Validate a SpliceAI prediction by looking for its predicted splice event in RNA from a sample that expresses the relevant gene, comparing the result with an appropriate control, and confirming a specific junction with targeted RT-PCR and sequencing when needed. SpliceAI prioritizes variants; RNA assays test whether an event is actually observed in a defined biological context.
A high score is not laboratory proof, and a negative result from one tissue or assay does not prove that a variant has no splicing effect. The strength of the conclusion depends on the transcript, tissue, allele, controls, coverage and confirmation method.
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What RNA validation can—and cannot—show
SpliceAI analyzes sequence and predicts how a variant might alter splice-site choice. RNA sequencing answers a different question: does the predicted transcript or junction appear in the RNA sample that was tested? Those statements should remain separate in both analysis and reporting.
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An RNA result is strongest when the sample carries the variant, expresses the affected transcript, and shows a junction or isoform difference that is absent or substantially lower in an appropriate comparison sample. Even then, observation and causation are not identical. Alternative splicing can occur in unaffected samples, and technical factors can change the number of reads supporting a junction.
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In a 2020 Genetics in Medicine blood-RNA study, SpliceAI classified more than 90% of variants consistently with experimental data in that cohort. That statistic describes the study’s samples, assays and variant set; it is not a universal accuracy guarantee.
Read the SpliceAI result before designing the experiment
Record the complete prediction rather than only the largest score. The repository documentation reports four delta scores and four corresponding delta positions:
| Field | Meaning | How to use it |
|---|---|---|
DS_AG |
Delta score for acceptor gain | Indicates a possible newly used acceptor. |
DS_AL |
Delta score for acceptor loss | Indicates a possible weakened or lost acceptor. |
DS_DG |
Delta score for donor gain | Indicates a possible newly used donor. |
DS_DL |
Delta score for donor loss | Indicates a possible weakened or lost donor. |
DP_AG, DP_AL, DP_DG, DP_DL |
Delta positions | Locate the predicted change relative to the variant. |
The maximum of the four delta scores is described by the SpliceAI repository as ranging from 0 to 1 and interpretable as a probability of splice alteration. The same documentation lists 0.2 as a high-recall threshold, 0.5 as a recommended threshold and 0.8 as a high-precision threshold. These are prioritization cutoffs from the tool documentation, not laboratory validation thresholds or universal clinical decision rules.
Translate the score and position into an event you can observe. For example, a donor-loss prediction may suggest exon skipping or intron retention; a donor- or acceptor-gain prediction may suggest a cryptic junction; an acceptor-loss prediction may suggest loss of an exon boundary or use of an alternative acceptor. The exact event depends on transcript structure and the location reported by the delta position.
Preflight checks that prevent misleading RNA results
Verify the variant representation
Confirm the reference genome build, chromosome and position, reference allele, alternate allele and strand orientation. The allele in the RNA experiment must be the allele used for the prediction. A mismatched representation can produce an apparently plausible score for the wrong change.
SpliceAI’s command-line workflow expects a VCF, a reference FASTA and gene annotation. Check that all three use compatible references. The repository notes that variants may not be annotated when they are within 5 kb of a chromosome end, when a deletion is longer than twice the selected distance parameter, or when the variant is inconsistent with the reference FASTA.
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Confirm the transcript and annotation context
Write down the transcript or exon model used to interpret the event. A prediction can be biologically meaningful while appearing at a different coordinate in another transcript annotation. Keep the annotation release with the result so that a later analyst can reconstruct which exon boundaries were being tested.
Establish that the sample can express the target
Choose RNA from a tissue or cell type in which the gene and relevant transcript are expressed. Also ask whether the variant-bearing transcript is expressed under the sampled condition. Blood RNA can be useful for clinical splice analysis, but blood cannot substitute for a tissue in which the gene is not expressed or is regulated differently.
Decide which SpliceAI file mode fits the analysis
The repository documentation states: “We recommend using raw files for alternative splicing analysis and masked files for variant interpretation.” This is a tool-specific recommendation. Follow the raw-versus-masked choice appropriate to the question and record that setting with the prediction.
