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SpliceAI delta scores predict how a variant may alter pre-mRNA splicing; they do not diagnose disease. Each result contains four directional scores—acceptor gain (DS_AG), acceptor loss (DS_AL), donor gain (DS_DG), and donor loss (DS_DL)—plus a delta position (DP) for each score. The headline delta score is the largest of the four. To interpret an output correctly, read the score suffix, pair it with the matching DP field, record the reference assembly and annotation settings, and treat threshold values as model- or evidence-calibration choices rather than universal pathogenicity percentages.
What a SpliceAI delta score represents
SpliceAI is a machine-learning model that predicts whether a DNA variant could change RNA splice-site usage. Its output summarizes four possible changes:
| Field | Meaning | Question it addresses |
|---|---|---|
| DS_AG | Delta score for acceptor gain | Could the variant create or strengthen use of an acceptor site? |
| DS_AL | Delta score for acceptor loss | Could the variant weaken or eliminate use of an acceptor site? |
| DS_DG | Delta score for donor gain | Could the variant create or strengthen use of a donor site? |
| DS_DL | Delta score for donor loss | Could the variant weaken or eliminate use of a donor site? |
Every DS value ranges from 0 to 1. The Illumina SpliceAI documentation defines the variant delta score as max(DS_AG, DS_AL, DS_DG, DS_DL). The project says this maximum can be interpreted as the probability that the variant is splice-altering. That is model language: it is not a probability of cancer, an inherited disorder, or clinical pathogenicity.
A high score means the model sees a strong signal for one predicted splice change. It does not by itself establish which mature transcript will be produced, whether an exon will be skipped, whether a pseudoexon will be included, or whether the person will have disease. Those conclusions require transcript, gene-mechanism, laboratory, population, segregation, and other clinical evidence.
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How to read the four DS fields
Separate acceptor from donor
An acceptor is generally the 3′ splice site of an intron, while a donor is generally the 5′ splice site. “Gain” and “loss” describe opposite predicted changes in use. Never quote a number without its suffix: “a SpliceAI score of 0.7” hides whether the model predicted acceptor gain, donor loss, or another event.
Find the largest directional score
Compare all four DS values. The largest one is the headline delta score, but the other values can still explain competing model predictions. For example, if DS_AG is 0.62, DS_AL is 0.03, DS_DG is 0.18, and DS_DL is 0.04, the headline score is 0.62 and the leading prediction is acceptor gain.
Do not add the four scores
The headline value is a maximum, not a sum or average. A variant with DS_AG 0.45 and DS_DG 0.44 has a headline score of 0.45; the two values are separate directional predictions, not 0.89 combined evidence.
Read small values and near-ties carefully
A score near zero means the model found little predicted change for that particular event; it does not prove that normal splicing is unaffected. Conversely, two similar high values may represent competing possible changes rather than two independent probabilities. Preserve all four fields, especially when the leading values are close, and avoid claiming a unique mechanism from the maximum alone.
How to interpret DP (delta position)
Each score has a matching position field: DP_AG, DP_AL, DP_DG, or DP_DL. Pair the DP with the DS that has the same suffix. If DS_DG is the largest score, inspect DP_DG—not DP_AG or a generic position field.
In the convention documented by the Illumina repository, a positive DP is downstream of the variant and a negative DP is upstream. DP is an offset relative to the variant representation, not an absolute chromosome coordinate. A value of −2 means the predicted affected splice position is two bases upstream under that convention; it does not mean genomic position −2.
Worked example
The repository illustrates an RYR1 output with DS_DG 0.91 and DP_DG −2. Read this as a strong predicted donor-gain signal whose affected donor position lies two bases upstream of the represented variant, using the repository’s sign convention. It is an interpretation of the software output, not experimental proof of the resulting transcript.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchVariants represented differently—especially insertions, deletions, or unnormalized alleles—can have a different relative offset. Preserve the exact VCF representation when recording a DP value. The same genomic event may therefore need normalization before two DP values can be compared meaningfully.
