Deciphering the impact of genetic variation on human polyadenylation using APARENT2.

ABSTRACT:

Background

3'-end processing by cleavage and polyadenylation is an important and finely tuned regulatory process during mRNA maturation. Numerous genetic variants are known to cause or contribute to human disorders by disrupting the cis-regulatory code of polyadenylation signals. Yet, due to the complexity of this code, variant interpretation remains challenging.

Results

We introduce a residual neural network model, APARENT2, that can infer 3'-cleavage and polyadenylation from DNA sequence more accurately than any previous model. This model generalizes to the case of alternative polyadenylation (APA) for a variable number of polyadenylation signals. We demonstrate APARENT2's performance on several variant datasets, including functional reporter data and human 3' aQTLs from G

SUBMITTER: Linder J 

PROVIDER: S-EPMC9636789 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature