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Use SpliceAI as a ranking signal, not a diagnosis. Confirm the variant representation, genome build, gene and transcript first; then inspect all four delta scores (acceptor gain, acceptor loss, donor gain and donor loss), the matching delta position, and whether the result is raw or masked. For broad candidate screening, the SpliceAI authors describe 0.2 as a high-recall operating point. Their recommended cutoff is 0.5, while 0.8 favors high precision. None of these values is a universal pathogenicity threshold. A clinical interpretation still requires transcript and gene-disease context, other variant evidence and, when possible, RNA or other functional data.
What a SpliceAI score actually measures
SpliceAI is a machine-learning predictor of how a DNA variant may alter pre-mRNA splicing. Its output is a set of delta scores that describe the largest predicted change in splice-site activity near the variant. The overall delta score is the largest of four values and ranges from 0 to 1:
max(DS_AG, DS_AL, DS_DG, DS_DL)
The Illumina SpliceAI README describes this delta score as interpretable as the probability that a variant is splice-altering. That wording does not make it a probability of disease, penetrance or pathogenicity. A high score means the model predicts a substantial splice change under its model, annotation and settings; it does not establish that the change occurs in the relevant tissue or that it causes the patient’s phenotype.
SpliceAI output also includes the alternate allele and gene symbol, four delta scores and four delta positions. The position attached to the largest score indicates where the predicted change occurs relative to the variant: a positive value is downstream and a negative value is upstream.
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Read the four delta scores, not just the headline number
DS_AG: acceptor gain
DS_AG is the predicted gain of an acceptor site. A high value can indicate that the variant may create or strengthen an acceptor at a new location.
DS_AL: acceptor loss
DS_AL is the predicted loss of an acceptor site. This can represent weakening of an existing acceptor or disruption of a site the model recognizes as functionally important.
DS_DG: donor gain
DS_DG is the predicted gain of a donor site. The model may be identifying a new or stronger 5′ splice-site signal.
DS_DL: donor loss
DS_DL is the predicted loss of a donor site. A high value suggests weakening or disruption of a donor signal.
Two variants can have the same overall score while implying different biological mechanisms. One may primarily lose a donor, another may gain a cryptic acceptor, and a third may show both gain and loss signals. Record which channel supplies the maximum before deciding what follow-up evidence is appropriate.
Use delta position to locate the predicted event
After identifying the channel with the largest score, read its delta position. A negative position places the predicted change upstream of the variant; a positive position places it downstream. The distance helps distinguish a change at the variant’s immediate splice context from a farther cryptic-site prediction. It is a model coordinate, not a statement that the event has been observed experimentally.
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A practical workflow for prioritizing variants
- Validate the variant record. Check the reference and alternate alleles, chromosome and position, genome assembly (for example, GRCh37 or GRCh38), and whether the variant has been normalized. Confirm that the gene and transcript are the ones relevant to the interpretation. A score from a different assembly or allele representation is not safely comparable.
- Obtain scores with a documented method. Note whether you ran the official command-line tool, used an Ensembl Variant Effect Predictor (VEP) SpliceAI plugin annotation, or queried a lookup service. Save the tool or service version, annotation release, assembly, distance setting and raw or masked status.
- Inspect all four channels. Read DS_AG, DS_AL, DS_DG and DS_DL separately. Identify the maximum and capture the corresponding DP value. Do not discard the non-maximum channels; a competing gain and loss can change the biological hypothesis.
- Choose a threshold that matches the queue you are managing. Use a lower threshold when missing a candidate is more costly than reviewing extra candidates. Use a higher threshold when laboratory or expert review capacity is limited. State the threshold in the analysis record.
- Check transcript and gene-disease relevance. Ask whether the affected transcript is expressed in the disease-relevant tissue, whether it is a clinically established transcript, and whether the gene-disease relationship is sufficiently strong for the phenotype under consideration.
- Integrate independent evidence. Review the variant’s population frequency, inheritance and phenotype fit, predicted consequence, conservation or other computational evidence, prior clinical observations and any RNA or minigene assay. Apply the ACMG/AMP framework and the ClinGen SVI splicing recommendations rather than converting a score directly into a classification.
- Plan confirmation. A high-priority prediction should lead to an appropriate functional or clinical follow-up question: for example, whether an exon is skipped, an intronic cryptic site is used, or normal transcript abundance is reduced.
Which cutoff should you use?
The official SpliceAI author guidance presents three operating points. They are prioritization conventions that trade sensitivity against specificity, not universal clinical boundaries.
