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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesUse SpliceAI as a ranking signal, not a diagnosis. Verify the variant’s allele, genome build, gene and transcript first; then inspect all four delta scores and their positions, choose a cutoff that matches your recall or precision goal, and record whether the values are raw or masked. A score raises or lowers a variant’s priority for follow-up, but clinical classification still requires gene–disease evidence, transcript relevance, other variant data and, when available, RNA or other functional results.
What a SpliceAI score actually measures
SpliceAI predicts how much a variant may alter pre-mRNA splicing. Its principal output is a delta score from 0 to 1. The overall delta score is not an independent fifth prediction: it is the maximum of four channel-specific values:
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| Field | Meaning | Question it helps answer |
|---|---|---|
| DS_AG | Acceptor-gain delta score | Could the variant create or strengthen an acceptor? |
| DS_AL | Acceptor-loss delta score | Could the variant weaken or remove an acceptor? |
| DS_DG | Donor-gain delta score | Could the variant create or strengthen a donor? |
| DS_DL | Donor-loss delta score | Could the variant weaken or remove a donor? |
| Delta score | max(DS_AG, DS_AL, DS_DG, DS_DL) | What is the strongest predicted splice change among the four channels? |
The score is a model prediction of splice alteration, not a probability that the variant causes disease. A high value can result from a splice change that is biologically real but irrelevant to the disease mechanism, the wrong transcript or a gene with no established disease relationship. A low value does not rule out every splice effect, particularly when the variant or transcript falls outside the model’s coverage or when experimental evidence is more informative than the prediction.
Read the four scores and the four positions together
SpliceAI output also includes a delta position for each channel: DP_AG, DP_AL, DP_DG and DP_DL. The position associated with the largest channel indicates where the predicted change occurs relative to the variant. Positive positions are downstream; negative positions are upstream. The units and exact interpretation should be read from the output specification for the tool and version used, but the direction is useful immediately: it tells you whether the predicted splice event is near the variant or displaced along the sequence.
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Use this reading order
- Confirm the record. Check the alternate allele, gene symbol and the reference allele against the variant you intended to score. A correct-looking number attached to a different allele is not useful evidence.
- Find the maximum channel. Identify whether DS_AG, DS_AL, DS_DG or DS_DL drives the overall delta score. Record the channel rather than retaining only the maximum.
- Read its matching position. Pair the driving score with DP_AG, DP_AL, DP_DG or DP_DL from the same channel. A gain and a loss can represent very different biological stories even if their numbers are similar.
- Inspect the remaining channels. A second high channel may indicate competing predicted effects or a complex local sequence context. Do not discard those values merely because they are not the maximum.
- Check transcript and annotation context. Confirm which gene and transcript annotation produced the record and whether that transcript is relevant to the disease or phenotype under review.
An illustrative record
Suppose a record has DS_AG 0.18, DS_AL 0.06, DS_DG 0.63 and DS_DL 0.11. The overall delta score is 0.63 because donor gain is the maximum, so DP_DG is the position to inspect first. The other channels remain part of the interpretation: they show that the model’s strongest signal is a donor gain rather than an acceptor change or donor loss. These numbers are an illustration of the reading method, not a benchmark or a clinical result.
Choosing a cutoff for the job
Illumina’s SpliceAI guidance describes three practical operating points. They are prioritization conventions that trade sensitivity against specificity; none is a universal pathogenicity threshold.
| Delta-score cutoff | How the guidance characterizes it | When it fits | What it does not establish |
|---|---|---|---|
| 0.2 | High recall | Broad screening where missing a candidate is more costly than reviewing extra variants | It does not make every passing variant splice-altering or pathogenic |
| 0.5 | Recommended cutoff | General-purpose prioritization when a balanced operating point is wanted | It is not a clinical classification rule |
| 0.8 | High precision | A short review queue where fewer, stronger predictions are preferred | A value below 0.8 is not proof of no effect |
Use the threshold to control queue size, not to label variants. A discovery screen may retain records at or above 0.2 for later triage. A diagnostic laboratory with limited review capacity may rank values at or above 0.5, then give the highest scores and strongest contextual evidence priority. A focused experimental project may start at 0.8 to select a small set of candidates, while still allowing biologically important lower-scoring variants into the design when other evidence warrants them.
Why a single number cannot be universal
Threshold performance depends on the task, variant spectrum, transcript set, assay design and the population of variants being examined. ClinGen’s Sequence Variant Interpretation Splicing Subgroup evaluated SpliceAI alongside in-vitro splicing data within the ACMG/AMP framework. In that 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 analyzed evidence and should not be generalized automatically to every gene, disease, variant class, assay or clinical setting.
