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To run the original SpliceAI command-line tool locally, you need three things: a variant VCF, a reference-genome FASTA that uses the same assembly and reference alleles as that VCF, and gene annotations. The standard package supplies the neural-network weights and canonical GENCODE V24 annotations for GRCh37 and GRCh38, so the reference FASTA is usually the only major biological data file you must obtain separately. The documented command is spliceai -I input.vcf -O output.vcf -R genome.fa -A grch37 (use grch38 for GRCh38).
The data SpliceAI needs
The original SpliceAI CLI reads variants from a VCF and scores them against sequence from a matching reference genome. It also needs gene models to determine which annotated transcripts and splice regions to evaluate. In a normal installation, those inputs are supplied as follows:
| Input or resource | What it contains | Do you download it separately? |
|---|---|---|
| Variant VCF | Your SNVs and supported simple indels, with chromosome, position, REF and ALT alleles | Yes; this is your own input data |
| Reference FASTA | The genome sequence used to verify alleles and extract sequence around each variant | Usually yes; obtain the GRCh37/hg19 or GRCh38/hg38 FASTA that matches your VCF |
| Gene annotation | Transcript and gene boundaries used to define annotated genes | No for ordinary use: canonical GENCODE V24 annotations for GRCh37 and GRCh38 are bundled; a compatible custom file is optional |
| Model weights | Parameters for the trained SpliceAI neural network | No for the standard original package; the weights are included with the package |
| Software dependencies | The executable and libraries, including TensorFlow | Installed through pip or conda; these are software requirements, not genomic input files |
The official README describes -R as a reference-genome FASTA that can be downloaded for GRCh37/hg19 or GRCh38/hg38. Select the build that corresponds to the coordinates and REF alleles in your VCF, not simply the build that is most convenient to find.
1. Prepare the variant VCF
What the VCF represents
Your VCF is the set of variants to be scored. The documented original workflow is designed for single-nucleotide variants and simple indels in which either the REF or ALT allele is a single base. It is not a general-purpose predictor for every structural-variant representation. Keep the VCF’s chromosome names, positions and REF/ALT strings unchanged unless you have deliberately converted the file to the selected reference assembly.
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Checks before running
- Confirm the coordinate assembly recorded by your pipeline (for example, GRCh37/hg19 or GRCh38/hg38).
- Check that each REF allele is the base present at that position in the selected FASTA.
- Make sure ALT alleles use the VCF representation expected by your variant-calling or normalization workflow.
- Ensure the file is a valid VCF and that the records you care about are not filtered out before SpliceAI receives them.
SpliceAI does not annotate variants inconsistent with the supplied FASTA. A build mismatch can therefore produce skipped records rather than merely slightly different scores.
2. Obtain the matching reference FASTA
Choose the correct assembly
For a GRCh37 or hg19 VCF, use a GRCh37/hg19 reference FASTA. For a GRCh38 or hg38 VCF, use GRCh38/hg38. The assembly label alone is not enough: the sequence must also agree with the VCF’s REF alleles. If your VCF was lifted over, use the FASTA for the post-conversion assembly and verify the resulting alleles.
Why the FASTA is required
SpliceAI extracts sequence context around each variant and compares the VCF allele with the reference sequence. Without the FASTA, it cannot establish the sequence context or validate whether the VCF’s REF allele is present. The FASTA is therefore the key external data file for a standard local run.
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Keep the FASTA and its index where your run can read them consistently. The original command takes the FASTA path directly with -R; use an unambiguous filename that records the build, such as a GRCh38 reference, rather than a generic genome.fa when several assemblies are present. The file’s provenance and build should be recorded with your results so a later rerun uses the same sequence.
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3. Select annotations: bundled canonical models or a custom file
Bundled GENCODE V24 annotations
The original package includes canonical GENCODE V24 annotations for both GRCh37 and GRCh38. Pass grch37 or grch38 to -A to select the corresponding bundled annotation set. This is why a separate annotation download is normally unnecessary.
When a custom annotation is appropriate
You can provide a custom annotation file in the format expected by the SpliceAI repository when your project requires a different transcript set or a particular gene definition. A custom file is an annotation choice, not a replacement for the reference FASTA: it must still correspond to the same assembly and coordinate system as the VCF and FASTA.
Changing annotations can change which variants are considered to lie within annotated genes and which transcript contexts are evaluated. Record the annotation choice with your output, especially when comparing runs made with different transcript releases.
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After installing the package and placing the VCF, FASTA and (if needed) custom annotation file in accessible locations, the documented form is:
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spliceai -I input.vcf -O output.vcf -R genome.fa -A grch37Use -A grch38 with a GRCh38 VCF and FASTA. The arguments mean:
-I: input VCF containing the variants to score.-O: output VCF to which SpliceAI writes its annotations.-R: reference-genome FASTA.-A: bundled assembly-specific annotation selector (grch37orgrch38) or a compatible custom annotation file, depending on the package’s accepted syntax.
The model weights used by the original CLI are packaged with the installation. You therefore do not normally add a separate model path for this workflow.
Software setup versus biological data
Installing SpliceAI still requires an environment capable of running its dependencies. The official README provides pip and conda installation routes and identifies TensorFlow as a dependency. These packages are executable prerequisites, not additional genome data. A machine that has the VCF and FASTA but lacks a working SpliceAI/TensorFlow environment cannot run the command; conversely, installing the software without supplying the correct FASTA and VCF leaves the biological inputs incomplete.
