InPAS: An R/Bioconductor Package for Identifying Novel Polyadenylation Sites and Alternative Polyadenylation from Bulk RNA-seq Data.

Background: Alternative cleavage and polyadenylation (APA) is a crucial post-transcriptional gene regulation mechanism that regulates gene expression in eukaryotes by increasing the diversity and complexity of both the transcriptome and proteome. Despite the development of more than a dozen experimental methods over the last decade to identify and quantify APA events, widespread adoption of these methods has been limited by technical, financial, and time constraints. Consequently, APA remains poorly understood in most eukaryotes. However, RNA sequencing (RNA-seq) technology has revolutionized transcriptome profiling and recent studies have shown that RNA-seq data can be leveraged to identify and quantify APA events.

Results: To fully capitalize on the exponentially growing RNA-seq data, we developed InPAS (Identification of Novel alternative PolyAdenylation Sites), an R/Bioconductor package for accurate identification of novel and known cleavage and polyadenylation sites (CPSs), as well as quantification of APA from RNA-seq data of various experimental designs. Compared to other APA analysis tools, InPAS offers several important advantages, including the ability to detect both novel proximal and distal CPSs, to fine tune positions of CPSs using a naïve Bayes classifier based on flanking sequence features, and to identify APA events from RNA-seq data of complex experimental designs using linear models. We benchmarked the performance of InPAS and other leading tools using simulated and experimental RNA-seq data with matched 3'-end RNA-seq data. Our results reveal that InPAS frequently outperforms existing tools in terms of precision, sensitivity, and specificity. Furthermore, we demonstrate its scalability and versatility by applying it to large, diverse RNA-seq datasets.

Conclusions: InPAS is an efficient and robust tool for identifying and quantifying APA events using readily accessible conventional RNA-seq data. Its versatility opens doors to explore APA regulation across diverse eukaryotic systems with various experimental designs. We believe that InPAS will drive APA research forward, deepening our understanding of its role in regulating gene expression, and potentially leading to the discovery of biomarkers or therapeutics for diseases.