Integrative transcriptomics to uncover functional elements and disease-associated variants in RNA

项目概要： 有必要进行综合数据分析，锚转录组学研究的背景下预测 人类健康，如对疾病相关同义转录变体的认识不断提高所示 和RNA生物技术，如mRNA疫苗。为了帮助发现重要的序列特征 对于RNA调控，我们提出了翻译效率的上下文依赖模型，这是一个关键的衡量标准。 转录功能。我们发现，位置依赖密码子使用偏好（PDCUB）识别起始密码子 在AUG中比Kozak序列更一致，而高PDCUB转录本富含 与人类发育和神经功能相关的重要医学基因。基于注意力的Transformer 网络和解释技术将独立预测人类翻译效率 转录本，与多种人类细胞系中的核糖体分析和RNA丰度数据进行比较， 以表征PDCUB和其他序列特征如何指导跨健康的翻译效率- 关键上下文。转染测定验证预测的序列特征的作用。 除了序列，更高级的结构也驱动RNA的功能和稳定性，包括 翻译调控和与microRNA和RNA结合蛋白（RBP）的相互作用。新的RNA 结构比对方法和相关聚类将揭示结构域并将它们分组 通过相互相似性来寻找影响RNA结构-功能关系的共同结构基序， 提高我们对转录本结构在发病机制中的作用的理解。评价将包括 在我们以前建立的RNA结构元数据库bpRNA-1m中对RNA家族进行聚类， 在核糖体分析数据的背景下分析结构域，以表征这些结构域的作用。 域在调节翻译。同时，根据RNA-蛋白质交联， 这些数据将使我们能够识别参与RBPs结合的基序。 最后，一个全面的转录组浏览器和元数据库将整合转录组 已知和新的转录水平特征的数据，包括上述那些。容易访问和 使用，这一资源将使科学和医学研究人员能够找到和定义RNA序列特征 和结构图案。通过对转录水平相互作用的复杂方面进行内聚编目，沿着 与转录调控相关的序列和结构特征，我们的转录组浏览器 帮助研究人员可视化核糖体占据，检查RNA结构，microRNA和RBP结合， 目录剪接变异，并了解驱动转录相互作用的序列特征。等位基因 映射到RNA转录位置的变体将结合我们的注释，沿着与基于特征的 将机器学习预测整合到浏览器中，以帮助研究人员生成第一遍 预测转录变体并在人类健康的背景下解释其结果。

PROJECT SUMMARY: There is a need for integrative data analyses that anchor transcriptomic research in contexts predictive of human health, as illustrated by growing awareness of disease-associated synonymous transcript variants and RNA biotechnologies such as mRNA vaccines. To help uncover sequence features that are important for RNA regulation, we present context-dependent models of translational efficiency, a key metric of transcript function. We show that position-dependent codon usage bias (PDCUB) identifies start codons among AUGs more consistently than the Kozak sequence, while high-PDCUB transcripts are enriched for medically important genes tied to human development and neural function. Attention-based transformer networks and interpretation techniques will independently predict translational efficiency in human transcripts, with comparison to ribosome profiling and RNA abundance data in multiple human cell lines, to characterize how PDCUB and other sequence features guide translational efficiency across health- critical contexts. Transfection assays validate the roles of predicted sequence features. Beyond sequence, higher-order structures also drive RNA function and stability, including translational regulation and interactions with microRNAs and RNA-binding proteins (RBPs). A new RNA structural alignment method and associated clustering will uncover structural domains and group them by mutual similarity to find common structural motifs that impact RNA structure-function relationships, improving our understanding of the role of transcript structure in pathogenesis. Evaluation will consist of clustering RNA families in our previously built RNA structure meta-database, bpRNA-1m, with identified structural domains analyzed in the context of ribosome profiling data to characterize the role of these domains in regulating translation. Meanwhile, clustering structures according to RNA-protein crosslinking data will let us identify motifs involved in the binding of RBPs. Finally, a comprehensive transcriptome browser and meta-database will integrate transcriptomic data for known and new transcript-level features, including those described above. Easy to access and use, this resource will enable scientific and medical researchers to find and define RNA sequence features and structural motifs. By cohesively cataloging the complex facets of transcript-level interactions, along with sequence and structural features relevant for transcript regulation, our transcriptome browser will help researchers visualize ribosomal occupancy, examine RNA structures, microRNA and RBP binding, catalog splice variants, and understand the sequence features that drive transcript interactions. Allelic variants mapped to RNA transcript positions will be combined our annotations, along with feature-based machine learning predictions incorporated into the browser, to assist researchers in generating first-pass predictions of transcript variants and interpreting their outcomes in the context of human health.