice
SpliceAI
SpliceAI reports delta scores representing predicted changes in acceptor and donor site probabilities between reference and alternate sequence. A useful report should preserve the score type, the model release, the genome assembly, the transcript or annotation source, the masking distance, and the cutoff used. A bare number without those settings is difficult to reproduce.
SpliceAI is a strong baseline because it performs well across several independent evaluations, including the 2023 functional benchmark and the 2026 deep-intronic comparison. It remains a prediction model, not ground truth: a high delta score indicates a predicted splice change, not confirmed pathogenicity.
Rank #3
Pangolin
Pangolin is the closest comparator in the available evidence. Its published evaluation focused on splice-site prediction and reported stronger average metrics than SpliceAI in that particular study. The 2023 massively parallel assay benchmark also found Pangolin and SpliceAI to be the most frequent top performers among the tested tools.
Use Pangolin as an independent second signal, especially when a result will be escalated for functional review. Agreement between Pangolin and SpliceAI can increase confidence that a sequence change deserves attention, but agreement still does not prove a clinical effect.
MMSplice and SQUIRLS
MMSplice and SQUIRLS broaden a comparison beyond two deep-learning systems. The 2023 benchmark treated both as annotation-dependent predictors. Their outputs can add information when your pipeline already maintains gene models and transcript-level features, but they should not be assumed to share the same score scale or calibration as SpliceAI or Pangolin.
Free tools Windows power users keep installed
One-click scans. No signup required.
Because exon-level predictions showed weaker inter-tool agreement in the benchmark, discordant MMSplice or SQUIRLS results are especially important to document rather than silently averaging.
OpenSpliceAI and CI-SpliceAI
OpenSpliceAI and CI-SpliceAI are reimplementations or retrainings, not identical copies of the original SpliceAI artifact. In the 2026 comparison, OpenSpliceAI used the PyTorch framework with RefSeq MANE v1.3 transcripts. CI-SpliceAI used curated GENCODE HAVANA splice-site annotations. Those choices affect which transcript models are scored and how training labels are defined.
The two alternatives tracked original SpliceAI more closely on larger benchmarks dominated by canonical splice-site variants than on deep-intronic data. If you substitute one for another, rerun validation; do not assume that a similar name means numerically equivalent scores.
Rank #4
Compare variant classes separately
| Variant class | What the evidence suggests | How to analyze it |
|---|---|---|
| Canonical splice-site variants | Several tools perform relatively consistently, and reimplementations can resemble original SpliceAI on canonical-heavy datasets. | Record exact transcript annotation and use a predeclared cutoff appropriate to the intended recall or precision. |
| Near-splice intronic variants | SpliceAI and Pangolin are strong candidates for initial ranking. | Compare model outputs and check whether the affected transcript is biologically relevant. |
| Exonic variants | Inter-tool correlation and prediction-to-assay concordance were lower than for intronic variants in the 2023 benchmark. | Use multiple predictors as prioritization signals and seek RNA or other functional evidence before drawing a firm conclusion. |
| Deep-intronic variants | Original SpliceAI led the 2026 curated benchmark, but useful thresholds were around 0.04 or lower rather than the usual 0.5. | Calibrate on a representative deep-intronic validation set and report the threshold-selection procedure. |
Annotation and transcript choice can change the result
Gene-model dependence is an operational issue, not a footnote. The 2023 benchmark notes that annotation files and transcript selection affect tools that require gene models. Two tools can receive the same genomic variant and produce different outputs simply because they score different transcripts or use different splice-site definitions.
Quick wins for a faster PC:
Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →The 2026 comparison illustrates the scale of this difference: original SpliceAI used GENCODE v24, OpenSpliceAI used RefSeq MANE v1.3, and CI-SpliceAI used curated GENCODE HAVANA annotations. These are not interchangeable references. A comparison should state the assembly, annotation release, transcript-selection rule, and whether the alternate allele was normalized to the same coordinate system.
For a clinical workflow, define a transcript policy before scoring variants. For example, you might require a MANE transcript where available, retain additional disease-relevant transcripts, and store the annotation release with every result. The exact policy is less important than applying it consistently and recording exceptions.
A reproducible comparison workflow
- Define the decision. Decide whether the output is for splice-site localization, variant-effect ranking, deep-intronic triage, or a clinical interpretation support step. Write down the target metric before looking at results.
- Separate variant classes. Label canonical, near-splice, exonic, and deep-intronic variants separately. Do not let a large canonical set hide poor performance on the class that matters to your project.
- Freeze the reference context. Record genome assembly, annotation source and release, transcript-selection rules, sequence orientation, and variant normalization.
- Run at least two complementary predictors. A SpliceAI-plus-Pangolin comparison is an evidence-backed starting point. Add MMSplice or SQUIRLS when annotation-dependent features are relevant, or evaluate OpenSpliceAI and CI-SpliceAI when you need separately maintained model artifacts.
- Preserve raw outputs. Store each model’s component scores, delta score or equivalent, transcript identifier, model version, and runtime settings. Do not retain only a final pass/fail label.
- Predeclare thresholds. Choose a cutoff for the intended operating point, or derive it on a training set and lock it before testing. For deep-intronic work, explicitly test lower thresholds instead of importing the usual 0.5 rule.
