Predictive Identification of Exonic Splicing Enhancers in Human Genes

Key Points

• To develop a computational framework for predicting exonic splicing enhancer motifs and assessing their regulatory roles in human pre-mRNA splicing.
• Developed RESCUE-ESE, a computational tool using statistical analysis of exon-intron boundaries and splice site sequence composition across large human gene datasets.
• Conducted in vivo experimental assays on predicted motifs and corresponding point mutants to evaluate splicing enhancer activity.
• Identified 10 predicted exonic splicing enhancer (ESE) motifs across human genomic sequences.
• Demonstrated that representative sequences from all 10 motifs exhibit functional enhancer activity in vivo.
• Showed that point mutations in these motifs sharply reduce enhancer activity, enabling accurate prediction of splicing phenotypes caused by exonic mutations.