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Validate a SpliceAI prediction by testing whether RNA from a relevant, gene-expressing sample contains the predicted splice junction or isoform, comparing that signal with an appropriate control, and confirming a specific event with targeted RT-PCR and sequencing when needed. A SpliceAI score prioritizes a possible splice change; RNA evidence shows whether that change is observed in the biological context you actually sampled.
Do not call a variant proven pathogenic because its score is high, and do not treat a negative RNA result as proof that it cannot alter splicing. The result is only as informative as the tissue, transcript abundance, RNA-seq design, controls, and assay used.
What a SpliceAI prediction actually tells you
SpliceAI analyzes sequence context and reports four delta scores for a variant:
| Field | Predicted event | What the delta position supplies |
|---|---|---|
| DS_AG | Acceptor gain | DP_AG locates the predicted gained acceptor relative to the variant. |
| DS_AL | Acceptor loss | DP_AL locates the predicted lost acceptor. |
| DS_DG | Donor gain | DP_DG locates the predicted gained donor. |
| DS_DL | Donor loss | DP_DL locates the predicted lost donor. |
The Illumina/SpliceAI repository describes the maximum of the four delta scores as ranging from 0 to 1 and interpretable as a probability of splice alteration. Its documented cutoffs are 0.2 for high recall, 0.5 as the recommended threshold, and 0.8 for high precision. These are prioritization thresholds supplied by the tool, not laboratory validation thresholds or universal clinical decision rules.
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Use the score and delta position together. A donor-loss prediction at a particular offset implies a different observable test from an acceptor-gain prediction at another offset. Translate the output into a concrete event before opening a genome browser: exon skipping, intron retention, use of a cryptic donor or acceptor, partial exon deletion, or altered inclusion of an alternative exon.
Check the prediction inputs first
The documented command-line workflow takes a VCF, a reference FASTA, and gene annotation. A result can be misleading or absent if any of those inputs is mismatched.
- Confirm that the chromosome build and reference allele in the VCF match the FASTA used for scoring.
- Confirm that the alternate allele is the one present in the sample you will analyze.
- Use an annotation set appropriate for the gene, transcript, and question. A prediction tied to one transcript cannot automatically be generalized to every isoform.
- Check tool limitations: the repository states that variants within 5 kb of chromosome ends, deletions longer than twice the selected distance parameter, and variants inconsistent with the reference FASTA may not be annotated.
If you used a precomputed score, record its source, genome build, annotation context, allele, and transcript. Without that metadata, a later RNA result may be impossible to reconcile with the original prediction.
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1. Define the event you will look for
Write a one-sentence prediction in observable terms. For example: the variant is predicted to weaken the canonical donor, causing skipping of exon 7; or it is predicted to create a cryptic acceptor 43 bases inside the next exon. Record the gene, transcript, relevant exon or intron, variant allele, strongest delta score, delta position, and expected junctions.
For each hypothesis, identify both the new or strengthened junction and the canonical junction that should be lost or reduced. A useful event definition might include:
- the exon-exon junction expected after exon skipping;
- the exon-intron or intron-exon junction expected with intron retention;
- the exact cryptic donor or acceptor coordinate;
- the normal junction that provides the comparison;
- the transcript or isoform in which the change should occur.
This prevents a common reporting error: saying that RNA sequencing confirmed a splice variant without stating which reads or isoforms were actually observed.
2. Choose RNA from a biologically informative sample
Use a tissue or cell type that expresses the relevant gene and transcript under the condition being studied. A prediction cannot be tested effectively if the target transcript is absent, extremely scarce, or regulated differently in the sampled tissue.
Consider four questions before sequencing:
- Is the gene expressed in this tissue at a level that can produce junction-spanning reads?
- Is the specific transcript or exon used in this tissue?
- Could the splice event be condition-dependent, such as after stimulation, differentiation, treatment, or stress?
- Does the sample carry the variant, and is it heterozygous or homozygous?
Blood RNA has been used for clinical splice analysis, including a 2020 Genetics in Medicine study that combined blood RNA with RT-PCR and Sanger sequencing. Blood is not a substitute for a tissue in which the gene is not expressed or is regulated differently. If the disease-relevant tissue is unavailable, state that limitation rather than presenting blood as a universal proxy.
