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Splice Site Prediction

Definition
AI-generated

Splice site prediction uses statistical or machine-learning models (including deep neural networks) to score how likely a DNA or RNA sequence acts as a splice donor, acceptor, or branch point, and to estimate how variants may alter splicing.

Topics
Post-GWAS
Related
Alternative Splicing Foundation Model Loss-of-Function Variant

Why it matters in GWAS

Fine-mapping often surfaces intronic or synonymous variants; in silico splice scores help prioritize candidates for functional follow-up and rare-variant burden tests when splice disruption is plausible. Calibrated tools should be interpreted alongside tissue context and known isoforms.

Example usage

"The methods section includes Splice Site Prediction to support interpretation of the primary findings."

Alternative Splicing Foundation Model Loss-of-Function Variant Splice-Site Variant Variant Annotation
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Jaganathan K, et al. (2019). Predicting splicing from primary sequence with deep learning. Cell.
  • Ji HJ, Pertea M, Salzberg SL. (2026). Annotating genomes at increased scale and resolution. Nat Rev Genet. https://doi.org/10.1038/s41576-026-00937-3
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