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SpliceAI is the original splice-variant predictor; SpliceAI2 keeps a comparable summary score but expands the task to quantitative splice-site use, splice junctions, and transcript isoforms. The two tools do not produce identical outputs, use different threshold recommendations, or have identical setup requirements. Keep SpliceAI when compatibility with an established four-score workflow is the priority. Consider SpliceAI2 when junction-level or transcript-level predictions, its broader model scope, or its newer documented evaluations are important—and verify genome build, GPU availability, licensing, and downstream interpretation first.
Neither score is a diagnosis. Treat both as computational evidence alongside transcript data, population evidence, functional assays, phenotype, and expert clinical review.
SpliceAI and SpliceAI2 at a glance
| Question | SpliceAI | SpliceAI2 |
|---|---|---|
| Primary prediction target | Gain or loss of donor and acceptor splice sites around a variant | Quantitative donor and acceptor effects, splice-junction gain/loss, and transcript isoform behavior |
| Main variant output | Four delta scores and four delta positions | A summary score plus detailed site and junction measurements, including reference/alternate probabilities, changes, and distances |
| Summary score range | 0–1 delta score: the maximum of four gain/loss scores | 0–1 spliceai2_summary_score: the maximum leading donor/acceptor site gain/loss delta |
| Suggested operating points | 0.2 high recall, 0.5 recommended, 0.8 high precision | 0.1 high recall, 0.25 balanced precision/recall, 0.5 high precision |
| Local input | VCF, reference FASTA, and gene annotation | Variant TSV, reference FASTA, and pretrained model folders |
| Local hardware | Use the original repository’s documented runtime requirements | CUDA-capable GPU required for local installation; --no_compile is available for small batches or compilation failures |
| Precomputed resources | Repository and community workflows provide the original scores | Pretrained weights and precomputed effects for GRCh38 protein-coding genes using GENCODE v48, subject to the repository’s access terms |
Read the operational documentation before pinning a production pipeline: the original SpliceAI repository and the SpliceAI2 repository.
What SpliceAI originally predicts
SpliceAI is a deep-learning tool for identifying splice-altering variants. For each annotated variant, its documented result is organized around four quantities: acceptor gain, acceptor loss, donor gain, and donor loss. Each delta score is accompanied by a delta position indicating where the largest predicted change occurs relative to the variant. The overall delta score is the largest of the four values and lies between 0 and 1.
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This compact representation made SpliceAI useful in annotation pipelines: a caller can retain one summary value for filtering while preserving the four component scores and positions for review. It also means that a SpliceAI result is fundamentally a site-change prediction, not a complete reconstruction of every transcript produced after the variant.
Masked and raw output
The original command-line workflow can produce masked and raw files. Masking changes which classes of gain or loss scores are set to zero in the masked representation. The repository recommends masked files for variant interpretation and raw files for alternative-splicing analysis: “We recommend using raw files for alternative splicing analysis and masked files for variant interpretation.” Keep that distinction in the pipeline metadata; otherwise two analysts can appear to disagree while using different output modes.
Supported inputs and unscored variants
The documented workflow takes a VCF, a reference FASTA, and gene annotation. It is designed for SNVs and simple indels within genes. A variant can remain unscored when it falls outside the annotation, lies within 5 kb of a chromosome end, has a deletion longer than twice the configured -D distance, or conflicts with the reference FASTA. The default -D value is 50. A missing score in these cases is a coverage or input-condition issue, not evidence that the variant has no splicing effect.
Build and annotation consistency are essential. The chromosome names, reference alleles, coordinates, FASTA, and gene model must describe the same assembly. Record the annotation release and masking mode with every result so that a later reanalysis can reproduce the original call.
What SpliceAI2 changes
From site scores to sites, junctions, and transcripts
SpliceAI2 is described by Illumina as a quantitative model of splice sites, splice junctions, and transcripts. Its variant output retains donor and acceptor gain/loss information but adds junction gain/loss measurements and reference-versus-alternate scores, changes, and distances. This allows an analyst to ask not only whether a nearby splice site is strengthened or weakened, but also how junction usage and transcript structure are expected to change.
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The model can also predict transcripts directly from a sequence input. For a variant analysis, the documented approach is to generate predictions for the reference and alternate sequences separately and compare them. That is a different modeling scope from interpreting only four site-level deltas.
How the summary score relates to original SpliceAI
SpliceAI2 exposes spliceai2_summary_score on a 0–1 scale. The repository defines it as the maximum of the leading donor/acceptor site gain/loss delta scores. Eight output columns correspond one-to-one with the original SpliceAI score and distance fields, which gives existing workflows a translation aid. The correspondence does not make every SpliceAI2 field identical to an original SpliceAI field: junction and transcript information is additional, and the underlying model and recommended thresholds differ.