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When publishing or handing off a prioritization list, state the cutoff, whether it was inclusive (for example, “at least 0.5”), the score file or software version, the genome assembly, the annotation and whether scores were masked. Another analyst should be able to reconstruct why a variant entered the queue.
Raw and masked scores answer different questions
Illumina distributes raw and masked predictions. For variant interpretation, its guidance recommends masked scores. For alternative-splicing analysis, it recommends raw scores.
| Score configuration | What it retains | Recommended use | Interpretive consequence |
|---|---|---|---|
| Raw | Predictions for strengthening annotated splice sites and weakening unannotated sites, in addition to other predicted changes | Alternative-splicing analysis and exploratory transcript work | May include signals that are usually less relevant to pathogenic-variant interpretation |
| Masked | Those selected changes are set to zero | Prioritizing variants for interpretation | Emphasizes changes considered more relevant to pathogenicity assessment by the masking rule |
Masking is not a quality score and does not make a prediction clinically valid. It is a deliberate filter. Record the configuration beside every score because a raw 0.7 and a masked 0.7 are not interchangeable statements about the same model output.
Prepare the variant before obtaining a score
Most apparent disagreements arise before anyone interprets the number. Normalize the variant representation and make the reference context explicit.
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Checklist for the input record
- Genome assembly: use the intended GRCh37 or GRCh38 context consistently from the VCF through annotation and interpretation.
- Reference and alternate alleles: verify that the reference base in the VCF matches the supplied reference FASTA. Normalize indels according to your pipeline’s convention before comparing records.
- Gene and transcript: establish which annotation set and transcript model generated the score. A score tied to a different transcript may not answer the clinical question.
- Variant class: check whether the method supports the variant. The documented command-line workflow scores SNVs and simple indels within annotated genes; other classes need explicit support from the selected service.
- Allele orientation and multiallelic sites: separate alternate alleles when required and ensure the reported allele is the one being prioritized.
Three ways to obtain SpliceAI annotations
| Route | What it does | Coverage and caveats | Best fit |
|---|---|---|---|
| Illumina command-line tool | Scores a VCF with a reference FASTA and gene annotation file, then appends the allele, gene, four DS values and four DP values to the VCF INFO field | Documented annotations are available for GRCh37 and GRCh38; documented scoring covers SNVs and simple indels in annotated genes | Controlled, repeatable local pipelines and batch processing |
| Ensembl VEP SpliceAI plugin | Retrieves precomputed annotations during Variant Effect Predictor runs | Ensembl files cover SNVs overlapping MANE transcripts on GRCh37 and GRCh38; Ensembl’s score set does not provide indel annotations and its documentation directs users to Illumina files for indel scores | VEP-centered annotation workflows that already use MANE transcript consequences |
| Broad Institute SpliceAI Lookup | Provides a web lookup service | The service documentation describes modified versions of SpliceAI and Pangolin, support for multi-nucleotide variants and customizations; it no longer uses Illumina’s precomputed tables, which were based on an older GENCODE version | Interactive exploration when the service’s version, sources and settings are acceptable |
Local scoring workflow
- Choose the assembly and matching gene annotation (GRCh37 or GRCh38) before running the tool.
- Validate the VCF reference alleles against the reference FASTA and resolve normalization or contig-name mismatches.
- Supply the VCF, reference FASTA and annotation file to the documented command-line workflow.
- Choose raw or masked output according to the purpose: masked for variant interpretation, raw for alternative-splicing analysis.
- Inspect the appended INFO fields and retain the complete DS and DP set, not just the overall maximum.
- Save the tool version, annotation release, assembly, masking choice and input-file checksums with the results.
VEP plugin workflow
- Run the Ensembl Variant Effect Predictor with the SpliceAI plugin and the score file that matches the input assembly.
- For GRCh37 or GRCh38, use the corresponding Ensembl precomputed file and confirm that the variant overlaps the covered MANE transcript set.
- Do not assume an absent indel score means a zero prediction; Ensembl’s documented score set does not provide indel annotations.
- For indels requiring SpliceAI scores, obtain the appropriate Illumina files or run the supported local workflow instead.
- Record the VEP release, plugin version, score-file name and transcript set in the output metadata.
Web lookup workflow
- Enter the normalized variant in the lookup service using the correct genome build.
- Read the service’s displayed source, model version and annotation details before comparing its value with another tool.
- Check whether the result comes from modified SpliceAI or another model offered by the service, such as Pangolin.
- Export or record all four DS and DP values, the allele, gene and settings; a screenshot of only the headline score is not reproducible evidence.
Why a score may be missing
An absent annotation is not a zero score and is not evidence that the variant has no splice effect. The official command-line documentation identifies several reasons a variant may be unscored:
- It 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.
