Aberrant splicing prediction across human tissues

AbSplice predicts aberrant splicing for 50 human tissues by integrating sequence-based deep learning models, DNA variation and RNA-seq obtained from accessible tissues. Aberrant splicing is a major cause of genetic disorders but its direct detection in transcriptomes is limited to clinically accessible tissues such as skin or body fluids. While DNA-based machine learning models can prioritize rare variants for affecting splicing, their performance in predicting tissue-specific aberrant splicing remains unassessed. Here we generated an aberrant splicing benchmark dataset, spanning over 8.8 million rare variants in 49 human tissues from the Genotype-Tissue Expression (GTEx) dataset. At 20% recall, state-of-the-art DNA-based models achieve maximum 12% precision. By mapping and quantifying tissue-specific splice site usage transcriptome-wide and modeling isoform competition, we increased precision by threefold at the same recall. Integrating RNA-sequencing data of clinically accessible tissues into our model, AbSplice, brought precision to 60%. These results, replicated in two independent cohorts, substantially contribute to noncoding loss-of-function variant identification and to genetic diagnostics design and analytics.

Automated summary

AbSplice predicts aberrant splicing in 50 human tissues by integrating deep learning models, DNA variation, and RNA-seq data. It increases prediction precision by mapping and quantifying tissue-specific splice site usage and modeling isoform competition. By incorporating RNA-sequencing data from clinically accessible tissues, precision in aberrant splicing prediction is improved to 60%.