R-loopBase v2.0 发布：提供物种与细胞类型分辨的定量 R-loop 图谱及调控资源
R-loopBase v2.0: species- and cell type-resolved quantitative R-loop maps and R-loop regulatory resources
R-loopBase v2.0 发布，将原先仅覆盖人类、只给出分类置信度的 R-loop 图谱升级为定量评分框架，并扩展到特定细胞类型及小鼠、果蝇和酵母。研究团队统一重新分析了 16 种技术产生的 358 套 R-loop 制图数据集，构建的定量评分在人类全基因组范围内与已知 R-loop 生物学特征高度一致。同时大幅扩充了跨物种 R-loop 调控因子集合，可用于推断 R-loop 稳态变化。新版本还加入扩展的多物种基因组浏览器、基于深度学习的 R-loop 预测、R-loop 稳态分析和简化的数据下载入口，数据库访问地址为 rloopbase.nju.edu.cn。
来源：Nucleic Acids Research · doi.org
R-loops are non-canonical nucleic acid structures implicated in diverse cellular processes and diseases. We previously developed R-loopBase to address the pressing challenges of inaccurate R-loop maps and the limited understanding of the R-loop regulatory network. However, R-loopBase provided only human R-loop maps with categorical confidence and did not account for cell type specificity. These limitations, coupled with the emerging R-loop profiling technologies and advances in R-loop regulatory mechanisms, have motivated the development of R-loopBase v2.0 (https://rloopbase.nju.edu.cn). After uniformly re-analysing 358 R-loop mapping datasets generated by 16 technologies, we introduce a quantitative R-loop scoring framework that assesses genome-wide R-loop confidence in human, demonstrating strong concordance with known R-loop biology. We further extend the framework to specific cell types and mouse, fruit fly, and yeast. In parallel, we substantially expand the collection of R-loop regulators across species, enabling the inference of changes in R-loop homeostasis. Building on these expanded datasets, we implement several new interactive features and analytical tools, including an expanded multi-species genome browser, deep learning-based R-loop prediction, R-loop homeostasis analysis, and a streamlined data download portal. We anticipate that R-loopBase v2.0 will serve as an enhanced, up-to-date one-stop resource for the R-loop research community.