Replication Data for: \"epialleleR: an R/BioC package for quantifying and analysing low-frequency DNA methylation\"

This data set contains necessary data sets (simulated next-generation sequencing data, preprocessed public next-generation sequencing data, precomputed analysis results) used to evaluate performance of epialleleR (https://github.com/BBCG/epialleleR, http://www.bioconductor.org/packages/epialleleR/) - a computational framework for sensitive detection, quantification and visualisation of low-frequency, mosaic epimutations in methylation sequencing data. All the supplementary R scripts that were used for preparation, testing and analysis of data sets are also provided. For additional information please check epialleleR package README.md file, vignettes or reference citation. Please use TREE VIEW to browse files efficiently
Abstract Constitutional epigenetic silencing of tumour suppressor genes has been detected in a small number of cancer patients. Recent finding have indicated that low-level mosaic methylation of the BRCA1 gene promoter occurs in 5-10% of healthy individuals and is associated with a significantly elevated risk of breast and ovarian cancer. This further suggests that similar mosaic constitutional methylation may occur in other tumour suppressor genes as well, potentially being a significant contributor to cancer burden. However, detection of low-level mosaic epigenetic events requires highly sensitive and robust methodology for methylation analysis. We here present epialleleR, a computational framework for sensitive detection, quantification and visualisation of low-frequency, mosaic epimutations in methylation sequencing data. We provide in-depth analysis of epialleleR performance using simulated and real data sets, as compared to the other three commonly applied tools for methylation assessment, and conclude that linkage to epihaplotype data allows very sensitive detection of low-frequency methylation events.

本数据集包含用于评估epialleleR（https://github.com/BBCG/epialleleR、http://www.bioconductor.org/packages/epialleleR/）性能的全套必要数据，包括模拟下一代测序（next-generation sequencing）数据、经预处理的公共下一代测序数据以及预计算分析结果。epialleleR是一款可在甲基化测序数据中实现低频嵌合表观突变的灵敏检测、定量与可视化的计算框架。本数据集同时附带了用于数据集制备、测试与分析的全部配套R脚本。如需获取更多相关信息，请查阅epialleleR包的“README.md”文件、使用教程或参考文献引用。请通过树状视图（TREE VIEW）高效浏览文件。

摘要 肿瘤抑制基因的结构性表观遗传沉默已在少数癌症患者中被检出。近期研究表明，BRCA1基因启动子的低水平嵌合甲基化在5%至10%的健康个体中存在，且与乳腺癌和卵巢癌的患病风险显著升高密切相关。这一发现进一步提示，其他肿瘤抑制基因中也可能存在类似的结构性嵌合甲基化，其或许是癌症负担的重要潜在驱动因素。然而，检测低水平嵌合表观遗传事件需要具备高灵敏度与稳健性的甲基化分析方法。本研究推出epialleleR，一款可在甲基化测序数据中实现低频嵌合表观突变的灵敏检测、定量与可视化的计算框架。我们通过模拟数据集与真实数据集对epialleleR的性能进行了深度评估，并与另外三款常用的甲基化评估工具进行对比，最终证实：结合表观单倍型数据的关联分析可实现对低频甲基化事件的超高灵敏度检测。