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For the original SpliceAI command-line tool, you need three biological inputs: a variant VCF, a reference-genome FASTA that matches the VCF’s assembly and REF alleles, and a gene-annotation choice. The standard package already contains the trained model weights and canonical GENCODE V24 annotations for GRCh37 and GRCh38, so the reference FASTA is normally the main external data file.
The documented run is spliceai -I input.vcf -O output.vcf -R genome.fa -A grch37 (use grch38 for GRCh38). SpliceAI2 is a separate workflow with TSV input, model folders, a reference FASTA, and a CUDA-capable GPU; its requirements should not be substituted for those of the original tool.
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What you must have before running the original SpliceAI
The original SpliceAI CLI annotates variants from a VCF and writes prediction scores to an output VCF. Gather these items before launching it:
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- Reference FASTA: the genome assembly corresponding to the VCF coordinates, with reference bases that agree with each VCF REF allele.
- Annotation selection: either the bundled
grch37orgrch38canonical annotation, or a compatible custom annotation file in the format expected by the repository. - Installed software: the SpliceAI package and its TensorFlow dependency, installed through the project’s pip or conda instructions.
Model weights are included with the original package. You do not normally download a separate model file for the standard command-line workflow.
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The reference FASTA is the key external data file
The official README describes -R as the reference genome FASTA and identifies GRCh37/hg19 and GRCh38/hg38 as the supported choices. Pick the assembly that your VCF actually uses; a GRCh37 VCF must not be scored against a GRCh38 FASTA, and vice versa.
The FASTA is used to verify the sequence context and the REF alleles. If a variant is inconsistent with the supplied FASTA, SpliceAI does not annotate it. This makes the FASTA more than a background file: it is part of the input validation that determines whether a record can be scored.
Choose the annotation that matches the assembly
The original package supplies canonical GENCODE V24 annotations for both GRCh37 and GRCh38. Set -A grch37 or -A grch38 to use the corresponding bundled annotation. A custom annotation file is possible when you need a different transcript or gene set, but it must follow the repository’s required format and remain compatible with the reference assembly.
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How to run the standard local workflow
The official four-argument pattern is:
spliceai -I input.vcf -O output.vcf -R genome.fa -A grch37Replace genome.fa with the matching GRCh37/hg19 FASTA, or change the annotation argument to grch38 and provide a GRCh38/hg38 FASTA for a GRCh38 VCF.
- Install the package and TensorFlow. Use the original project’s pip or conda installation method. This is software setup, not an additional genomic data requirement.
- Inspect the VCF. Confirm that coordinates and REF alleles belong to the intended assembly and that the records are valid VCF records.
- Obtain the corresponding FASTA. Use a GRCh37/hg19 or GRCh38/hg38 reference according to the VCF. Keep the FASTA available locally and make sure the path supplied to
-Ris readable. - Select the annotation. Use the bundled assembly label for ordinary runs. Supply a custom annotation only when its format and assembly are known to be compatible.
- Run SpliceAI. Pass the input and output VCF paths, FASTA path and annotation option exactly as shown above.
- Review the output VCF. Check which records received scores and investigate any records that were skipped rather than treating missing scores as zero.
What the VCF and FASTA must agree on
Assembly and coordinates
Every VCF coordinate is meaningful only within a particular assembly. GRCh37/hg19 and GRCh38/hg38 use different coordinate systems, so an apparently valid position can refer to a different base in the other build. Select the annotation label and FASTA that correspond to the VCF’s build.
REF alleles
For each record, the VCF REF allele must match the sequence in the FASTA at that location. A mismatch can arise from using the wrong build, a different reference sequence, or an incorrectly normalized record. The documented behavior is to leave variants inconsistent with the FASTA unannotated.
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Variant representations supported by the original CLI
The documented command handles single-nucleotide variants and simple indels in which REF or ALT is a single base. It is not a general scorer for every possible structural or complex variant representation. Before a run, classify the records in your VCF so that unsupported forms are not mistaken for failed installation.
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When SpliceAI will not produce a score
Missing output for a record can be a scope limitation rather than a software error. The project identifies several cases in which variants may be skipped:
- The variant lies outside a gene represented in the selected annotation.
- The variant is within 5 kb of a chromosome end.
- The variant is a deletion longer than twice the configured
-Ddistance. - The VCF REF allele does not agree with the supplied FASTA.
- The record is outside the documented SNV or simple-indel handling.