Why database displays need checking
A downstream database may rename, transform, or display SpliceAI fields under its own conventions. The NCI TP53 Database manual, for example, documents a context-specific DP sign convention. Do not transfer that convention to a raw SpliceAI result without checking the database’s documentation and the original annotation source.
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What does a score such as 0.5 mean?
A score of 0.5 is a substantial model signal, but its practical meaning depends on the analysis settings and the question being asked. The original SpliceAI project characterizes three operating points:
| Delta-score cutoff | Project description | Appropriate interpretation |
|---|---|---|
| 0.2 | High recall | Useful when the priority is to capture more possible splice-altering variants, accepting more candidates for follow-up. |
| 0.5 | Recommended cutoff in the project documentation | A commonly used prioritization threshold in the project’s evaluation, not a 50% disease risk. |
| 0.8 | High precision | More selective; fewer predictions pass, but the retained set is intended to be enriched for strong model signals. |
These labels describe recall–precision trade-offs in model evaluation. They are not benign, uncertain, or pathogenic categories. In particular, a score of 0.5 does not mean a 50% chance that the variant is pathogenic.
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Thresholds are also operational choices. A screening workflow may deliberately use 0.2 to avoid missing candidates, whereas a narrowly defined review queue may use 0.8 to reduce follow-up volume. Neither choice changes the underlying DS value, and neither substitutes for validating the relevant transcript or mechanism.
Why clinical thresholds are not universal
ClinGen’s Splicing Subgroup has evaluated how computational splice predictions can be calibrated for use as evidence in variant classification. Its response document, updated March 22, 2024, gives examples that use different score cutoffs and distance settings from the original project guidance.
Example calibration using raw scores
One analysis described in the ClinGen response used raw SpliceAI delta scores with a maximum distance of ±4,999 nucleotides. In that context, scores at or below 0.1 were evaluated for a benign-supporting use (BP4), while scores at or above 0.2 were evaluated for a pathogenic-supporting use (PP3). Those proposed evidence applications belong to that calibration framework; they are not a universal conversion table for every SpliceAI run.
A separate precomputed-score analysis
The same document describes another analysis using precomputed hg19 scores, a ±50-nucleotide window, and cutoffs below 0.1 and at least 0.5. It also notes that precomputed scores based on GENCODE v24 can differ from lookup scores based on GENCODE v43. A value cannot be interpreted responsibly without knowing which analysis, genome build, annotation version, and distance window produced it.
What PP3 and BP4 do—and do not—mean
PP3 and BP4 are evidence codes in a clinical variant-classification framework. They indicate how a calibrated computational result may contribute to an overall classification. They do not turn SpliceAI into a standalone diagnostic test. The gene’s disease mechanism, the relevant transcript, population data, segregation, phenotype, and other evidence still matter.
Keep provenance with every score
Two annotations for the same variant can differ without either being erroneous. Record at least the following with a result:
- Reference genome assembly, such as GRCh37 or GRCh38.
- Variant normalization and the exact allele representation.
- Gene and transcript annotation source and version.
- Whether the score came from a raw or masked file.
- Maximum distance used around the variant.
- SpliceAI software/model and score source or release.
- The four DS fields and their corresponding DP fields, not only the maximum.
The maintainer’s command-line documentation lists built-in GRCh37 and GRCh38 options using GENCODE V24 canonical annotations. ClinGen’s comparison of score sources demonstrates why the annotation version should not be omitted.
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How to compare two annotations
When two services report different values, compare them in a fixed order: first assembly and normalization, then transcript or gene annotation, then raw versus masked mode, then maximum distance, and finally the score-file or software release. Also verify that both services report the same allele and the same DS field. A difference in one of these inputs can explain a changed maximum or DP without implying a biological disagreement.
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Inputs and limits of a SpliceAI run
The documented command-line workflow requires three principal inputs:
- A VCF file containing the variants and their reference and alternate alleles.
- A reference-genome FASTA matching the assembly and the VCF representation.
