SpliceAI predicts splice-site changes with four gain/loss delta scores and their positions. SpliceAI2 keeps a comparable summary score but adds quantitative splice-junction and transcript-isoform predictions, richer reference-versus-alternate outputs, new threshold guidance, and different local-compute requirements. They are related tools, not interchangeable score scales: choose based on the prediction target, genome build, annotation, compute, access terms, and how your downstream pipeline interprets the result.
For a basic, established score-annotation workflow, the original SpliceAI can remain the practical choice. Consider SpliceAI2 when junction-level or transcript-level effects are central, when its expanded outputs fit your analysis, or when you can support its GPU and data requirements. Treat both as computational evidence rather than a standalone clinical diagnosis.
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SpliceAI and SpliceAI2 at a glance
The original SpliceAI repository documents a deep-learning predictor for splice variants. Its familiar result is a set of four delta scores: acceptor gain, acceptor loss, donor gain, and donor loss, each paired with a delta position showing where the largest predicted change occurs relative to the variant.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsSpliceAI2, documented in the SpliceAI2 repository, retains a 0-to-1 summary score for compatibility with score-oriented analyses but models a wider set of effects. Its outputs include splice-site and splice-junction gains and losses, reference and alternate probabilities, changes, and distances. It can also predict transcript structures directly from sequence and compare predictions made on reference and alternate sequences.
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| Decision point | SpliceAI | SpliceAI2 |
|---|---|---|
| Primary prediction scope | Acceptor and donor site gain/loss effects | Site effects plus junction and transcript-level modeling |
| Core output | Four delta scores and four delta positions | Summary score plus detailed site, junction, reference, alternate, change, and distance fields |
| Summary-score guidance | 0.2 high recall, 0.5 recommended, 0.8 high precision | 0.1 high recall, 0.25 balanced precision/recall, 0.5 high precision |
| Typical local inputs | VCF, reference FASTA, and gene annotation | Variant TSV, reference FASTA, and pretrained model folders |
| Local hardware | Repository workflow does not specify a CUDA GPU requirement | CUDA-capable GPU required for documented local installation |
| Precomputed coverage described by the repository | Not stated in the reviewed README | All possible single-nucleotide variants and population-observed indels in protein-coding genes using GENCODE v48 and GRCh38 |
| Best fit | Stable, score-based annotation and continuity with prior analyses | Junction, transcript, and expanded isoform-aware analyses |
The table describes the documented interfaces, not a claim that one model is universally more accurate. SpliceAI2’s performance numbers below come from Illumina’s own 2026 article and release, and should be read with that provenance.
What the original SpliceAI predicts
Four delta scores and four positions
SpliceAI computes the maximum predicted change among four classes: acceptor gain, acceptor loss, donor gain, and donor loss. Each delta score ranges from 0 to 1. The associated delta position identifies the location of the strongest predicted change relative to the variant, allowing an analyst to distinguish a nearby canonical-site effect from a more distant cryptic-site prediction.
This compact representation is why many annotation pipelines still use SpliceAI. A downstream filter can retain the largest delta score, preserve the four component scores, and use the positions to prioritize variants for transcript review or experimental follow-up.
The Tool Desk
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The documented command-line workflow accepts a VCF, a matching reference FASTA, and gene annotation. The repository describes support for SNVs and simple indels that fall within annotated genes. The FASTA must agree with the alleles in the VCF; a reference mismatch can cause a variant to be left unscored rather than silently analyzed against the wrong sequence.
The distance setting -D controls the sequence window examined around a variant. Its documented default is 50. The repository lists several conditions that can produce an unscored record:
- The variant lies outside the supplied gene annotation.
- The variant is within 5 kb of a chromosome end.
- A deletion is longer than twice the configured
-Ddistance. - The reference allele conflicts with the reference FASTA.
Raw and masked output
The masking option -M changes which classes of gain/loss scores are zeroed in the masked output. The Illumina SpliceAI repository recommends raw files for alternative-splicing analysis and masked files for variant interpretation. Do not mix the two representations when comparing cohorts: a zero in a masked result may reflect the masking policy rather than an absence of a raw model signal.
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What SpliceAI2 adds
A richer variant result
SpliceAI2 reports a spliceai2_summary_score from 0 to 1. The repository defines it as the maximum of the leading donor and acceptor site gain/loss delta scores. That summary makes it possible to retain a single headline value, but it is only one part of the output.