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| Original SpliceAI concept | SpliceAI2 relationship | What remains different |
|---|---|---|
| Acceptor or donor gain/loss delta | One-to-one score mapping is provided for the corresponding site fields | SpliceAI2 also reports reference/alternate values and changes |
| Delta position | Corresponding distance fields are retained | Additional distances can describe junction-level effects |
| Overall delta score | spliceai2_summary_score supplies a comparable 0–1 summary |
Threshold labels are not interchangeable between versions |
| No junction or transcript field | SpliceAI2 adds junction gain/loss and transcript prediction | There is no original SpliceAI counterpart for every new field |
Thresholds: similar numbers, different operating points
SpliceAI documentation characterizes a delta score of 0.2 as a high-recall point, 0.5 as the recommended point, and 0.8 as a high-precision point. SpliceAI2 recommends 0.1, 0.25, and 0.5 for those corresponding goals. These are tool-specific operating points, not a common interpretation scale.
| Interpretation goal | SpliceAI suggested threshold | SpliceAI2 suggested threshold |
|---|---|---|
| High recall | 0.2 | 0.1 |
| Balanced/recommended use | 0.5 | 0.25 |
| High precision | 0.8 | 0.5 |
A SpliceAI score of 0.5 therefore should not be described as equivalent to a SpliceAI2 score of 0.5 merely because the numeric values match. If you compare cohorts, predefine the model, score field, threshold, transcript set, genome build, and confidence objective. Report the complete distribution or the actual operating point rather than silently converting one model’s labels into the other’s.
Inputs, installation, and access requirements
SpliceAI workflow
- Prepare a VCF whose reference alleles match the selected reference FASTA.
- Use the gene annotation expected by the original repository and keep its release with the run record.
- Set the
-Ddistance deliberately; the documented default is 50, and long deletions can be left unscored when their length exceeds twice that configured distance. - Choose masked output for ordinary variant interpretation or raw output for alternative-splicing analysis, following the repository guidance.
- Investigate missing scores caused by annotation boundaries, chromosome ends, deletion length, or reference mismatches before treating them as biological negatives.
Exact command-line syntax and version-specific dependencies are maintained in the official repository; copy them from the version you install rather than mixing instructions from different releases.
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SpliceAI2 workflow
- Create the documented variant TSV with chromosome, position, reference allele, alternate allele, and strand.
- Supply a reference FASTA and the pretrained model folders required by the repository.
- For an unknown strand, evaluate both strands and aggregate predictions as instructed in the SpliceAI2 documentation.
- Use a CUDA-capable GPU for local installation. If you are scoring a small batch or compilation fails, the documented
--no_compileoption can bypass compilation. - Capture the summary score together with site, junction, reference/alternate, change, and distance fields; downstream code should not discard the new columns unless it intentionally maintains an original-SpliceAI-compatible view.
The repository states that pretrained weights and precomputed effects for protein-coding genes based on GENCODE v48 and GRCh38 cover all possible single-nucleotide variants and population-observed indels. Those resources are offered for academic and non-commercial research; commercial use requires contacting Illumina. The same documentation identifies DRAGEN Annotation as another route to precomputed scores. Verify current terms, model versions, and assembly compatibility before production deployment.
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An established four-score pipeline
Keep SpliceAI when your database schema, quality-control rules, dashboards, or published analyses depend on the four delta scores and four positions. Replacing the model can change the score distribution and the set of variants crossing a filter, even if you retain a similarly named summary field.
A supported variant scope is all you need
If the task is prioritizing SNVs and simple intragenic indels for likely donor or acceptor changes, the original output may answer the question without adding junction or transcript data. This can reduce migration work and preserve comparability with historical cohorts.
Reproducibility with prior results
For longitudinal studies, consistency can be more valuable than a newer version. Freeze the original model, reference FASTA, annotation, -D setting, and masking behavior when the goal is to reproduce earlier findings. Run SpliceAI2 as a separate analysis rather than overwriting the historical score.
When SpliceAI2 is worth evaluating
Junction-level questions
Use SpliceAI2 when a site score alone cannot express the expected effect on a splice junction. Its gain/loss and reference/alternate junction measurements provide additional evidence for exon skipping, cryptic junction use, or altered connectivity between splice sites, subject to the model’s documented limits.
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Transcript-isoform analysis
SpliceAI2 is the relevant option when transcript reconstruction or direct transcript prediction is part of the analysis objective. Generate predictions for reference and alternate sequences and compare the resulting transcript behavior rather than reducing the output immediately to one scalar.