- A gene annotation, either a supplied annotation or one of the documented built-in options.
The documented default maximum distance is 50 bases. The tool scores SNVs and the simple indel forms supported by its implementation when they fall within annotated genes. A result therefore depends on both the biological variant and the software’s input scope.
When no score is produced
The repository lists several situations in which a variant may not be scored:
- The variant is outside a gene in the selected annotation.
- It lies within 5 kilobases of a chromosome end.
- A deletion is longer than twice the configured maximum distance.
- The VCF reference allele does not match the supplied FASTA.
- The variant is outside the SNV or supported simple-indel forms.
A missing annotation is not equivalent to four zero scores. It may indicate that the variant was outside the model’s supported input or that the reference, assembly, representation, or annotation did not match. Troubleshoot the input and annotation before assigning a low-impact interpretation.
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The -M setting distinguishes two kinds of precomputed output:
| File type | Documented behavior | Typical use described by the project |
|---|---|---|
Raw (-M 0) |
Includes predictions that can strengthen annotated splice sites and weaken unannotated sites. | Alternative-splicing analysis. |
Masked (-M 1) |
Sets those masked scores to zero. | Variant interpretation. |
Raw and masked values are not interchangeable. If two pipelines disagree, first check this setting before treating the difference as a biological contradiction. Include the mode in the report or variant database record. A zero created by masking has a different operational meaning from a genuinely small model prediction, so the file type should remain visible during review.
A practical workflow for interpreting an output
- Confirm the variant identity. Check chromosome, position, reference allele, alternate allele, assembly, and normalization. A mismatch can invalidate the lookup or shift the relative DP interpretation.
- Confirm the annotation context. Record the gene/transcript annotation and its version. Check whether the output uses the documented GENCODE V24 canonical files or another annotation.
- Read all four DS values. Label each as acceptor/donor and gain/loss. Do not begin with the maximum alone.
- Identify the maximum. Compute or verify
max(DS_AG, DS_AL, DS_DG, DS_DL). Preserve ties or near-ties rather than implying a single mechanism when the values are similar. - Pair the leading DS with its DP. Use the same suffix and apply the stated sign convention. Describe DP as an offset, not a genomic coordinate.
- Check the score provenance. Note raw versus masked mode, maximum distance, reference build, annotation release, and score source.
- Apply a threshold only for its stated purpose. Use the project’s 0.2, 0.5, or 0.8 descriptions for model prioritization, or a ClinGen-calibrated threshold only when your evidence framework and settings match that calibration.
- Seek biological and clinical confirmation. Examine the relevant transcript, gene-disease mechanism, RNA or minigene data when available, population frequency, phenotype, segregation, and other classification evidence.
Common interpretation errors
Calling a high score pathogenic
SpliceAI predicts splice impact, not disease causality. A high value can support additional investigation or a calibrated computational evidence code, but it cannot replace the complete clinical classification process.
Calling 0.2, 0.5, or 0.8 universal boundaries
The project and ClinGen use thresholds for different purposes and configurations. Mixing them produces a false impression that one number has the same meaning in every pipeline.
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Ignoring the suffix
DS_AG and DS_AL are not interchangeable, and neither is equivalent to DS_DG or DS_DL. Always state the predicted event type.
Reading DP as a coordinate
DP is relative to the represented variant. A negative number indicates upstream in the Illumina repository convention; it is not a negative chromosome position.
Inferring the exact transcript consequence
The four summary scores do not establish the precise exon-skipping, intron-retention, cryptic-site, or pseudoexon outcome. Inspect transcript models and experimental evidence before making that claim.
Treating an absent result as zero
Missing output can reflect unsupported variant type, annotation boundaries, chromosome-end restrictions, deletion length, or a reference mismatch. Resolve the cause before assigning biological meaning.