- Validate on independent labels. Use functional assay results when available, and keep ClinVar-derived labels distinct from experimentally measured outcomes. Report class balance and the number of genes and variants.
- Investigate discordance. Check transcript choice, annotation, variant class, and threshold before concluding that one model failed. Discordant exonic predictions are expected often enough to deserve their own review branch.
- Escalate biologically plausible candidates. Use RNA testing, minigene or massively parallel assays, segregation, clinical review, and other evidence as appropriate. A computational score alone does not establish pathogenicity.
- Document deployment terms. Verify the license for the exact weights, precomputed scores, code, and annotation data before redistributing results or using them commercially.
How to interpret thresholds and metrics
Thresholds express an operating preference. Lowering a cutoff generally admits more candidates and can improve recall while reducing precision; raising it does the opposite. The 2026 deep-intronic results show why one global threshold can be inappropriate across variant classes and model artifacts.
Balanced accuracy averages sensitivity and specificity, which can be informative when classes are imbalanced but does not tell you how many false positives will appear in a large screening population. AUPRC is sensitive to class prevalence and is useful for ranking rare positives, but an AUPRC from one dataset cannot be transplanted to another without checking prevalence and label definitions.
Top-1 accuracy asks whether the highest-ranked prediction is correct under a particular task. It does not mean that every variant above a score cutoff is clinically actionable. Always state the metric, dataset, class definition, and threshold together.
Best Value
Licensing and deployment checks
The 2026 comparison characterizes original SpliceAI model weights and precomputed scores as limited to academic and non-commercial use. Treat that as a release-specific warning, not a permanent statement about every future package. Check the license attached to the exact weights and data you plan to run.
The same paper does not independently establish current licenses, redistribution rights, runtime requirements, or package availability for every alternative. Before production use, confirm whether you may host the model, cache scores, expose an application programming interface, redistribute outputs, and use the system for commercial or clinical services.
Reproducibility also requires more than installing a package. Pin the model version, framework, annotation files, transcript database, reference assembly, and threshold configuration. If an open reimplementation produces a different result, those artifacts are the first things to compare.
Common mistakes and their fixes
- Declaring a universal winner: Replace the ranking with a task-specific statement tied to a named benchmark.
- Mixing metrics from different papers: Keep splice-site AUPRC, top-1 accuracy, assay concordance, and balanced accuracy in separate tables.
- Using 0.5 for every variant: Evaluate class-specific calibration, especially for deep-intronic candidates.
- Ignoring annotation: Record the transcript and annotation release next to every score.
- Calling a reimplementation identical: Treat OpenSpliceAI and CI-SpliceAI as separately validated models.
- Overinterpreting exonic scores: Require stronger functional or clinical evidence because agreement was lower in the benchmark.
- Training and testing on the same cases: Lock thresholds before evaluating the held-out set.
- Equating prediction with diagnosis: Integrate RNA testing, laboratory validation, and clinical interpretation.
- Overlooking conflicts or study scope: The 2026 authors disclosed employment by Golden Helix, which develops VarSeq containing legacy ensemble tools included in their comparison. Consider that disclosure alongside the paper’s dataset and threshold limitations.
Which tool should you choose?
Choose SpliceAI first when
- You need a strong general baseline for splice-altering variant prioritization.
- Your evaluation includes deep-intronic variants and you are prepared to calibrate a lower threshold.
- You can fix the assembly, annotation, transcript policy, and model release before analysis.
Add Pangolin when
- You want an independent comparator with strong published splice-site results.
- You are testing whether a candidate is consistently prioritized across models.
- Your benchmark resembles the splice-site or functional-assay settings in which Pangolin performed well.
Add MMSplice or SQUIRLS when
- Your pipeline can provide stable gene-model annotations and transcript features.
- You want complementary evidence rather than another score treated as a replacement for SpliceAI.
Evaluate OpenSpliceAI or CI-SpliceAI when
- You need a separately implemented model for reproducibility, engineering, or comparative research.
- You can validate the specific annotation and training choices used by that implementation.
- You are willing to test deep-intronic performance independently rather than assuming canonical-site equivalence.
Clinical interpretation: what a score can and cannot do
These predictors estimate the likelihood or magnitude of a splicing change. The cited studies evaluate predictive performance; they do not show that one score confirms pathogenicity, replaces RNA testing, or supersedes laboratory validation.
A defensible clinical workflow uses the score to prioritize review, then integrates the affected transcript, disease mechanism, population data, segregation, phenotype, RNA evidence, and other laboratory results. When no functional evidence exists, report the computational result with its model, annotation, threshold, and uncertainty rather than presenting it as a diagnosis.
Bottom line
SpliceAI and Pangolin are the most defensible starting pair for a modern comparison, but neither is a universal winner. SpliceAI led the cited deep-intronic benchmark at a study-optimized threshold, while Pangolin matched or exceeded it in several splice-site evaluations. Exonic predictions are less concordant, reimplementations diverge when annotations or variant classes change, and thresholds must be calibrated to the task. The reliable choice is therefore a documented, multi-model workflow tied to the exact variant class, transcript annotation, validation set, and deployment terms you intend to use.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