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For reproducibility, capture the package version, Python environment, TensorFlow version, operating-system details and the exact command line alongside the VCF, FASTA build and annotation selection. The documentation does not state that a CUDA-capable GPU is required for the original CLI; do not assume the GPU requirement described for SpliceAI2 applies to this tool.
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Make sure your variants are in SpliceAI’s scoring scope
Supported variant forms
The original documentation describes scoring for SNVs and simple indels where REF or ALT is a single base, within genes defined by the chosen annotation. A record can be syntactically valid in VCF and still fall outside the predictor’s documented scope.
Reasons a record may be skipped
- The variant is outside a gene represented in the selected annotation.
- It lies within 5 kilobases of a chromosome end.
- It is a deletion longer than twice the configured
-Ddistance. - The REF allele is inconsistent with the supplied FASTA.
- The representation is outside the documented SNV/simple-indel handling.
Inspect the output for records that lack scores and investigate them against these conditions rather than treating every missing annotation as a low-impact prediction.
Scoring options that affect interpretation, not required inputs
Sequence distance (-D)
The default -D distance is 50. This setting controls the distance used for the prediction context and is also relevant to the documented rule for long deletions: deletions longer than twice the configured distance may not be scored. Changing -D does not remove the need for a matching FASTA or alter the assembly requirement.
Raw versus masked scores (-M)
The default -M setting is 0, which produces raw scores. The README recommends raw scores for alternative-splicing analysis and masked scores for variant interpretation. Choose the mode for your analytical objective and record it with the output; it is a scoring choice, not a different input-data package.
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Build-consistency troubleshooting
Symptoms of a mismatch
Typical warning signs are many records receiving no score, messages about alleles that do not match the reference, or results that disappear when the same VCF is run with a different FASTA. These symptoms are consistent with a VCF/FASTA assembly or allele mismatch and should be resolved before interpreting scores.
A recovery sequence
- Identify the assembly used to produce the VCF and the exact FASTA supplied with the command.
- Verify representative REF alleles directly against that FASTA.
- Confirm that chromosome naming and coordinate conversion did not change during any lift-over or normalization step.
- Run a small subset of known, valid records before processing the full file.
- Check whether skipped records are outside annotated genes, near chromosome ends, long deletions, or unsupported variant forms.
- Only after those checks, compare raw versus masked score settings or change
-D.
This order separates input-data errors from legitimate scope limitations and from interpretation choices.
Original SpliceAI and SpliceAI2 are different local workflows
SpliceAI2 is a related but separate tool. Its documented workflow uses a TSV rather than the original CLI’s VCF, requires chromosome, position, REF, ALT and strand columns, and expects a reference FASTA plus pretrained model folders. Illumina’s SpliceAI2 README states that a CUDA-capable GPU is required. Do not add those requirements to an installation of the original SpliceAI package.
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|---|---|---|
| Variant input | VCF | TSV with chromosome, position, REF, ALT and strand |
| Annotations and models | Canonical GENCODE V24 annotations and model weights are included for ordinary use; custom annotation is optional | Pretrained model folders are supplied as separate resources in the documented workflow |
| Reference sequence | Matching GRCh37/hg19 or GRCh38/hg38 FASTA | Reference FASTA |
| Compute requirement stated in documentation | TensorFlow-based software setup; no CUDA-capable GPU requirement is stated in the cited original instructions | CUDA-capable GPU required |
| Resource figures | Not applicable to the original local input requirement | Illumina describes precomputed-resource scope of 4 billion possible single-nucleotide variants and 150 million population-observed indels (2026); these are not files you download to run original SpliceAI |
Confirm the version and compatibility requirements of whichever tool you select. A SpliceAI2 model folder cannot be substituted for the original package’s bundled resources, and an original-SpliceAI VCF command is not a SpliceAI2 TSV invocation.
Licensing and maintenance before deployment
GitHub marks Illumina’s original SpliceAI repository archived and read-only on April 20, 2026. Its README states that the source code is under the PolyForm Strict License 1.0.0 and that trained models are under CC BY-NC 4.0 for academic and non-commercial use. Other uses require a commercial license from Illumina. Review the current license text and contact Illumina for commercial deployment questions; the license terms should be treated separately from the technical question of which files the program reads.
Archival status also matters operationally: pin the package and dependency versions used for a validated run, retain the exact command and input-file checksums, and document any custom annotation file. That record is especially important if the repository remains read-only and your environment must be reproduced later.
Quick Recap
A complete preflight checklist
- VCF coordinates and REF alleles are tied to a known GRCh37/hg19 or GRCh38/hg38 assembly.
- The FASTA uses that same assembly and agrees with representative REF alleles.
- The annotation selector matches the assembly:
grch37orgrch38, or a compatible custom file. - The SpliceAI package and TensorFlow dependency install successfully through pip or conda.
- You understand that original SpliceAI bundles model weights and canonical GENCODE V24 annotations.
- Your variants are SNVs or documented simple indels in annotated genes.
- You have considered chromosome-end proximity, long deletions and FASTA-inconsistent records.
- You have selected raw (
-M 0) or masked scoring for the intended analysis and recorded the-Dvalue. - You are not applying SpliceAI2’s TSV, model-folder or CUDA requirements to the original CLI.
- Your use complies with the source-code and model licenses, including commercial-use restrictions.
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