Proto is not affiliated with Illumina. This toolkit is open source and builds on the implementation produced by this organization. Product names, logos, and trademarks are the property of their respective owners.

### SpliceAI2 Variant Scoring (`spliceai2-score`) Scores genetic variants (chromosome, 1-based position, ref, alt, gene strand) for splice-altering effects, returning the ten strongest donor, acceptor, and junction gains and losses per variant and a headline `summary_score`. Reads each variant's sequence context from a reference genome. #### API Reference

Source Variants to score. A single variant is auto-wrapped into a list. Strand of the gene the variant is scored against. Score both strands when it is unknown. Available options: `+`, `-` Chromosome name, e.g. `'1'` or `'chr1'`. A `chr` prefix is added or dropped to match the reference FASTA's naming. Variant position, 1-based (VCF convention). Reference allele, e.g. `'A'` or `'AC'` (DNA bases A/C/G/T/N). Alternate allele, e.g. `'G'` or `'GTT'` (DNA bases A/C/G/T/N).

Source Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). `True` is coerced to `1` and `False` to `0`. Device to run the model on (default `cuda`). Maximum execution time in seconds. `None` waits indefinitely. Random seed. When set, tools run reproducibly up to small GPU float noise (see `BaseToolOutput.approx_equal`), and the seed participates in cache keys. When None, cacheable seed-sensitive tools skip cache until seeded. The reference genome the sequence around each variant is read from. Either a provisioned assembly name (`'grch38'`/`'grch37'`, or `'hg38'`/`'hg19'`), downloaded on first use, or a path to a FASTA on this machine. Required at call time; `None` raises a `MissingAssetError`. Species assembly whose channel conditions the model. `GRCh38` (human) for human sequence, including GRCh37 coordinates. Available options: `GRCh38`, `NHGRI_mPanTro3-v2.0`, `Panubis1.0`, `T2T-MFA8v1.1`, `Mmul_10`, `ARS-UCD2.0`, `ARS-UI_Ramb_v3.0`, `Sscrofa11.1`, `GRCm38`, `GRCr8`

Source Per-variant predictions, 1:1 with the input variants and in the same order. Variant chromosome. Variant position (1-based). Reference allele. Alternate allele. Gene strand the variant was scored against. Ten strongest new or strengthened donors. Ten strongest lost or weakened donors. Ten strongest new or strengthened acceptors. Ten strongest lost or weakened acceptors. Ten strongest new or strengthened junctions. Ten strongest lost or weakened junctions. Per-variant summary score. **Metrics** (one set per `results` item) | Metric | Type | Range | Availability | | - | - | - | - | | `summary_score` | float | 0.0 to 1.0 | always |

#### Applications Use this to triage candidate variants from a sequencing study for splicing impact, including deep-intronic variants that create new splice sites, or to rank variants of uncertain significance where a coding effect is absent. The TSV export uses the column layout of the upstream `.spliceai2` output, so existing downstream scripts can read it. #### Usage Tips * **`strand` is required for every variant.** It is the strand of the gene the variant is scored against. When it is unknown, score the variant on both strands as two separate entries and aggregate the predictions (as the upstream README recommends). * **Filter on `summary_score` with the upstream thresholds.** 0.1 for high recall, 0.25 to balance precision and recall, 0.5 for high precision (the SpliceAI equivalents are 0.2 / 0.5 / 0.8). The summary score is the maximum of the strongest donor gain, donor loss, acceptor gain, and acceptor loss delta scores; junction effects are reported but excluded from it. * **`reference_fasta` is required.** Pass `'grch38'` (or `'grch37'`; `'hg38'`/`'hg19'` also work), which is downloaded on first use, or a path to a local FASTA. A `chr` prefix on `chromosome` is added or dropped to match the FASTA (`'chr1'` and `'1'` both work), and `ref` must match the genome at `position`. * **Keep `assembly='GRCh38'` for any human variant, including GRCh37 coordinates.** `assembly` selects the species channel the model is conditioned on, not the coordinate system; set it to another training assembly only for non-human sequence. * **The scoring geometry is fixed.** Each allele is scored in a 196,608 bp window centered on the variant, changes are reported within ±32,768 bp, both alleles share 1,024 candidate splice sites, and predictions average the two-model ensemble. These are the values the model was trained and evaluated with, so they are not configurable. Positions beyond a chromosome end are padded with all-zero (unknown) input.