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The detailed result adds gain and loss measurements for splice sites and junctions, reference and alternate probabilities, the calculated changes, and distances. Eight columns correspond one-to-one with the original SpliceAI score and distance fields, which gives existing pipelines a starting point for translation. The additional junction and transcript fields have no equivalent in the original four-score interface, so a conversion that keeps only the summary score discards information.
Transcript-level prediction
SpliceAI2 can predict transcripts directly from sequence. For a variant analysis, the documented approach is to generate predictions separately for the reference and alternate sequences and then examine the difference. This is a different question from asking only whether a donor or acceptor site score rises or falls: it can expose changes in transcript reconstruction and isoform usage.
The broader scope is especially relevant when a variant is near multiple competing splice sites, when a junction is expected to be created or lost, or when the principal biological question concerns which transcript isoform is produced. It also means that output parsing and downstream review are more involved than with the original four delta scores.
Training data and model scope
Illumina’s 2026 article says SpliceAI2 used long-read data to train transcript-level prediction. It describes 330 ENCODE long-read samples and a training dataset that the October 8, 2026 release announcement characterizes as 100 times larger than the prior dataset. The 100-times statement is an Illumina release claim; the reviewed materials do not provide an independent audit of that comparison.
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Can SpliceAI and SpliceAI2 scores be compared directly?
Only with care. The repositories provide a practical field-level bridge, but they do not establish a shared calibration or clinical interpretation scale.
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| Original SpliceAI concept | SpliceAI2 translation aid | What remains different |
|---|---|---|
| Acceptor gain delta score | One of the one-to-one site gain/loss compatibility columns | SpliceAI2 also supplies reference and alternate probabilities and junction-level effects |
| Acceptor loss delta score | One of the one-to-one site gain/loss compatibility columns | The expanded output can describe linked junction and transcript consequences |
| Donor gain delta score | One of the one-to-one site gain/loss compatibility columns | The summary score is derived within the SpliceAI2 output system |
| Donor loss delta score | One of the one-to-one site gain/loss compatibility columns | Additional fields have no original SpliceAI counterpart |
| Four original delta positions | One-to-one distance/position compatibility fields | SpliceAI2 also reports distances for its added site and junction measurements |
| Maximum original delta score | spliceai2_summary_score |
Same 0-to-1 range does not mean identical calibration or biological meaning |
A score of 0.5 from one model is not automatically equivalent to 0.5 from the other. Even the labels attached to recommended thresholds differ. When migrating a pipeline, preserve the model name, version, build, annotation, masking state, component fields, and threshold used for every result.
Thresholds: similar labels, different operating points
The original SpliceAI documentation characterizes 0.2 as a high-recall threshold, 0.5 as its recommended threshold, and 0.8 as a high-precision threshold. SpliceAI2 recommends 0.1 for high recall, 0.25 as a balanced precision/recall point, and 0.5 for high precision.
| Operating objective | SpliceAI suggested delta-score threshold | SpliceAI2 suggested summary-score threshold |
|---|---|---|
| High recall | 0.2 | 0.1 |
| Recommended or balanced operating point | 0.5 | 0.25 |
| High precision | 0.8 | 0.5 |
These are tool-specific operating suggestions, not a universal interpretation rubric. A team changing tools should rerun its validation and review thresholds against the intended objective—candidate discovery, cohort prioritization, rare-disease review, or another task—instead of carrying over the old cutoff because the number looks familiar.
Running SpliceAI: practical workflow and failure checks
- Lock the reference build. Select the reference FASTA that matches the VCF alleles and chromosome naming scheme. Record the build and FASTA checksum in the run metadata.
- Prepare annotation. Supply the gene annotation expected by the SpliceAI workflow. Variants outside that annotation can remain unscored.
- Confirm variant class. The documented scope is SNVs and simple indels within genes. Treat larger or structurally complex events as outside this stated scope unless you have separate validation.
- Set the distance window deliberately. The default
-D 50is not a universal biological setting. A different window changes the sequence context and can affect both the result and the list of variants that are rejected because a deletion exceeds twice the configured distance. - Choose raw or masked output before analysis. Follow the repository’s recommendation: masked files for variant interpretation, raw files for alternative-splicing analysis. Store that choice with the output.