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Before comparing two values, align the assembly and normalization, annotation version, raw/masked mode, maximum distance, and score source. A difference caused by GENCODE V24 versus V43 is not evidence that the variant’s biology changed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How SpliceAI fits into clinical evidence
ClinGen’s 2023 Splicing Subgroup recommendations and its 2024 feedback response address how predicted and observed splicing effects can be incorporated into variant-evidence frameworks. Their calibration examples include datasets of 1,008 BRCA1/BRCA2 variants, 659 mismatch-repair-gene variants, 284 NF1 variants, and 1,070 POU1F1 variants. These are analyzed calibration datasets, not prevalence estimates and not universal performance figures.
For a patient-level interpretation, ask whether the transcript is expressed in the relevant tissue, whether loss or gain of splicing fits the gene’s known disease mechanism, and whether independent evidence supports the predicted effect. A computational prediction is strongest when it agrees with appropriately designed RNA or functional testing and the rest of the clinical record.
Licensing and operational record-keeping
The project repository states that its source code uses a PolyForm Strict License and that trained models are provided for academic and noncommercial use; commercial use requires a commercial license, with Illumina identified as the licensing contact. This is separate from the scientific interpretation of a score, but laboratories should verify that their intended deployment complies with those terms.
For reproducibility, archive the input VCF, FASTA and annotation identifiers, SpliceAI version or score file, masking setting, maximum distance, and the complete DS/DP record. Without these details, a later reviewer may be unable to explain why a score differs between analyses.
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Quick examples of responsible wording
For a result with DS_AG 0.62 and DP_AG 14, write that the output shows a predicted acceptor-gain signal of 0.62 at an offset 14 bases downstream under the stated convention. For DS_DL 0.03, write that the model gives little predicted donor-loss signal; do not write that donor loss is impossible. For a missing result, report that no score was returned and document the input or annotation condition, rather than reporting “SpliceAI = 0.”
This wording keeps the prediction, location, and uncertainty together. It also prevents a database user from mistaking a directional model output for an observed RNA result or a final clinical classification.
Frequently Asked Questions
What does a SpliceAI score of 0.5 mean?
It is a substantial predicted splice-altering signal and the original project describes 0.5 as a recommended cutoff for prioritization. It does not mean a 50% chance of pathogenicity or disease.
How do I read DS_AG and DP_AG?
DS_AG is the predicted acceptor-gain delta score. DP_AG is the matching relative position for that acceptor-gain prediction. Read them together and apply the sign convention documented by the score source.
What does a negative SpliceAI delta position mean?
In the Illumina repository convention, a negative DP means the predicted affected splice position is upstream of the represented variant. It is an offset, not an absolute genomic coordinate.
Does a high SpliceAI score mean a variant is pathogenic?
No. A high score predicts a stronger splice-impact signal. Clinical classification also requires gene-disease context, transcript relevance, population and segregation evidence, functional data, phenotype, and other evidence.
Why are my SpliceAI scores missing?
The variant may be outside the selected annotation, near a chromosome end, an unsupported variant form, a deletion longer than twice the configured distance, or inconsistent with the reference FASTA. Missing output should not be converted to four zeroes.
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Check assembly and normalization, annotation version, raw versus masked mode, maximum distance, and score-file or software release. GENCODE V24 and V43, for example, can produce different lookup results.
Should I use raw or masked scores?
The repository describes raw files for alternative-splicing analysis and masked files for variant interpretation. Record the mode because the values are not interchangeable.
Can the four scores tell me exactly which exon will be skipped?
No. DS and DP summarize predicted directional splice-site changes. They do not alone establish exon skipping, intron retention, cryptic-site use, pseudoexon inclusion, or the final transcript.
The Bottom Line
Bottom line: Read SpliceAI as a directional prediction: identify the largest of DS_AG, DS_AL, DS_DG, and DS_DL, pair it with the matching DP, and state the assembly, annotation, distance, and raw/masked provenance. Use 0.2, 0.5, 0.8, or ClinGen-calibrated thresholds only in their intended settings. A high or missing score is not, by itself, a diagnosis or proof of a particular transcript consequence.
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