Integrative approaches for decoding the function and regulation of unconventional RNA translation

项目概要/摘要 基于核糖体分析（ribo-seq）技术的最新研究揭示了一种意想不到的复杂的 后生动物的翻译景观，广泛的翻译超出了传统的注释翻译 事件这些新的开放阅读框（ORF）编码的多肽中的一些由未注释的 翻译事件已被证明在发育或生理上起重要作用。但 大多数未注释的ORF的功能仍然是未知的，解码它们功能的关键的第一步是 是系统地对那些经历主动翻译的进行分类。Ribo-seq数据可以说提供了最好的 鉴于ribo-seq技术的全基因组覆盖范围和灵敏度，这是这项任务的信息来源。 存在基于使用不同翻译抑制剂的核糖测序技术的变体。 常规核糖序列（rRibo-seq）利用环己酰亚胺（CHX），一种翻译延伸抑制剂， 翻译核糖体。与CHX相反，使用翻译抑制剂三尖杉酯碱或乳酰氨霉素， 具有更强的捕获起始核糖体的作用，使翻译的全局映射成为可能， 起始位点（TIS）的序列测定。尽管rRibo-seq的广泛适用性和广泛采用 和TI-seq，一个全面和集成的计算平台，使从头预测的新的 ORFs从不同类型的ribo-seq数据，并允许交互式探索，可视化和Meta 缺乏对内部的和市场上可获得的ribo-seq数据集的分析来研究未注释的ORF。填补这一 缺口，我们建议开发一个集成的计算平台，以促进未注释的ORF的研究， 使用不同类型的ribo-seq数据的真核生物。这个计算平台将有三个核心组成部分： 首先，一个新的计算工具包，它为低层次和高层次的问题提供了一个全面的信息解决方案， 对来自不同类型的ribo-seq实验的数据的水平分析;第二， 实现用户友好的交互式探索和可视化质量控制和分析结果，以及 ribo-seq信号跟踪单个数据集;第三，网络数据门户，动态更新和分析 已发布的来自后生动物、植物和真菌的ribo-seq数据集，并允许一般用户执行Meta- 跨不同数据集、物种和生物背景的未注释ORF分析。此外，作为 生物应用，我们将结合联合收割机这一计算平台， 实验方法来发现新的ORF，其表达受雌激素调节， 重要的雌激素依赖性细胞增殖或生存，并剖析分子机制 它们的生物学功能。这里提出的研究建立在强有力的初步数据基础上。鉴于我们 在计算和实验生物学的专业知识，以及高度互补的专业知识和支持 由我们来自UT MD安德森癌症中心、贝勒医学院和UT的合作者提供 西南部，我们是解决这个项目的理想地点。

PROJECT SUMMARY/ABSTRACT Recent studies based on ribosome profiling (ribo-seq) technique have revealed an unanticipated, complex translational landscape in metazoans, with extensive translation beyond the conventional annotated translation events. Some of these novel open reading frame (ORF)-encoded polypeptides produced by unannotated translation events have been shown to play important developmental or physiological roles. However, the functions of most unannotated ORFs remain unknown, and the critical first step toward decoding their functions is to systematically catalogue those that undergo active translation. Ribo-seq data arguably provide the best source of information for this task, given the genome-wide coverage and sensitivity of the ribo-seq technique. There are variations of ribo-seq technique that are based on the use of different translational inhibitors. Regular ribo-seq (rRibo-seq) utilizes cycloheximide (CHX), a translation elongation inhibitor, to freeze all translating ribosomes. In contrast to CHX, the use of translation inhibitor harringtonine or lactimidomycin, which has a much stronger effect for capturing the initiating ribosomes, enables global mapping of translation initiating sites (TISs) by sequencing (TI-seq). Despite the broad applicability and wide adoption of rRibo-seq and TI-seq, a comprehensive and integrated computational platform that enables de novo prediction of novel ORFs from different types of ribo-seq data, and allows for interactive exploration, visualization and meta- analysis of in-house and publically available ribo-seq datasets to study unannotated ORFs is lacking. To fill this gap, we propose to develop an integrated computational platform to facilitate the study of unannotated ORFs in eukaryotes using different types of ribo-seq data. This computational platform will have three core components: first, a new computational toolkit that provides a comprehensive informatic solution to both low-level and high- level analysis of data from different types of ribo-seq experiments; second, a computational framework that enables user-friendly interactive exploration and visualization of quality control and analysis results as well as ribo-seq signal tracks of individual datasets; third, a web data portal that dynamically updates and analyzes the published ribo-seq datasets from metazoa, plants and fungi, and allows a general user to perform meta- analysis of unannotated ORFs across different datasets, species and biological contexts. Furthermore, as a biological application, we will combine this computational platform with both large- and small-scale experimental approaches to uncover novel ORFs whose expression is regulated by estrogen and that are important for estrogen-dependent cell proliferation or survival, and to dissect the molecular mechanisms underlying their biological function. The study proposed here builds upon strong preliminary data. Given our expertise in computational and experimental biology, and the highly complementary expertise and support provided by our collaborators from UT MD Anderson Cancer Center, Baylor College of Medicine and UT Southwestern, we are ideally situated to tackle this project.