A step-by-step RNA-seq validation workflow
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Define the observable event
Create an event record containing the gene, transcript, affected exon or intron, variant allele, predicted gain or loss, delta score and delta position. State what RNA would look like if the prediction were correct: a novel donor junction, a novel acceptor junction, exon skipping, intron retention, altered exon inclusion or another defined isoform change.
Include the canonical junction expected to be lost or reduced. Looking only for the novel junction can miss the more informative evidence that the normal junction has changed in the same sample.
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Select an informative sample and comparison
Use a variant-bearing sample whose relevant transcript is expressed. For a heterozygous individual, preserve the genotype information so that allele-aware analysis is possible. Select a matched reference, an unaffected control or another comparison justified by the study design. Document tissue, condition, collection time, RNA quality and whether the control is expected to express the same isoforms.
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Choose RNA-seq data suited to the event
RNA-seq must provide enough evidence to sample the target transcript and its junctions. Read length, sequencing depth, library preparation, strandedness, paired-end design, aligner and splicing-analysis method should be chosen for the gene and event; no single set of values is established for every study. A rare transcript or low-frequency isoform needs more sensitivity than a highly expressed gene with abundant junction reads.
Keep the analysis question explicit. Discovery-oriented analysis may survey all junctions and isoforms, whereas a targeted validation asks whether particular junctions are present and how their support compares with controls.
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Inspect the predicted and canonical junctions
Examine reads that span the predicted novel junction, not merely reads mapping somewhere in the gene. Record the junction coordinates, number and orientation of supporting reads, mapping quality and whether paired reads agree with the structure. Also inspect the canonical junction or exon-exon connection expected to be lost, along with relevant transcript isoforms.
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Compare the event with controls and allele information
Interpret junction support alongside transcript abundance and sample quality. A junction seen only in a degraded or poorly sequenced library is weak evidence. In a heterozygous sample, allele-aware reads or other phasing evidence can strengthen the link between the variant and the altered transcript, but the available evidence does not establish one universally correct control design.
Consider biological differences such as tissue, developmental state, treatment and cell composition. A control from a different context may differ in splicing for reasons unrelated to the variant.
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Confirm a specific junction when the decision requires it
Design RT-PCR primers around the predicted event. The 2020 clinical blood-RNA study used RT-PCR, Sanger sequencing and agarose-gel electrophoresis; a separate protocol describes RT-PCR with amplicon sequencing for splice-isoform measurement. Sequence the product so that the junction composition, not just its size, is verified.
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Report the result at the strength supported by the design
Specify the sample and tissue, genotype, transcript, observed junction, read or product evidence, control comparison and assay. Distinguish “the predicted transcript was observed” from “the variant caused the transcript.” Use causal wording only when allele-aware or otherwise controlled evidence supports that attribution.
What counts as supporting RNA evidence?
Exon skipping
Look for reads that connect the exon before the predicted skipped exon directly to the exon after it. Quantify or compare those junctions with reads supporting the normal adjacent junctions. A skipped-exon junction that is already common in controls may represent normal alternative splicing rather than a variant effect.
Intron retention
Inspect reads extending into the intron and the balance between retained-intron and normally spliced transcripts. Intron sequence can be difficult to measure reliably because of transcript degradation and uneven coverage, so confirm the exact retained boundaries with a targeted assay when they matter.
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Verify the precise novel boundary predicted by the delta position. A nearby junction that is not the predicted boundary is not equivalent evidence. Sequence the RT-PCR product when possible to establish the exact donor and acceptor nucleotides used.
