Abstract
Single nucleotide variants (SNVs) within polyadenylation signals (PASs), a specific six-nucleotide sequence required for mRNA maturation, can impair RNA-level gene expression and cause human diseases. However, there is a lack of genome-wide investigation and systematic confirmation tools for identifying PAS variants. Here, we present a computational strategy to integrate the most reliable resources for discovering distinct genomic features of PAS variants and also develop a credible and convenient experimental tool to validate the effect of PAS variants on expression of disease-associated genes. This approach will greatly accelerate the deciphering of PAS variation-related human diseases.
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