New model evaluations are relevant
SpliceAI2 may be appropriate for a new study that can support a CUDA-capable GPU and wants to assess the model’s expanded training and evaluation claims. Treat those claims as evidence to test against your own variant set, not as a reason to change a clinical threshold automatically.
What Illumina reports about SpliceAI2 performance
Illumina’s 2026 article, “Introducing SpliceAI2: The Next Generation of Splicing and Transcript Isoform Prediction,” reports the following results. They are developer-reported findings from the model’s research article, not independent replication established here.
- Transcript reconstruction: SpliceAI2 reconstructed the single most common transcript for genes unseen during training 82% of the time using long-read training data, compared with 78% when trained on short-read data alone.
- Rare-disease cohort: In an analysis of 7,504 Genomics England participants, the article reports 17% more disease-associated variants than other tested splicing models at matched confidence thresholds.
- Confidence-interval comparisons: Against legacy SpliceAI, the article reports 33% more disease-relevant splice variants at a 2X confidence interval and 66% more at a 4X confidence interval. These figures describe the article’s confidence-interval framing; they are not general accuracy percentages.
- Long-read training: The article says 330 ENCODE long-read samples were used for transcript-level training.
- Tissue modeling: Its analysis covered 147 splicing regulators, 48 human tissues, and nearly 15 million differential-usage measurements. The article also says variant-specific changes between tissues were harder to predict than the underlying tissue splicing program, which was the stronger predictor.
- Training-set size: An Illumina release announcement describes a training dataset 100 times larger than the prior one. That is a release claim, not an independently audited dataset comparison.
The release, published October 8, 2026, calls the work a preprint. The reviewed materials do not establish peer-reviewed publication status. Keep the date, attribution, and study context attached whenever these numbers appear in a report.
Can you compare SpliceAI and SpliceAI2 scores directly?
You can compare them operationally, but not as if they were interchangeable measurements. Start with the one-to-one score and distance mappings listed by the SpliceAI2 repository, then perform a task-specific comparison.
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- Align the inputs. Use the same assembly, reference allele, transcript or gene annotation policy, variant representation, and strand handling.
- Align the field. Compare the original four site deltas with the corresponding SpliceAI2 site fields before examining junction or transcript outputs.
- Keep model-specific thresholds. Report the threshold recommended for the chosen model and objective; do not relabel a 0.25 SpliceAI2 score as a “0.5-equivalent” SpliceAI result.
- Inspect discordant variants. Review delta positions, reference/alternate probabilities, junction changes, transcript predictions, and known transcript evidence.
- Measure agreement on your dataset. Summarize counts above each model’s prespecified threshold and investigate cases where one model crosses a filter and the other does not.
A translation layer can preserve legacy columns for downstream software while adding SpliceAI2-specific fields beside them. Do not overwrite the original score, because doing so destroys the ability to explain why a historical classification changed.
Clinical and research interpretation
A high score indicates that a model predicts a substantial splice-related change under its training and scoring definition. It does not prove pathogenicity, establish the affected tissue, or show that a patient has a disease. A low score does not rule out an effect outside the model’s supported sequence context, transcript set, or training distribution.
For clinical review, combine predictions with RNA evidence when available, segregation and population data, phenotype, conservation, assay results, and the applicable laboratory or professional framework. Document whether the value came from raw or masked SpliceAI output, which SpliceAI2 field was used, the genome build, annotation release, model version, and threshold. Predictions should support—not replace—expert review.
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- Define whether the goal is site prioritization, junction quantification, transcript reconstruction, or historical reproducibility.
- Inventory every downstream consumer of the original four scores and positions.
- Confirm assembly, chromosome naming, FASTA, annotation release, and strand conventions.
- Decide whether local GPU inference is feasible or whether precomputed resources are acceptable under your use terms.
- Run both tools on a representative validation set without changing clinical filters mid-analysis.
- Compare score distributions, threshold counts, discordant variants, and missing-score reasons.
- Store SpliceAI and SpliceAI2 outputs as separate, versioned fields.
- Write the model name, threshold rationale, masking mode, and evidence limitations into the interpretation report.
- Recheck the SpliceAI2 repository for model, precomputed-resource, and licensing changes before a production release.
Bottom line
SpliceAI is still the practical choice for a stable, four-delta-score annotation workflow. SpliceAI2 is the broader model for studies that need quantitative junction and transcript predictions and can meet its GPU, input, build, and access requirements. Use the repository’s mappings to aid migration, but keep thresholds and conclusions model-specific. Illumina’s reported 2026 performance gains are promising yet vendor-reported and described in a preprint; validate them on the variants and clinical questions that matter to your laboratory.
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