Protocol for unlocking alternative polyadenylation insights from bulk RNA-seq data with PolyAMiner-Bulk.

Abstract

PolyAMiner-Bulk, a deep-learning-based algorithm to decode alternative polyadenylation (APA) dynamics from bulk RNA sequencing (RNA-seq) data, enables scientists to identify and quantify APA events from processed bulk RNA-seq data. The protocol allows researchers to explore differential APA usage between two conditions and gain a better understanding of post-transcriptional regulatory mechanisms. The major steps involve input data preparation, executing PolyAMiner-Bulk, and interpreting the results. A basic familiarity with pre-processing bulk RNA-seq data and command-line tools is suggested. For complete details on the use and execution of this protocol, please refer to Jonnakuti et al.¹.