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Loss of a canonical site
A lost donor or acceptor should generally be evaluated through both reduced support for the normal junction and appearance of one or more replacement junctions. A reduction without a clearly characterized replacement may reflect low coverage, transcript decay or a broader expression change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.RNA-seq and targeted RT-PCR: choosing the complementary assay
| Axis | RNA sequencing | Targeted RT-PCR or amplicon sequencing |
|---|---|---|
| Breadth | Can survey transcript and junction evidence beyond one preselected event, subject to expression, library design and coverage. | Focuses on the event covered by primer design. |
| Best use | Test whether predicted splicing is observable and compare transcript or junction patterns across samples. | Provide orthogonal confirmation and characterize a specific junction or isoform. |
| Quantification | Depends on design, depth, analysis method and transcript abundance; report the method actually used. | Endpoint gels are qualitative. Junction-specific quantitative assays are better suited to measuring abundance. |
| Main constraints | Tissue expression, transcript abundance, read support and study-specific analysis choices. | Primer placement, amplification efficiency, product length and size bias. |
| Long transcript structure | Short reads may not connect distant events into one full isoform. | For large amplicons or full-length connectivity, the cited protocol points to Oxford Nanopore or PacBio Iso-Seq. |
Failure modes and how to interpret them
The score is high but the predicted junction is absent
First check allele and genome-build representation, transcript expression, RNA quality and coverage over the relevant exons. The prediction may concern a tissue-specific event, a low-abundance isoform or a condition not represented by the sample. A high score prioritizes the variant; it does not require every sample to display the event.
The event appears in both variant and control samples
Determine whether the event is normal alternative splicing, a shared technical artifact or a difference in relative abundance rather than presence versus absence. Compare junction proportions and transcript levels under matched conditions. Presence alone is insufficient to assign causality.
A negative RNA result is being treated as exclusion
Scope the result to the tested tissue, condition, library and assay sensitivity. The relevant transcript may not be expressed, the isoform may be too scarce, or splicing may depend on a stimulus absent from the experiment. Do not convert one negative result into a universal claim that the variant cannot affect splicing.
PCR gives an unexpected band pattern
Sequence products instead of inferring identity from size alone. The protocol notes reduced PCR efficiency for fragments longer than approximately 1 kb and describes a short-read amplicon limit of 500 bp in its design, with a preference for shorter products. These are limitations of that protocol, not universal hard limits for every laboratory method. Primer dimers, nonspecific products and preferential amplification can all distort an endpoint gel.
A gel appears to show a large isoform difference
Endpoint gels are qualitative. Amplification efficiency and product size can make a band look stronger or weaker without reflecting the starting RNA proportion. Use a junction-specific quantitative assay when the conclusion depends on relative isoform abundance.
Short reads cannot establish isoform connectivity
When distant splice events must be shown on the same molecule, use a method that reads the full structure. The cited protocol identifies Oxford Nanopore and PacBio Iso-Seq as options for larger amplicons or full-length transcript connectivity.
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A distal exonic prediction has weak experimental concordance
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. Treat a distal prediction as a hypothesis requiring careful event definition and context-matched testing, not as a direct estimate of effect size.
How to write the validation conclusion
A reproducible report should contain:
- Variant representation, genome build, transcript annotation and SpliceAI score and position fields.
- The predicted event and the canonical junction or isoform used for comparison.
- Tissue or cell type, condition, genotype, RNA quality and expression status.
- RNA-seq design and the method used to inspect or quantify junctions.
- Observed junction coordinates, supporting evidence and comparison with controls.
- Any RT-PCR, Sanger or amplicon-sequencing confirmation, including primer target and product identity.
- A conclusion limited to the tested sample and assay, with causal wording only where the design supports it.
Useful wording is specific: “In variant-carrying blood RNA, reads spanning the predicted acceptor-gain junction were detected above the matched control background, and the junction sequence was confirmed by RT-PCR and Sanger sequencing.” If allele linkage, tissue specificity or quantitative enrichment was not established, say so rather than implying it.
Quick Recap
Validation checklist
- Is the VCF allele identical to the allele used for prediction?
- Are the reference FASTA, genome build and annotation compatible?
- Are score and delta-position fields recorded together?
- Does the selected tissue express the relevant gene and transcript?
- Is the predicted event defined as a measurable junction or isoform change?
- Does the RNA-seq design provide usable coverage for that event?
- Were both novel and canonical junctions inspected?
- Is the control biologically and technically appropriate?
- Was a specific junction sequenced when size alone was ambiguous?
- Does the final wording distinguish observation from causation and scope negative findings to the assay?
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