PolyAMiner-Bulk: A Machine Learning Based Bioinformatics Algorithm to Infer and Decode Alternative Polyadenylation Dynamics from bulk RNA-seq data.

ABSTRACT: More than half of human genes exercise alternative polyadenylation (APA) and generate mRNA transcripts with varying 3' untranslated regions (UTR). However, current computational approaches for identifying cleavage and polyadenylation sites (C/PASs) and quantifying 3'UTR length changes from bulk RNA-seq data fail to unravel tissue- and disease-specific APA dynamics. Here, we developed a next-generation bioinformatics algorithm and application, PolyAMiner-Bulk, that utilizes an attention-based machine learning architecture and an improved vector projection-based engine to infer differential APA dynamics accurately. When applied to earlier studies, PolyAMiner-Bulk accurately identified more than twice the number of APA changes in an RBM17 knockdown bulk RNA-seq dataset compared to current gen

SUBMITTER: Jonnakuti VS 

PROVIDER: S-EPMC9900750 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature