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.

摘要

我们最近开发了rG4-seq方法来检测和定位体外转录组范围内的RNA G四联体（rG4s）结构。纯化的人HeLa RNA的rG4-seq显示了许多非规范性rG4，并且相邻序列对rG4的形成具有影响。在这项研究中，我们旨在使用生物信息学方法改善rG4-seq实验中的结果和假阳性鉴别。通过建立rG4-seq文库制备化学与测序数据的基础属性之间的联系，我们确定了如何减轻rG4-seq中的固有采样误差和背景噪声。我们利用这些发现开发了一个名为rG4-seeker（https://github.com/TF-Chan-Lab/rG4-seeker）的新型生物信息学管道，该管道使用量身定制的噪声模型来自主评估和优化重复样本中的rG4检测。独立的方式。与以前的方法相比，rG4搜寻者对非经典rG4表现出更好的假阳性辨别力和更高的敏感性。使用rG4搜索程序，我们确定了rG4形成中以前没有的新特征。rG4-seeker为rG4-seq研究提供了可靠而灵敏的方法，为进一步阐明rG4生物学奠定了基础。