Department of Molecular and Cellular Biology and Genome Center, University of California, Davis, Davis, CA, USA.
Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Univ Lyon, École Normale Supérieure de Lyon, Lyon, France.
EMBO J. 2021 Feb 15;40(4):e106394. doi: 10.15252/embj.2020106394. Epub 2021 Jan 7.
R-loops represent an abundant class of large non-B DNA structures in genomes. Even though they form transiently and at modest frequencies, interfering with R-loop formation or dissolution has significant impacts on genome stability. Addressing the mechanism(s) of R-loop-mediated genome destabilization requires a precise characterization of their distribution in genomes. A number of independent methods have been developed to visualize and map R-loops, but their results are at times discordant, leading to confusion. Here, we review the main existing methodologies for R-loop mapping and assess their limitations as well as the robustness of existing datasets. We offer a set of best practices to improve the reproducibility of maps, hoping that such guidelines could be useful for authors and referees alike. Finally, we propose a possible resolution for the apparent contradictions in R-loop mapping outcomes between antibody-based and RNase H1-based mapping approaches.
R 环是基因组中大量存在的非 B 型 DNA 结构。尽管它们是瞬时形成的，频率也不高，但干扰 R 环的形成或溶解会对基因组的稳定性产生重大影响。要了解 R 环介导的基因组不稳定性的机制，就需要对它们在基因组中的分布进行精确的描述。已经开发了许多独立的方法来可视化和绘制 R 环，但它们的结果有时并不一致，导致混乱。在这里，我们回顾了 R 环作图的主要现有方法，并评估了它们的局限性以及现有数据集的稳健性。我们提供了一套最佳实践，以提高图谱的可重复性，希望这些准则对作者和审稿人都有用。最后，我们提出了一种可能的解决方案，以解决基于抗体和 RNase H1 的作图方法在 R 环作图结果上的明显矛盾。