3. Select RNA-seq data capable of testing the event
Use an RNA-seq design and read depth that can sample the target transcript and the junctions of interest. There is no single read length, depth, aligner, or differential-splicing test that is correct for every gene and study design. The appropriate choices depend on transcript abundance, the size and location of the predicted event, the number of samples, and whether you need discovery or confirmation.
For a junction-level question, make sure the analysis retains splice-junction evidence rather than only gene-level counts. Paired-end data can help characterize exon connectivity, but short reads may still fail to connect distant events into one full-length isoform. Record the library preparation, read configuration, sample quality metrics, alignment and junction-calling method, and filtering rules so another analyst can understand how support was counted.
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When comparing affected and unaffected samples, keep tissue, collection conditions, RNA quality, library preparation, and sequencing batch as similar as practical. Large technical differences can look like biological splicing differences.
4. Inspect the predicted junctions and isoforms
Examine reads spanning the predicted novel junction, reads supporting the canonical junction expected to be lost, and reads supporting the relevant transcript isoforms. Report the actual junction coordinates and the number or proportion of supporting reads using the method defined for your study.
Examples of evidence to inspect include:
- Exon skipping: reads that connect the upstream exon directly to the downstream exon, together with reduced reads supporting the inclusion junction.
- Intron retention: reads extending from an exon into the intron, while checking whether the signal reflects unprocessed nuclear RNA or a mature retained-intron transcript.
- Cryptic donor or acceptor: reads beginning or ending at the predicted alternative coordinate, with the exact offset matching the SpliceAI delta position.
- Partial exon loss: a junction that starts or ends inside an exon rather than at its annotated boundary.
- Isoform switching: a change in the relative support of complete transcript models, not merely one isolated junction.
Inspect the alignment around the site for mismatches, low-complexity sequence, multimapping reads, and neighboring alternative junctions. A handful of reads at a highly expressed or repetitive locus is not equivalent to a reproducible isoform-level observation.
5. Compare the event with an appropriate control
Use the control design that matches the biological question. Options include a matched reference sample, unaffected controls processed under similar conditions, or allele-aware comparison in a heterozygous sample.
For each comparison, account for:
- RNA integrity and library quality;
- target transcript abundance;
- cell-type composition and tissue heterogeneity;
- developmental state, treatment, or other condition differences;
- sequencing depth and batch effects;
- whether the same alternative junction occurs naturally in controls.
Alternative splicing can be normal. A positive junction is more persuasive when it is enriched in the variant-bearing sample, linked to the variant-bearing allele, or absent or substantially reduced in well-matched controls. In a heterozygote, allele-aware analysis can help determine whether the abnormal transcript is associated with the alternate allele, but allele-specific conclusions require adequate informative heterozygous sites and sufficient reads.
6. Confirm a specific junction with targeted RT-PCR when useful
Targeted RT-PCR is an orthogonal test for a defined event. Place primers so that the predicted products differ in a way that can be resolved or sequenced: for example, primers in the flanking exons for exon skipping, or one primer spanning a cryptic junction.
The clinical blood RNA workflow reported in Genetics in Medicine used RT-PCR, agarose gel electrophoresis, and Sanger sequencing. A protocol published in 2026 describes RT-PCR and amplicon sequencing for measuring splice isoforms. Before treating the result as confirmation, verify that:
- the primers amplify the intended transcript region;
- the product is a single interpretable band or a defined set of products;
- the sequenced product contains the predicted junction and bases on both sides;
- no-template and normal-sample controls behave as expected;
- the assay does not preferentially amplify one isoform simply because it is shorter or more efficiently primed.
Endpoint gels are qualitative. If you need a quantitative exon-inclusion estimate, use a junction-specific quantitative assay or another validated quantitative method rather than converting band brightness directly into an isoform percentage.
Interpret product-size guidance in context. The cited protocol notes reduced PCR efficiency for fragments longer than approximately 1 kb, describes a short-read amplicon limit of 500 bp in its design, and favors shorter products. Those are limitations of that protocol, not universal hard limits for every laboratory assay.
7. Report the result at the correct strength
A complete result states what was predicted, what was observed, in which sample and tissue, how it compared with controls, and which assay produced the evidence.
