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### SpliceTransformer Splicing Prediction (`splice-transformer-prediction`) Predicts splice sites at single-nucleotide resolution across a batch of target sequences and reports tissue-specific usage for each predicted site. The tool accepts one or more 1,000 nucleotide target sequences, each paired with a 4,000 nucleotide left context and a 4,000 nucleotide right context drawn from the same genomic locus, and returns a probability tensor of shape `[batch, 1000, 18]`. The first three channels give the probability that a position is neither a splice site, an acceptor, or a donor, and the remaining fifteen channels give the predicted usage of that position as a splice site in each tissue. #### API Reference

Source RNA or DNA sequence(s) on which to make splicing predictions. These are the central sequences where splice sites will be predicted at single-nucleotide resolution. All sequences in the batch should have the same length (typically 1000bp). Sequence(s) providing left (5') context for each target sequence. Must have the same number of sequences as `target_seqs`. All left context sequences must have the same length (typically 4000bp) to provide sufficient context for accurate prediction. Sequence(s) providing right (3') context for each target sequence. Must have the same number of sequences as `target_seqs`. All right context sequences must have the same length (typically 4000bp) matching the left context.

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. Override of `BaseConfig.device` because SpliceTransformer is a GPU tool (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.

Source Prediction tensor of shape `[batch, target_length, 18]`

#### Applications This tool is appropriate for any analysis that begins with a genomic locus and asks where the splice sites are and how their usage differs between tissues. Representative applications include annotating candidate splice sites in a newly characterised gene, comparing predicted acceptor and donor usage between tissues such as brain and liver to identify tissue-specific isoforms, and screening a region for positions whose splicing behaviour is restricted to a particular tissue. The fifteen tissue channels make the tool well suited to studies of [alternative splicing](https://en.wikipedia.org/wiki/Alternative_splicing) where the tissue of interest is known in advance. #### Usage Tips * **Sequence lengths are fixed by the published model and are enforced on input.** Every target sequence must be exactly 1,000 nucleotides and every left and right context must be exactly 4,000 nucleotides. Inputs of any other length are rejected before the model runs. * **The target and its two contexts must come from the same genomic locus and be supplied in genomic order.** The model concatenates the left context, the target, and the right context into a single window, so a context drawn from a different region or assembled in the wrong order produces predictions that do not correspond to the intended locus. * **The three input lists must contain the same number of sequences.** Each target is paired by position with one left context and one right context, and a mismatch in list length is rejected on input. * **Acceptor and donor probabilities are most informative when read against the canonical [GT-AG rule](https://en.wikipedia.org/wiki/RNA_splicing).** Confident donor predictions are expected at GT dinucleotides and confident acceptor predictions at AG dinucleotides, so inspecting these positions helps confirm that a high score reflects a genuine splice site. * **Tissue channels report usage rather than the presence of a splice site.** A position can carry a high acceptor or donor probability while showing strong usage in only a few tissues, so differential analysis across the tissue channels is the basis for identifying tissue-specific splicing. * **The model was trained on human sequence and is intended for human loci.** Predictions on sequences from other species are not supported by the training data and should not be interpreted as reliable.