OpenSplice
Alternative splicing of mRNA precursors is an important step in gene regulation and a major mechanism by which genetic variants cause human disease.  However, changes in splicing have only been quantified for a tiny fraction of possible variants in the human genome, limiting our ability to interpret clinical variants, evaluate and develop machine learning models, and understand the splicing regulatory code.  Here, to start addressing this data gap, we present OpenSplice, a well-calibrated experimental dataset that quantifies the impact of >590,000 variants on the splicing of >600 human alternatively spliced exons.  OpenSplice measures changes in inclusion for all possible exonic and proximal intronic single nucleotide (nt) substitutions, as well as all 1, 3, 6 and 21nt deletions in minigene constructs.  More than half of variants affected exon inclusion, with a bias towards decreasing inclusion. We use OpenSplice to evaluate the performance of deep learning splicing variant effect predictors to identify splice-altering clinical variants and to map the rich and diverse regulatory architectures of exons. Exons and introns vary from dense regulatory landscapes dominated by enhancers or silencers, to checkboard architectures with interleaved enhancers and silencers, and sparse architectures with limited regulatory content. Expanding OpenSplice genome-wide will produce a complete atlas of variants that impact splicing, a rich testing and training dataset for machine learning models, and a comprehensive map of regulatory elements for mechanistic studies.
Alternative splicing of mRNA precursors is an important step in gene regulation and a major mechanism by which genetic variants cause human disease.  However, changes in splicing have only been quantified for a tiny fraction of possible variants in the human genome, limiting our ability to interpret clinical variants, evaluate and develop machine learning models, and understand the splicing regulatory code.  Here, to start addressing this data gap, we present OpenSplice, a well-calibrated experimental dataset that quantifies the impact of >590,000 variants on the splicing of >600 human alternatively spliced exons.  OpenSplice measures changes in inclusion for all possible exonic and proximal intronic single nucleotide (nt) substitutions, as well as all 1, 3, 6 and 21nt deletions in minigene constructs.  More than half of variants affected exon inclusion, with a bias towards decreasing inclusion. We use OpenSplice to evaluate the performance of deep learning splicing variant effect predictors to identify splice-altering clinical variants and to map the rich and diverse regulatory architectures of exons. Exons and introns vary from dense regulatory landscapes dominated by enhancers or silencers, to checkboard architectures with interleaved enhancers and silencers, and sparse architectures with limited regulatory content. Expanding OpenSplice genome-wide will produce a complete atlas of variants that impact splicing, a rich testing and training dataset for machine learning models, and a comprehensive map of regulatory elements for mechanistic studies.
Centre for Genomic Regulation