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SpliceVault predicts the precise nature of variant-associated mis-splicing.

Ruebena DawesAdam M BournazosSamantha J BryenShobhana BommireddipalliRhett G MarchantHimanshu JoshiSandra T Cooper
Published in: Nature genetics (2023)
Even for essential splice-site variants that are almost guaranteed to alter mRNA splicing, no current method can reliably predict whether exon-skipping, cryptic activation or multiple events will result, greatly complicating clinical interpretation of pathogenicity. Strikingly, ranking the four most common unannotated splicing events across 335,663 reference RNA-sequencing (RNA-seq) samples (300K-RNA Top-4) predicts the nature of variant-associated mis-splicing with 92% sensitivity. The 300K-RNA Top-4 events correctly identify 96% of exon-skipping events and 86% of cryptic splice sites for 140 clinical cases subject to RNA testing, showing higher sensitivity and positive predictive value than SpliceAI. Notably, RNA re-analyses showed we had missed 300K-RNA Top-4 events for several clinical cases tested before the development of this empirical predictive method. Simply, mis-splicing events that happen around a splice site in RNA-seq data are those most likely to be activated by a splice-site variant. The SpliceVault web portal allows users easy access to 300K-RNA for informed splice-site variant interpretation and classification.
Keyphrases
  • rna seq
  • single cell
  • nucleic acid
  • machine learning
  • gene expression
  • cystic fibrosis
  • pseudomonas aeruginosa
  • artificial intelligence