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Updated: Jun 13, 2026

Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells
Published on: April 26, 2017
Pathogenic variants that alter protein code often disrupt splicing
Rachel Soemedi1,2, Kamil J Cygan1,2, Christy L Rhine2
1Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA.
Abstract:
The lack of tools to identify causative variants from sequencing data greatly limits the promise of precision medicine. Previous studies suggest that one-third of disease-associated alleles alter splicing. We discovered that the alleles causing splicing defects cluster in disease-associated genes (for example, haploinsufficient genes). We analyzed 4,964 published disease-causing exonic mutations using a massively parallel splicing assay (MaPSy), which showed an 81% concordance rate with splicing in patient tissue. Approximately 10% of exonic mutations altered splicing, mostly by disrupting multiple stages of spliceosome assembly. We present a large-scale characterization of exonic splicing mutations using a new technology that facilitates variant classification and keeps pace with variant discovery.
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