Dissecting epitranscriptomic signal from complex tissues

标题：剖析复杂组织的表观转录组信号 项目概要： 近年来，“RNA表观遗传学”或“表观转录组学”成为一项令人兴奋且活跃的研究 研究基因表达转录后调控的领域。 RNA修饰发挥着重要作用 参与基因表达调控，并参与神经发育和许多神经系统疾病。 甲基化RNA免疫沉淀测序（MeRIP-seq）是一项新开发的技术，用于 RNA 表观遗传修饰的转录组分析。 MeRIP-seq 导致扩展 在基础研究和临床研究中都有应用，但以其独特的特性在分析中面临着许多挑战 数据特征，包括 1) 由于序列内容和样本而存在技术假象 MeRIP特有的准备程序； 2）缺乏适当的方法来识别和比较 RNA甲基化区域； 3）缺乏解释组织样本异质性的方法。 在本提案中，我们将解决这些挑战并开发一系列新颖的统计方法 MeRIP-seq 数据预处理和分析。它们包括一个数据标准化程序来删除 技术偏差、准确有效的 RNA 甲基化位点检测和比较方法以及信号 反卷积方法根据组织样本的数据得出细胞类型特定的推论。所有方法 该项目中开发的产品将作为免费开源软件实施和发布，以造福于 表观基因组学研究团体，包括从事遗传学、表观遗传学、基因研究的基础科学家 调节和细胞发育，以及临床医生寻找疾病生物标志物。

Title: Dissecting epitranscriptomic signal from complex tissues PROJECT SUMMARY: “RNA epigenetics” or “epitranscriptomics” has emerged in recent years as an exciting and active research field to study post-transcriptional regulation of gene expressions. The RNA modifications play important roles in gene expression regulation, and are involved in neurodevelopment and a number of neurological diseases. Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq) is a newly developed technology for transcriptome-wise profiling of the RNA epigenetic modifications. MeRIP-seq leads to an expansion of applications in both basic and clinical research, but faces a number of challenges in analysis with its unique data characteristics, including 1) the presence of technical artifacts due to sequence content and sample preparation procedures unique to MeRIP; 2) lack of appropriate methods for identification and comparison of RNA methylated regions; and 3) lack of methods to account for the heterogeneity of tissue samples. In this proposal, we will address these challenges and develop a series of novel statistical methods for MeRIP-seq data preprocessing and analyses. They include a data normalization procedure to remove technical bias, accurate and efficient methods for RNA methylation site detection and comparison, and signal deconvolution methods to draw cell type specific inferences based on data from tissue samples. All methods developed in this project will be implemented and released as free, open source software to benefit the epigenomics research community, including basic scientists working on genetics, epigenetics, gene regulation and cell development, as well as clinicians looking for disease biomarkers.