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

项目摘要/摘要 最近基于核糖体图谱(ribo-seq)技术的研究揭示了一种意想不到的、复杂的 后生动物中的翻译景观，在传统的带注释的翻译之外进行了广泛的翻译 事件。其中一些新的开放阅读框架(ORF)编码的多肽是由未注释的 翻译事件已被证明发挥着重要的发育或生理作用。然而， 大多数未加注释的ORF的功能仍然未知，解码其功能的关键第一步 是对那些正在进行主动翻译的人进行系统的分类。Ribo-Seq数据可以说提供了最好的 这项任务的信息来源，考虑到RIBO-SEQ技术的全基因组覆盖率和敏感性。 存在基于不同翻译抑制物使用的RIBO-SEQ技术的变体。 常规rRibo-seq(rRibo-seq)使用平移延伸抑制剂环己亚胺(CHX)冻结所有 翻译核糖体。与CHX相反，使用翻译抑制剂三尖杉酯碱或乳霉素，这是 对捕获起始核糖体有更强的作用，使翻译的全球图谱成为可能 起始点(TIS)通过测序(TI-SEQ)。尽管rRibo-seq具有广泛的适用性和广泛的采用率 和TI-SEQ，一个全面和集成的计算平台，使新的从头预测成为可能 来自不同类型的RIBO-SEQ数据的ORF，并允许交互探索、可视化和元数据 缺乏对内部和公共可用的RIBO-SEQ数据集的分析，以研究未加注释的ORF。为了填满这个 GAP，我们建议开发一个集成的计算平台来促进对未注释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件： 首先，一个新的计算工具包，提供了一个全面的信息化解决方案，无论是低级别的还是高级的-- 对来自不同类型的RIBO-SEQ实验的数据进行水平分析；第二，计算框架 支持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信号跟踪各个数据集；第三，Web数据门户动态更新和分析 已公布的来自后生动物、植物和真菌的ribo-seq数据集，并允许普通用户执行meta-seq 跨不同数据集、物种和生物背景的未注释ORF分析。此外，作为一个 生物应用，我们将把这个计算平台与大尺度和小尺度结合起来 发现新的ORF的实验方法，其表达受雌激素调节， 对雌激素依赖的细胞增殖或存活具有重要意义，并剖析其分子机制 它们的生物学功能。这里提出的这项研究建立在强大的初步数据基础上。鉴于我们的 在计算和实验生物学方面的专业知识，以及高度互补的专业知识和支持 由德克萨斯大学MD安德森癌症中心、贝勒医学院和德克萨斯大学的合作者提供 我们位于西南部，地理位置优越，是解决这一项目的理想选择。

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.