metaAPA: a tool for integration of PolyA site predictions from single-cell and spatial transcriptomics.

Motivation: Single-cell 3'-tagging sequencing, such as that provided by 10x Genomics, can be used to study alternative polyadenylation (APA). APA can affect RNA function, stability, and subcellular localization, thereby influencing development and disease processes. Computational tools based on various algorithms, such as Sierra, polyApipe, and SCAPE, have been developed to infer polyA site positions from scRNA-seq data. However, these methods exhibit significant differences in the number of predicted sites and positional inconsistencies in the sites identified for the same gene, leading to divergent conclusions when analyzing the same data with different tools. Results: We designed two strategies to integrate the outputs of alternative APA tools, enabling users to select appropriate polyA site sets based on their specific needs. Our method can be used to extract high-confidence sites, supported by all tools, as well as putative sites supported by a subset of tools. We find that tools with high sensitivity for detecting APA sites can be usefully augmented by tools with higher positional accuracy but lower sensitivity. We show that our method obtains the expected number of high-confidence sites and that these sites exhibit the expected biological sequence characteristics. Availability and implementation: https://github.com/ManchesterBioinference/metaAPA.