Abstract
We recently developed the rG4-seq method to detect and map in vitro RNA G-quadruplex (rG4s) structures on a transcriptome-wide scale. rG4-seq of purified human HeLa RNA has revealed many non-canonical rG4s and the effects adjacent sequences have on rG4 formation. In this study, we aimed to improve the outcomes and false-positive discrimination in rG4-seq experiments using a bioinformatic approach. By establishing connections between rG4-seq library preparation chemistry and the underlying properties of sequencing data, we identified how to mitigate indigenous sampling errors and background noise in rG4-seq. We applied these findings to develop a novel bioinformatics pipeline named rG4-seeker (https://github.com/TF-Chan-Lab/rG4-seeker), which uses tailored noise models to autonomously assess and optimize rG4 detections in a replicate-independent manner. Compared with previous methods, rG4-seeker exhibited better false-positive discrimination and improved sensitivity for non-canonical rG4s. Using rG4-seeker, we identified novel features in rG4 formation that were missed previously. rG4-seeker provides a reliable and sensitive approach for rG4-seq investigations, laying the foundations for further elucidation of rG4 biology.