Use the annotation choice and the reference sequence to diagnose the first four cases. For a complex variant, confirm that the original CLI is the appropriate tool before changing command-line options.
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Scoring options that change results, not input requirements
Context distance (-D)
The default -D distance is 50. This setting controls the prediction context and also relates to the documented handling of long deletions: deletions longer than twice the configured distance may not be annotated. Changing -D does not remove the need for a matching FASTA or a valid VCF.
Raw versus masked scores (-M)
The default -M setting is 0, which returns raw scores. The README recommends raw scores for alternative-splicing analysis and masked scores for variant interpretation. This is a downstream scoring choice; it does not alter which reference, annotation or model data are required.
Bundled resources versus files you provide
| Resource | Original SpliceAI status | What you need to do |
|---|---|---|
| Trained model weights | Included with the package | No routine separate download is required. |
| Canonical gene annotations | GENCODE V24 annotations for GRCh37 and GRCh38 are bundled | Select grch37 or grch38, or provide a compatible custom annotation. |
| Variant data | Not supplied by the tool | Provide your own input VCF. |
| Reference genome | Not supplied as part of the standard run | Download and provide the matching GRCh37/hg19 or GRCh38/hg38 FASTA. |
| TensorFlow and package runtime | Software dependency | Install through the project’s pip or conda instructions. |
Original SpliceAI versus SpliceAI2
“SpliceAI” can refer to two different local workflows. The original tool is the VCF-based command-line program described above. SpliceAI2, documented separately by Illumina, uses a different input and resource layout.
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| Decision point | Original SpliceAI CLI | SpliceAI2 |
|---|---|---|
| Variant input | VCF | TSV containing chromosome, position, REF, ALT and strand |
| Annotations and models | Model weights and canonical GENCODE V24 annotations are packaged; custom annotation is optional | Requires pretrained model folders and its own workflow resources |
| Reference sequence | Matching genome FASTA is required | Reference FASTA is required |
| Compute requirement | The original documentation describes the package and TensorFlow dependency; no CUDA-capable GPU requirement is stated in the cited CLI instructions | Documentation states that a CUDA-capable GPU is required |
| Output workflow | Annotated output VCF | TSV-oriented workflow described by the SpliceAI2 repository |
Do not download SpliceAI2 model folders merely because you are running the original VCF command. Conversely, do not assume the original package’s bundled annotations satisfy SpliceAI2’s resource requirements.
What the SpliceAI2 resource figures mean
Illumina describes SpliceAI2 precomputed resources covering 4 billion possible single-nucleotide variants and 150 million population-observed indels, figures reported for 2026. Those numbers describe the scope of precomputed resources; they are not the amount of data a user must download to run the original SpliceAI locally.
Practical troubleshooting checklist
No variants receive scores
- Verify that the FASTA assembly matches the VCF assembly.
- Check REF alleles against the FASTA at the stated positions.
- Confirm that the selected
-Avalue corresponds to the same build. - Check whether the records are outside annotated genes, near chromosome ends or beyond the documented variant scope.
Some records are missing
Separate expected filtering from an input problem. A record may be omitted because it is a complex variant, a long deletion, outside an annotated gene, within 5 kb of a chromosome end, or inconsistent with the reference. Compare the affected records with those conditions before changing scoring options.
You need a different transcript set
Use a custom annotation only if you can supply the repository’s required format and keep the annotation tied to the same assembly as the FASTA and VCF. A custom file changes the gene definitions used for scoring; it does not replace the reference genome.
You are planning commercial use
Check the project’s current license terms before deployment. GitHub marks the original Illumina repository archived and read-only on April 20, 2026. Its README states that source code is under the PolyForm Strict License 1.0.0, while trained models are under CC BY-NC 4.0 for academic and non-commercial use. The README says other use requires a commercial license from Illumina. This is a licensing matter, not a technical workaround; commercial users should review the license and contact Illumina.
A minimal pre-run checklist
- Is the VCF’s assembly known?
- Does the FASTA use that same assembly?
- Do REF alleles agree with the FASTA?
- Are you using
grch37for GRCh37/hg19 orgrch38for GRCh38/hg38? - Do you need bundled canonical annotations, or do you have a correctly formatted custom annotation?
- Are your records SNVs or documented simple indels?
- Have you distinguished skipped records from records with genuinely low scores?
- Are your intended uses compatible with the source-code and model licenses?
If the answers are yes, the original local run needs no large model download or separate annotation package beyond the bundled resources; the essential external genome data is the matching reference FASTA, together with your VCF.
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