- Its reference allele disagrees with the supplied reference FASTA.
- Its class is outside the documented SNV and simple-indel support.
Troubleshooting sequence
- Confirm the assembly and contig naming in the VCF, FASTA and annotation.
- Compare the VCF reference allele with the FASTA at the exact position.
- Left-normalize or otherwise standardize the variant, then verify that the alternate allele still represents the intended event.
- Check whether the locus is in an annotated gene and whether it is near a chromosome terminus.
- Review deletion length and the tool’s configured distance parameter.
- Try a method whose documented variant coverage matches the class, while preserving the original representation for auditability.
From prioritization to interpretation
After ranking variants, evaluate the biological and clinical context separately. A strong SpliceAI result is one piece of evidence, not a verdict.
Context that should accompany a high-priority prediction
- Gene–disease relationship: establish that the gene is credibly associated with the phenotype and determine whether loss of function, gain of function or another mechanism is established.
- Transcript relevance: verify that the affected transcript is expressed in the disease-relevant tissue and is an accepted clinical transcript when one is specified.
- Variant consequence: determine whether the predicted donor or acceptor change could produce exon skipping, cryptic-site use, intron retention or another event that fits the gene’s mechanism.
- Independent variant evidence: consider population frequency, segregation, allelic observations, phenotype match and other validated computational or functional data under the applicable framework.
- Experimental RNA evidence: prioritize minigene, patient RNA, long-read or other appropriately designed assays when the result can resolve the predicted event.
ClinGen SVI recommendations describe how predicted and observed splice effects can be incorporated into ACMG/AMP evidence assessment. Apply the relevant framework rather than translating a delta score directly into a pathogenic or benign label.
Common interpretation mistakes
Treating the overall score as a probability of disease
The maximum score summarizes predicted splice alteration. It does not estimate penetrance, disease risk or pathogenicity. Keep the model output and the classification conclusion in separate fields in databases and reports.
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Reporting only the maximum
“SpliceAI 0.62” omits whether the signal is an acceptor gain, acceptor loss, donor gain or donor loss and hides the predicted location. Preserve the driving channel and its delta position, plus the other channel values.
Mixing builds or annotation sources
A GRCh37 precomputed value, a GRCh38 local score and a result based on a different transcript set are not automatically comparable. Re-run or harmonize the inputs before drawing a difference-based conclusion.
Comparing raw and masked values
A change in masking can alter the reported maximum. Label the configuration and compare like with like.
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The Broad lookup service documents modified models and changed score sources, while Ensembl and Illumina provide distinct precomputed or local workflows. Service name, model version, annotation, assembly and settings belong in the evidence record.
Calling an unscored variant benign
Coverage limits, allele mismatches and unsupported variant classes can all produce missing output. Investigate the input and method before assigning biological meaning to absence.
Build a reproducible prioritization record
For each candidate, retain a compact record that lets another analyst reproduce the ranking:
- Variant representation, reference and alternate alleles, and genome build.
- Gene, transcript or annotation source, including the release used.
- Tool or lookup service, version and score-file identifier.
- Raw or masked configuration.
- DS_AG, DS_AL, DS_DG, DS_DL and the corresponding DP fields.
- Overall delta score and the cutoff applied, including whether the comparison was “at least” or “greater than.”
- Reason for inclusion, such as broad screening at 0.2 or high-priority review at 0.8.
- Any missing-score explanation or input warning.
- Downstream evidence and the classification framework used.
This metadata matters when a score is regenerated after a transcript update, a genome-build conversion or a change in masking policy. It also prevents a prioritization convention from being mistaken for a laboratory-validated classification rule.
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Licensing and use in commercial settings
Illumina states that its trained models are provided under CC BY-NC 4.0 for academic and non-commercial use and that other use requires a commercial license. Organizations using SpliceAI in a commercial workflow should review the repository’s licensing terms and contact route before deployment. Licensing status is separate from the scientific interpretation of a score and does not change the need to document model and annotation versions.
Quick Recap
A practical decision framework
- Screen broadly: start around the 0.2 operating point when recall is the priority, retain lower-scoring candidates when phenotype or gene mechanism makes them important, and plan a triage or assay stage.
- Balance review effort: use 0.5 as the documented general-purpose operating point, then rank within the passing set by channel, position and biological context.
- Shortlist for focused validation: begin around 0.8 when resources permit only a few experiments, but do not discard lower values that have compelling independent evidence.
- Classify clinically: stop treating the score as a queue rule and apply the relevant ACMG/AMP and ClinGen guidance with transcript, disease-mechanism and functional evidence.
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