- Audit unscored records. Check for annotation gaps, chromosome-end proximity within 5 kb, oversized deletions, and reference-FASTA conflicts. Fix input problems before interpreting an apparent absence of prediction.
- Keep all four scores and positions. The maximum score is convenient for filtering, but component gain/loss classes and positions are needed to understand the predicted mechanism.
Running SpliceAI2: inputs, local installation, and alternatives
Build the variant TSV
The documented SpliceAI2 variant input is a tab-separated file with chromosome, position, reference allele, alternate allele, and strand. Ensure that positions, allele orientation, and chromosome labels match the reference FASTA. If strand is unknown, the instructions say to evaluate both strands and aggregate the predictions rather than assuming a strand.
Prepare the model and reference files
Local inference uses a reference FASTA and pretrained model folders. The repository states that local installation requires a CUDA-capable GPU. That requirement is material for batch planning: a CPU-only workstation should use an approved precomputed route or another supported service rather than assuming the local package will run unchanged.
Handle compilation and batch size
The documented --no_compile option is intended for small batches or situations in which compilation fails. It can make a small test run easier to diagnose, but it does not remove the need for the model files, reference sequence, or compatible hardware.
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Validate the output before scaling up
- Run a small set of variants with known chromosome, allele, and strand values.
- Check that the summary score is populated and that detailed site and junction fields are present.
- Compare reference and alternate predictions for the same variant; do not interpret an alternate-only score as a transcript consequence without its reference context.
- Confirm that the output fields selected by your pipeline include both the eight compatibility columns and the added junction/transcript measurements you intend to use.
- Only then process the full cohort and archive the model folder, reference FASTA, input TSV, software version, and threshold.
Precomputed results and access terms
The SpliceAI2 repository says its pretrained weights and precomputed effects for protein-coding genes cover all possible single-nucleotide variants and population-observed indels using GENCODE v48 and GRCh38. It describes those resources as available for academic and non-commercial research, with commercial use requiring contact with Illumina. The repository also identifies DRAGEN Annotation as another route to precomputed scores. Verify the current license, data-use terms, genome build, and annotation release before putting either route into a production or commercial workflow.
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When SpliceAI is the better choice
- Your pipeline already consumes four delta scores and positions. Staying with the original tool minimizes schema changes and preserves continuity with historical analyses.
- Your question is site-level prioritization. If you need a ranked list of candidate splice-site disruptions rather than transcript reconstruction, the original output may contain everything required.
- Your supported variants fit the documented scope. SNVs and simple intragenic indels, with a matching FASTA and annotation, are the clearest fit.
- Reproducibility with prior cohorts matters more than expanded output. A model change can alter score distributions and thresholds even when the biological question is unchanged.
Before retaining SpliceAI, check the annotation release, FASTA build, distance setting, masking mode, and the proportion of variants left unscored. A stable workflow is useful only when those conditions are recorded and comparable between runs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When SpliceAI2 is worth adopting
- Junction effects are a primary endpoint. SpliceAI2 reports junction gain/loss measurements that the original four-score interface does not provide.
- Transcript or isoform behavior matters. Direct sequence-based transcript prediction and reference-versus-alternate comparisons can support questions that a single site score cannot answer.
- You can support the documented local environment. Plan for a CUDA-capable GPU, pretrained model folders, a compatible FASTA, and an input TSV with correct strand handling.
- You are prepared to recalibrate downstream filters. Use the SpliceAI2 threshold suggestions as starting points, then evaluate them for your own objective rather than treating them as inherited clinical cutoffs.
- The build and terms fit your project. The precomputed resources described by the repository are tied to GENCODE v48 and GRCh38, and access conditions differ between academic/non-commercial and commercial use.
What Illumina reports about SpliceAI2’s performance
Illumina’s article, Introducing SpliceAI2: The Next Generation of Splicing and Transcript Isoform Prediction, and its October 8, 2026 release, Illumina releases SpliceAI2 to help advance rare disease research, report several comparisons. These are developer-reported results from Illumina’s work; the reviewed materials do not establish independent replication.
Transcript reconstruction
Illumina reports that SpliceAI2 reconstructed the single most common transcript for genes unseen during training 82% of the time when long-read training data were used, compared with 78% when training relied on short-read data alone. The comparison is specifically about transcript reconstruction in the described evaluation, not a general accuracy guarantee for every variant or tissue.