### SpliceAI2 Splice and Transcript Prediction (`spliceai2-predict`) Predicts per-position donor and acceptor usage along one or more sense-strand DNA sequences, the junctions between candidate splice sites, and the highest-scoring decoded transcripts. No reference genome is needed. #### API Reference

Source Sense-strand DNA sequence(s) to predict on. A single string is auto-wrapped into a list. Any length is accepted: the model's 65,536 bp of context per side is padded with unknown bases, so predictions cover every input position.

Source Number of highest-scoring transcripts to decode per sequence. Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). `True` is coerced to `1` and `False` to `0`. Device to run the model on (default `cuda`). Maximum execution time in seconds. `None` waits indefinitely. Random seed. When set, tools run reproducibly up to small GPU float noise (see `BaseToolOutput.approx_equal`), and the seed participates in cache keys. When None, cacheable seed-sensitive tools skip cache until seeded. Species assembly whose channel conditions the model. `GRCh38` (human) for human sequence. Available options: `GRCh38`, `NHGRI_mPanTro3-v2.0`, `Panubis1.0`, `T2T-MFA8v1.1`, `Mmul_10`, `ARS-UCD2.0`, `ARS-UI_Ramb_v3.0`, `Sscrofa11.1`, `GRCm38`, `GRCr8`

Source One entry per input sequence, in input order. Per-position splice donor usage (0-1), scored at the first base of each intron. Per-position splice acceptor usage (0-1), scored at the last base of each intron. Junctions with usage >= 0.01 between candidate splice sites, ordered by donor then acceptor. Highest-scoring transcripts, best first. Empty when fewer than two candidate splice sites are found.

#### Applications Use this to map the splice-usage landscape of a gene or locus, to see which junctions and isoforms a sequence is predicted to produce, or to compare reference and edited versions of a sequence (predict each separately and diff the outputs). It also suits designed constructs, minigenes, and saturation scans where no genome coordinates exist. #### Usage Tips * **Embed short constructs in genomic context where possible.** Every input is padded with 65,536 bp of unknown (all-zero) context per side, so predictions cover every input position at any length. Isolated short sequences fall outside what the model was evaluated on (it always saw real flanking genome), so prefer including the surrounding genomic sequence and reading predictions over the region of interest. * **Coordinates are 1-based and intron-anchored.** Donor usage is scored at the intron's first base and acceptor usage at its last base; each junction's `donor` and `acceptor` are those intron boundary positions. Transcript `exons` are 1-based inclusive intervals. * **Transcript ends are not predicted.** The first and last exon of every decoded transcript extend to the start and end of the input sequence, because SpliceAI2 does not model transcription start or end sites. `num_transcripts` (default 3) sets how many top-scoring paths are decoded; duplicates are dropped, so fewer may be returned, and none are returned when fewer than two candidate sites clear 0.01 usage. * **Junctions are reported at usage >= 0.01** between candidate sites (positions whose donor or acceptor usage exceeds 0.01), ordered by donor then acceptor. * **`N` bases are encoded as `A`, matching upstream.** Ambiguous bases are read as adenine rather than as missing sequence, so avoid `N` runs in regions you care about; only the padding outside the input is all-zero. * **Use `assembly='GRCh38'` for any human sequence.** It selects the species channel the model is conditioned on; choose another training assembly only for non-human sequence. * **GPU memory grows with input length.** Peak memory is about 5 GiB plus 4 GiB per 100 kb of input (measured on an H100: 13 GiB at 190 kb, 44 GiB at 1 Mb, 63 GiB at 1.5 Mb), so inputs longer than about 1.8 Mb do not fit on an 80 GB GPU, and smaller GPUs reach their limit proportionally sooner. For a long locus, predict overlapping windows that each keep genomic flanking sequence.