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|---|---|---|---|
| 0.2 | High recall | Broad screening when you want to retain more possible splice-altering variants for later triage or testing | It does not establish that every retained variant alters splicing or causes disease |
| 0.5 | Recommended cutoff in the Illumina repository guidance | General-purpose prioritization when a balanced operating point is appropriate | It is not an ACMG/AMP pathogenicity rule |
| 0.8 | High precision | Shortlists where review or experimental capacity is very limited and stronger predictions should be ranked first | A score below 0.8 is not proof of a benign or non-splicing effect |
Use a threshold together with the direction of the analysis. A screening pipeline can retain variants at or above 0.2, rank them by score and channel, and send a manageable subset to testing. A small diagnostic review queue may start at 0.5 or 0.8, while still examining lower-scoring variants when phenotype, inheritance or other evidence makes them plausible.
What ClinGen’s analysis adds
The ClinGen SVI Splicing Subgroup evaluated SpliceAI thresholds alongside in-vitro splicing assay data and the ACMG/AMP framework. In the examined dataset, a score of at least 0.5 showed strong evidence for an impact on cell survival, while a score below 0.1 showed evidence for no impact. Those findings are calibration results for the studied data and framework. They should not be generalized automatically to every gene, disease, variant class, assay, tissue or clinical laboratory.
When reporting a threshold, identify whether it came from the general SpliceAI operating guidance or from a study-specific calibration. Also report the score configuration and evidence used to move from computational prioritization to a clinical code such as a splicing-related ACMG/AMP criterion.
Raw versus masked scores
SpliceAI distributions commonly provide raw and masked results. Illumina recommends masked scores for variant interpretation and raw scores for alternative-splicing analysis.
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Masked files set to zero predictions for strengthening annotated splice sites and weakening unannotated sites. The repository describes these events as typically less relevant to pathogenic variant interpretation. Raw files retain them, which is useful when the scientific question is broader than clinical variant assessment—for example, cataloguing possible alternative-splicing events.
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The choice can change whether a variant appears above your queue threshold. Record the setting beside every score, along with assembly and annotation source. Never compare a masked value from one workflow with a raw value from another as though they were equivalent measurements.
Ways to obtain SpliceAI scores
Your choice of scoring route affects coverage, annotation and reproducibility. The following methods documented by the source projects are complementary, not interchangeable.
| Route | How it works | Documented coverage or characteristic | Important record-keeping |
|---|---|---|---|
| Official SpliceAI command-line tool | Scores a VCF with a reference FASTA and gene annotation file | Documents GRCh37 and GRCh38 options; scores SNVs and simple indels within annotated genes; appends allele, gene, four DS values and four DP values to the VCF INFO field | Assembly, FASTA, annotation, distance parameter, software version and masking option |
| Ensembl VEP SpliceAI plugin | Retrieves precomputed annotations during VEP processing | Ensembl files cover SNVs overlapping MANE transcripts and support GRCh37 and GRCh38; the documented Ensembl score set does not provide indel annotations | VEP and plugin versions, score-file release, assembly, transcript set and allele representation |
| Broad Institute SpliceAI Lookup | Web lookup service for variant annotations | Current service documentation describes modified versions of SpliceAI and Pangolin, support for multi-nucleotide variants and service-specific customizations; it no longer uses Illumina’s older precomputed tables based on an older GENCODE version | Service version, model, source database, annotation, genome build and settings; do not assume its values match Illumina or Ensembl values |
Local scoring with the command-line tool
The local route is useful when you need a reproducible, auditable run over a cohort or a custom annotation. Prepare three matching inputs:
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- A VCF whose alleles are expressed on the selected assembly.
- A reference FASTA for that same assembly, indexed as required by the tool.
- The corresponding gene annotation file, using the documented GRCh37 or GRCh38 option.
Run the command according to the release’s documented syntax, then inspect the appended INFO fields. Confirm that the output allele and gene match the intended record before importing scores into a prioritization table. A pipeline should preserve the original VCF, the exact command and the generated output so another analyst can reproduce the result.
Using VEP precomputed annotations
The VEP plugin can be convenient for SNVs that overlap the MANE transcripts represented in Ensembl’s score files. It is not a drop-in replacement for local scoring of every variant class: the plugin documentation notes that Ensembl does not provide indel annotations in that score set and points users to Illumina files for indel scores. Match the input assembly to the score file and verify the transcript context before comparing a plugin result with a local result.
Interpreting lookup-service results
A web lookup is useful for an individual variant, but the service’s model and annotation choices can differ from the original precomputed tables. For a result that will support a report or publication, save the returned record and document the service date or version, model, genome build and annotation. A numerical difference between services is not automatically an error; it may reflect a different model, transcript set, GENCODE release or variant representation.
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Why a score may be missing
An absent SpliceAI annotation is not a zero and is not evidence that the variant has no splice effect. The official repository lists several reasons a variant may be unscored:
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- The variant lies outside genes in the supplied annotation.
