Using synthetic RNA to benchmark poly(A) length inference from direct RNA sequencing

Polyadenylation is a dynamic process that is important in cellular physiology, which has implications in messenger RNA decay rates, translation efficiency, and isoform-specific regulation. Oxford Nanopore Technologies direct RNA sequencing provides a strategy for sequencing the full-length RNA molecule and analysis of the transcriptome. Several tools are currently available for poly(A) tail length estimation, including well-established methods like tailfindr and nanopolish, as well as more recent deep learning models like Dorado. However, there has been limited benchmarking of the accuracy of these tools against gold-standard datasets. In this article, we present our novel deep learning poly(A) estimation tool-BoostNano-and compare with 3 existing tools-tailfindr, nanopolish, and Dorado. We evaluate the 4 poly(A) estimation tools, using 2 sets of synthetic in vitro transcribed RNA standards with known poly(A) tail lengths-Sequin (30 or 60 nucleotides) and enhanced green fluorescent protein (10-150 nucleotides) RNA. Analyzing datasets with known ground-truth values is a valuable approach to measuring the accuracy of poly(A) length estimation. The tools demonstrated length- and sample-dependent performance, and accuracy was enhanced by averaging over multiple reads via estimation of the peak of the density distribution. Overall, Dorado is recommended as the preferred approach due to its relatively fast runtimes, low mean error, and ease of use with integration with base-calling. These results provide a reference for poly(A) tail length estimation analysis, aiding in improving our understanding of the transcriptome and the relationship between poly(A) tail length and other transcriptional mechanisms, including transcript stability or quantification.