Rare-disease cohort comparison
In an analysis of 7,504 Genomics England participants, Illumina reports 17% more disease-associated variants than other tested splicing models at matched confidence thresholds. The result is tied to that cohort, model set, and confidence-matching method; it should not be restated as a universal 17% sensitivity improvement.
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The article reports 33% more disease-relevant splice variants at a 2X confidence interval and 66% more at a 4X confidence interval compared with legacy SpliceAI. Those figures use the article’s confidence-interval framing. They are not interchangeable with ordinary percentage accuracy, sensitivity, or clinical-validity claims.
Tissue-specific modeling
Illumina describes modeling that included 147 splicing regulators, 48 human tissues, and nearly 15 million differential-usage measurements. The same article says the model was less successful at predicting how a particular variant’s effect changes between tissues, and that the underlying tissue splicing program was a stronger predictor. Tissue-aware output therefore should not be interpreted as a guarantee of patient-specific tissue effects.
Publication status and evidence limits
Illumina announced SpliceAI2 on October 8, 2026. The repository links a manuscript, and the release describes the work as a preprint. The reviewed material does not establish peer-reviewed publication status. Keep that status, the model version, and the evaluation provenance visible whenever these performance numbers are cited.
Clinical and research interpretation
A splice prediction is computational evidence. It can help prioritize variants, suggest a mechanism, or guide laboratory follow-up, but neither SpliceAI nor SpliceAI2 by itself establishes that a patient has a disease or that a variant is clinically causal. Interpretation still requires the relevant transcript, genome build, phenotype, inheritance context, population evidence, laboratory data, and whatever clinical review process governs your setting.
Do not convert a threshold label into a diagnosis. A high-recall cutoff is useful when missing candidates is costly, but it will include more false positives. A high-precision cutoff narrows the list but can miss real effects. The proper choice depends on the analysis objective and must be documented with the model and output type.
Migration and comparison checklist
- Define the question. Decide whether you need site-level ranking, junction gain/loss, transcript reconstruction, tissue-aware analysis, or compatibility with a historical score table.
- Freeze reference context. Record genome build, FASTA, annotation release, transcript set, chromosome naming, and strand conventions.
- Map fields explicitly. Keep the eight SpliceAI-compatible score/position categories when translating, and add separate columns for SpliceAI2-only junction and transcript outputs.
- Re-evaluate thresholds. Start with each tool’s documented operating points, then test recall and precision for your own labeled set or review objective.
- Check hardware and access. Confirm GPU capacity for local SpliceAI2 inference or select a permitted precomputed route. Review academic, non-commercial, or commercial terms before deployment.
- Run concordance checks. Compare both tools on the same alleles, FASTA, annotation context, and strand handling. Investigate large differences instead of assuming one score is wrong.
- Preserve provenance. Archive model version, input files, masking state, distance setting, thresholds, and software configuration with every result.
Common failure modes and fixes
Scores are missing for a subset of variants
For SpliceAI, inspect annotation coverage, chromosome-end proximity, deletion length relative to 2 × -D, and reference-FASTA allele matches. For SpliceAI2, verify TSV formatting, chromosome labels, allele orientation, strand, and the presence of the model and reference files.
The two tools disagree strongly
First rule out build, annotation, strand, and masking differences. Then compare component site scores and positions rather than only the headline values. Different thresholds and model scopes can produce legitimate disagreement; a shared 0-to-1 range does not remove that distinction.
SpliceAI2 will not run locally
Confirm that the machine has a CUDA-capable GPU and that the pretrained model folders are available. For a small batch or a compilation failure, try the documented --no_compile option. If the environment remains incompatible, use an approved precomputed resource or a supported platform route instead of treating a failed local run as a biological negative.
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Check whether the file was generated with -M. Masking can zero selected gain/loss classes. Use the repository’s raw-versus-masked recommendation for the task and do not compare masked output directly with raw output from another run.
Bottom line for choosing between them
Use SpliceAI when a proven four-score annotation workflow answers your question and continuity, simple inputs, or existing validation are priorities. Move to SpliceAI2 when junction and transcript-level predictions materially improve the analysis and you can meet its GPU, build, data, and access requirements. For a fair comparison, run both on harmonized inputs, preserve their complete outputs, apply tool-specific thresholds, and report Illumina’s performance figures as vendor-reported results rather than independent clinical validation.
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