EAGER: Algorithmic frameworks and resources for mapping RNA modifications from single molecule direct RNA-sequencing data

EAGER：用于从单分子直接 RNA 测序数据映射 RNA 修饰的算法框架和资源

• 批准号: 1940422
• 负责人: Sarath Chandra Janga
• 金额: $ 29.97万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940422&HistoricalAwards=false
• 关键词: EAGER; Algorithmic; frameworks; resources; mapping

A basic question in cell biology is to understand the driving mechanisms that control how and when genes are expressed, and to identify the active switches in those processes. The first step of gene expression is production of an RNA molecule from the genomic DNA, "transcription". As instruments become available that allow detection of the original RNA molecules from cells, it is becoming possible to identify sites where RNA bases have been chemically modified after their initial transcription. This is important because some of these post-transcriptional modifications play a role in how the expressed RNA is translated into expressed protein. Little is known as yet about the molecular players that are involved in the myriad steps that govern expression patterns, including localization, splicing, stability and folded structure of the RNA. This project aims to detect, identify and quantify the extent of modifications on RNA molecules as measured on the Oxford Nanopore platform, as a required first step in understanding those biological functions. Gold-standard calibration sets of synthetic oligonucleotides will be designed, produced and tested as part of the experimental design, and new algorithms and subsequent software will provide single-nucleotide resolution of the type and locations of robustly detected modifications in natural transcripts in yeast and human data sets. Lack of efficient high throughput detection methods has plagued the emerging field of epitranscriptomics, which is focused on the role of chemical modifications on RNA bases in modulating the biological function and structure of RNA molecules. The overarching research goal of this project is to develop computational methods to map RNA modification sites for 5-methyl cytosine (5mC), 1-methyl adenosine (m1A) and methylation of the backbone of the RNA nucleotides (Nm) at a single nucleotide resolution. Experiments will employ synthetic calibration oligonucleotides as well as use newly developed algorithms to probe natural yeast and human transcripts, using the long-read direct RNA sequencing data resulting from Oxford Nanopore sequencing technology. The project will complement current transcriptomic reference maps of these modification events with additional data needed to train computational methods, from gold-standard calibration sets composed of synthetic RNA oligonucleotides. The resulting Oxford Nanopore signatures of modification sites will be analyzed using deep learning for signal analysis and statistical methods for robustness in precision and accuracy. All resulting methods, databases and maps of RNA modification types across species will be made publicly available from the project web site. The research program involves a team whose expertise intersects several domains of science, including engineering, bioinformatics, genomics and computational science, providing an excellent environment and experience for developing a new generation of inter-disciplinary scientists. Data, code and other infrastructure resources will be reported at http://www.iupui.edu/~jangalab/.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

细胞生物学的一个基本问题是了解控制基因表达方式和时间的驱动机制，并识别这些过程中的主动开关。基因表达的第一步是从基因组 DNA 产生 RNA 分子，即“转录”。随着能够检测细胞中原始 RNA 分子的仪器的出现，识别 RNA 碱基在初始转录后被化学修饰的位点变得可能。这很重要，因为其中一些转录后修饰在表达的 RNA 翻译成表达的蛋白质的过程中发挥着重要作用。目前我们对参与控制表达模式的无数步骤（包括 RNA 的定位、剪接、稳定性和折叠结构）的分子参与者知之甚少。该项目旨在检测、识别和量化在 Oxford Nanopore 平台上测量的 RNA 分子的修饰程度，这是了解这些生物功能所需的第一步。作为实验设计的一部分，将设计、生产和测试合成寡核苷酸的金标准校准集，新的算法和后续软件将提供酵母和人类数据集中天然转录本中可靠检测到的修饰的类型和位置的单核苷酸分辨率。缺乏有效的高通量检测方法一直困扰着新兴的表观转录组学领域，该领域的重点是RNA碱基化学修饰在调节RNA分子的生物学功能和结构中的作用。该项目的总体研究目标是开发计算方法，以单核苷酸分辨率绘制 5-甲基胞嘧啶 (5mC)、1-甲基腺苷 (m1A) 和 RNA 核苷酸主链甲基化 (Nm) 的 RNA 修饰位点。实验将采用合成校准寡核苷酸，并使用新开发的算法来探测天然酵母和人类转录本，并使用牛津纳米孔测序技术产生的长读直接RNA测序数据。该项目将补充这些修饰事件的当前转录组参考图，以及训练计算方法所需的额外数据，这些数据来自由合成 RNA 寡核苷酸组成的金标准校准集。由此产生的修饰位点的牛津纳米孔特征将使用深度学习进行信号分析和统计方法进行分析，以确保精度和准确性的鲁棒性。所有由此产生的跨物种 RNA 修饰类型的方法、数据库和图谱将在项目网站上公开提供。 该研究项目涉及的团队的专业知识跨越多个科学领域，包括工程、生物信息学、基因组学和计算科学，为培养新一代跨学科科学家提供了良好的环境和经验。数据、代码和其他基础设施资源将在 http://www.iupui.edu/~jangalab/ 上报告。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。