- It is within 5 kb of a chromosome end.
- It is a deletion longer than twice the configured distance parameter.
- The reference allele disagrees with the supplied FASTA.
- The variant class is outside the documented command-line scope of SNVs and simple indels in annotated genes.
Troubleshoot in this order:
- Confirm chromosome naming and genome build.
- Compare the VCF reference allele with the FASTA at the exact position.
- Normalize left alignment and representation, especially for indels.
- Check that the gene annotation contains the relevant gene and transcript.
- Review the variant length and the tool’s configured distance.
- If the variant is a multi-nucleotide change or another unsupported class, use a method whose documented coverage includes it and record that method explicitly.
Only after these checks should you conclude that the chosen resource does not cover the variant.
How to turn a high score into a useful follow-up question
Predicted donor or acceptor loss
Ask whether the affected exon boundary is skipped, whether a neighboring exon is retained or omitted, and whether the resulting transcript is expected to trigger nonsense-mediated decay. RNA from a relevant tissue, when available and technically suitable, can test these hypotheses.
Predicted donor or acceptor gain
Look for use of a cryptic site, pseudoexon inclusion or an altered exon boundary. The delta position helps locate where to inspect the transcript model, but experimental confirmation is needed to establish which isoform is produced.
Multiple strong channels
If gain and loss channels are both high, do not collapse them into one mechanism. Define separate hypotheses and choose assays that can distinguish them. A single overall score hides this distinction.
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SpliceAI contributes computational evidence about a possible splice alteration. Clinical classification asks a broader question: does the variant cause disease in this gene, for this phenotype and inheritance model? The answer requires evidence outside the score.
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- Gene-disease validity: establish that the gene is credibly associated with the condition being evaluated.
- Transcript relevance: confirm that the transcript and exon are clinically meaningful and expressed in a relevant context.
- Variant and phenotype fit: assess inheritance, phenotype specificity, allele frequency and other molecular consequences.
- Independent observations: consider previously reported cases and segregation where reliable.
- Functional evidence: weigh RNA sequencing, RT-PCR, minigene assays or other validated experiments, including assay limitations.
- Framework rules: apply the ACMG/AMP framework and the ClinGen SVI splicing recommendations rather than assigning a pathogenicity category from a numerical cutoff alone.
A low score can miss a real effect, particularly when the relevant transcript, tissue or variant class is poorly represented. A high score can predict an event that is not used in vivo or is not disease-relevant. The score changes the order in which candidates deserve attention; it does not replace the evidentiary framework.
Reproducibility checklist for reports and pipelines
- Variant identifier, normalized alleles and genome assembly.
- Gene and transcript source, including whether a MANE transcript or another transcript was used.
- Tool or lookup service name and version.
- Model or precomputed score release and annotation version.
- Raw or masked score status.
- All four DS values and all four DP values, not only the maximum.
- Threshold, rationale and whether the queue was optimized for recall or precision.
- Variant-class coverage and any exclusions.
- Independent evidence reviewed after computational ranking.
- Any functional assay, its tissue or cell system, and the interpretation limits.
This record prevents a common failure: treating a score as a portable property of a variant when it actually depends on representation, assembly, annotation, model and settings.
Common interpretation mistakes
Calling a variant pathogenic because it exceeds 0.5
The 0.5 value is a recommended prioritization cutoff in the official guidance and a threshold examined in ClinGen’s study context. It is not a standalone pathogenicity criterion.
Calling a variant benign because it is below 0.2
Lower scores reduce the model’s predicted splice alteration under that configuration, but they do not exclude effects that the model, transcript set or assay cannot represent. ClinGen’s cited analysis specifically examined a below-0.1 range; even that result is tied to its studied data and framework.
Reporting only the maximum delta score
Without the driving channel and delta position, a reader cannot tell whether the model predicts donor loss, acceptor gain or another mechanism, or where the event is located.
Mixing assemblies or score sources
GRCh37 and GRCh38 coordinates are not interchangeable, and values from different model or annotation releases may differ. Keep each score attached to its source metadata.
Treating no annotation as zero
Coverage and input constraints can produce a missing result. Validate the FASTA, alleles, annotation and supported variant class before drawing a biological conclusion.
Ignoring masking
Raw and masked outputs answer different questions. A threshold applied to one should not be silently compared with the other.
A compact decision rule
For each candidate, ask four questions in sequence: Is the record correct? Which splice mechanism and position drive the score? Does the threshold fit the purpose of this queue? What independent evidence can confirm or refute the predicted event? If the first answer is uncertain, fix the representation before interpreting the score. If the last answer is absent, treat the result as a research or triage lead rather than a clinical conclusion.
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