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Distinguish these statements:
- Observation: “Reads spanning the predicted exon 6–exon 8 junction were detected in the variant-bearing blood RNA sample.”
- Association: “The junction was enriched in the variant-bearing sample compared with matched controls.”
- Causal conclusion: “The variant causes the splice change.”
The third statement requires stronger design support than the first two. If the sample carries the variant and the event is allele-associated, say so. If the assay only demonstrates an isoform in one tissue, scope the conclusion to that tissue and assay.
RNA-seq versus targeted RT-PCR and amplicon sequencing
| Axis | RNA sequencing | Targeted RT-PCR or amplicon sequencing |
|---|---|---|
| Breadth | Can survey transcript and junction evidence beyond one preselected event, subject to expression, design, and coverage. | Focuses on the event covered by the primer design. |
| Best use | Testing whether predicted splicing is observable and comparing transcript or junction patterns. | Orthogonal confirmation and focused characterization of a predicted junction or isoform. |
| Quantification | Depends on study design, depth, analysis method, and transcript abundance; report the method and evidence actually used. | Endpoint gel is qualitative; junction-specific quantitative assays are better suited to measuring relative abundance. |
| Main constraints | Tissue expression, transcript abundance, read support, and study-specific analysis choices. | Primer placement, amplification efficiency, product length, and size bias. |
| Large transcript structures | Short reads may not connect distant events into a full isoform. | For larger amplicons or full-length connectivity, the cited protocol points to Oxford Nanopore or PacBio Iso-Seq. |
These methods answer related but different questions. RNA-seq is useful for discovering whether the predicted event appears in the sample and whether other isoforms complicate the interpretation. RT-PCR is useful for confirming a specific junction with an independent assay. Agreement between methods strengthens the observation, but shared sample or primer limitations still matter.
How to interpret positive and negative results
When RNA supports the prediction
A positive result means the predicted splice event was observed under the sampled conditions. Confidence increases when the junction matches the predicted coordinate, has reproducible read support, is enriched relative to appropriate controls, and is confirmed by an independent targeted assay.
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It still does not automatically prove that the variant caused the event. The same isoform may occur naturally, the sample may contain mixed cell populations, or another linked variant may affect splicing. Causal language should reflect whether the design establishes variant-to-isoform linkage.
When RNA does not show the prediction
A negative result is scoped to the tissue, condition, assay, and detection sensitivity. Possible explanations include:
- the gene or relevant transcript is not expressed in the sampled tissue;
- the isoform is too rare for the available depth;
- the event occurs only under a different biological condition;
- the transcript is unstable or subject to nonsense-mediated decay;
- the library or alignment method failed to capture the junction;
- the predicted event is weaker or more complex than the score suggests.
Do not assign a universal false-negative rate from one negative sample. Reassess expression, RNA quality, read depth, junction filters, tissue choice, and the possibility of a different splice consequence before concluding that the prediction lacks biological effect.
Prediction strength is not observed effect size
A high SpliceAI score indicates strong computational prioritization, not a guaranteed large change in isoform abundance. The 2026 validation protocol reports better concordance for variants near canonical splice sites and cautions that distal exonic predictions do not consistently reflect the magnitude or complexity of observed changes. A distal prediction can therefore produce a subtle, mixed, or unexpected RNA phenotype even when its score is high.
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Common failure modes and how to fix them
The wrong genome build or allele was analyzed
Symptom: the predicted coordinate does not match the gene model, or no reads support a junction that appears biologically plausible.
Fix: recheck the VCF allele against the reference FASTA, confirm the chromosome build, and document the annotation release and transcript. Re-score the correctly represented variant before interpreting RNA data.
The sample does not express the target transcript
Symptom: neither canonical nor predicted junctions have meaningful coverage.
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Fix: verify gene and transcript expression in the chosen tissue. Use a more relevant tissue or condition when ethically and practically possible. If only blood is available, state that the result is limited to blood RNA.
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Symptom: a report says “the SpliceAI isoform was found” without listing a junction.
Fix: translate the delta position into exact genomic or transcript coordinates and report the junction sequence or exon relationship that was observed.
Too few reads support the junction
Symptom: one or two reads appear at the predicted site, with no reproducibility.
Fix: examine target expression and coverage, review mapping quality and multimapping, and avoid presenting sparse support as confirmation. A targeted assay may help if sufficient RNA is available.
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Symptom: the predicted junction appears at a similar level in unaffected samples.
Fix: determine whether it is a normal alternative isoform, a tissue-specific pattern, or a technical artifact. Focus on enrichment, allele association, and reproducibility rather than presence alone.
RT-PCR favors the shorter product
Symptom: gel bands suggest one isoform dominates, but RNA-seq shows a more balanced pattern.
Fix: check primer placement and product lengths. Treat endpoint gels as qualitative, sequence the products, and use a junction-specific quantitative assay when relative abundance is important.
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Short reads cannot establish full-length connectivity
Symptom: separate junctions are observed, but it is unclear whether they occur in the same molecule.
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Fix: design a longer targeted assay or use long-read RNA sequencing. The cited protocol identifies Oxford Nanopore and PacBio Iso-Seq as options for larger amplicons and full-length transcript structure.
SpliceAI file settings were mismatched to the analysis
Symptom: an analysis of alternative splicing uses an output file intended for a different interpretation context.
Fix: follow the repository documentation: it states, “We recommend using raw files for alternative splicing analysis and masked files for variant interpretation.” Treat this as a tool-specific file-handling recommendation, not a general rule for every splicing pipeline.
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No single read count or score establishes validity for every gene. A persuasive case usually combines several independent observations:
- The variant and prediction are correctly represented on the chosen reference and transcript.
- The sampled tissue expresses the relevant gene and transcript.
- RNA-seq detects the predicted junction or isoform with interpretable, reproducible support.
- The canonical junction or competing isoform changes in the expected direction.
- The event is absent, lower, or otherwise different in an appropriate control, or it tracks the alternate allele in a heterozygote.
- A targeted RT-PCR product reproduces the expected size and junction sequence when such confirmation is feasible.
- The final wording distinguishes observation, association, and causality.
This is an evidence pattern, not a mandatory checklist with a universal pass mark. A study may have strong RNA-seq evidence but no suitable control, or excellent targeted confirmation in a tissue with limited disease relevance. Report those trade-offs explicitly.
How to write the final result
A concise, defensible report can follow this structure:
- Prediction: identify the variant, transcript, delta score, delta position, and expected splice event.
- Sample: identify tissue or cell type, condition, genotype, and whether the target transcript was expressed.
- RNA-seq evidence: name the observed junctions or isoforms and describe their support and comparison with controls.
- Orthogonal assay: describe RT-PCR, product sizing, and sequencing results if performed.
- Interpretation: state whether the data show an observed event, an association with the variant, or a stronger causal conclusion supported by the design.
- Limitations: state tissue specificity, transcript abundance, coverage, control limitations, and any amplification or isoform-connectivity concerns.
For example, a properly scoped conclusion might say that the predicted cryptic acceptor junction was detected in variant-bearing blood RNA, was not detected in matched controls above the assay threshold, and was confirmed by sequencing of an RT-PCR product. That wording establishes what the experiment observed without claiming that every tissue will behave identically.
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A 2020 Genetics in Medicine blood-RNA study reported that SpliceAI classified more than 90% of variants consistently with experimental data in its cohort. That statistic belongs to the study’s cohort, assay, and variant mix; it is not a general accuracy guarantee for every gene, tissue, or score threshold.
The original SpliceAI paper in Cell (2019) established the model’s use for predicting splice effects from primary sequence, while RNA-seq studies such as Goldstein and colleagues in PLOS ONE (2016) demonstrate how junction-level RNA evidence and RT-PCR can be used to validate novel splice events. Together, these sources support a two-stage approach: computational prediction to prioritize an event, followed by sample-specific RNA testing to determine whether it is observed.
The practical decision rule
Call a SpliceAI prediction RNA-supported only when the relevant sample expresses the target transcript and the predicted junction or isoform is actually observed with interpretable evidence. Strengthen the claim with an appropriate control, allele-aware analysis where possible, and targeted RT-PCR plus product sequencing for the specific event. Scope both positive and negative findings to the tissue, condition, assay, and design that produced them.
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