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

整合转录组学揭示 RNA 中的功能元件和疾病相关变异

• 批准号: 10707989
• 负责人: David Anthony Hendrix
• 金额: $ 30.56万
• 依托单位: OREGON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-22 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707989
• 关键词: Algorithms; Attention; Awareness; Binding; Binding Proteins; Biological; Biological Assay; Biotechnology; Cataloging; Catalogs; Code; Codon Nucleotides; Complex; Custom; Data; Data Analyses; Data Set; Databases; Development; Disease; Elements; Evaluation; Family; Foundations; Genes; Genome; Genomics; Health; Higher Order Chromatin Structure; Human; Human Cell Line; Human Development; In Vitro; Infrastructure; Initiator Codon; Investigation; Life; Location; Machine Learning; Maps; Measures; Medical; Methods; MicroRNAs; Modeling; Neurophysiology - biologic function; Outcome; Output; Pathogenesis; Pattern; Peptide Initiation Factors; Positioning Attribute; Process; Proteins; RNA; RNA Binding; RNA Sequences; RNA Splicing; RNA vaccine; RNA-Binding Proteins; Regulation; Research; Research Personnel; Resources; Ribosomal RNA; Ribosomes; Role; Site; Standardization; Structure; Structure-Activity Relationship; TIE gene; Techniques; Training; Transcript; Transfection; Translational Regulation; Translations; Update; Variant; Visualization; Work; cell type; crosslinking and immunoprecipitation sequencing; genetic variant; improved; insight; intermolecular interaction; mRNA Stability; mRNA Translation; machine learning method; machine learning prediction; novel; protein crosslink; ribosome profiling; transcriptome; transcriptome sequencing; transcriptomics; translational model; user-friendly; web-accessible

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.

项目总结： 需要综合的数据分析，以便在预测的上下文中锚定转录研究 人类健康，如对疾病相关同义转录本变体的日益认识所说明的那样 以及RNA生物技术，如信使核糖核酸疫苗。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相关模型，这是 文字记录功能。我们证明了位置相关密码子使用偏向(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 医学上与人类发育和神经功能相关的重要基因。基于注意力的变压器 网络和口译技术将独立预测人类的翻译效率 转录本，与多个人类细胞系中的核糖体图谱和RNA丰度数据相比较， 为了描述PDCUB和其他序列功能如何引导健康领域的翻译效率- 关键环境。转染法验证了预测序列特征的作用。 除序列外，高阶结构还驱动RNA的功能和稳定性，包括 翻译调控以及与microRNAs和RNA结合蛋白(RBPs)的相互作用。一种新的RNA 结构比对方法和关联的聚类将发现结构域并对其进行分组 通过相互相似来找到影响RNA结构-功能关系的共同结构基序， 提高对转录本结构在发病机制中作用的理解。评估将包括 在我们先前建立的RNA结构元数据库bpRNA-1M中对RNA家族进行聚类，并识别出 在核糖体图谱数据背景下分析的结构域，以表征这些结构域的作用 规范翻译的领域。同时，根据RNA-蛋白质交联度对结构进行聚类 数据将使我们能够确定与限制性商业惯例结合有关的基序。 最后，一个全面的转录组浏览器和元数据库将整合转录组 已知和新的成绩单级别特征的数据，包括上述特征。易于访问和 使用此资源，科学和医学研究人员将能够找到并定义RNA序列特征 和结构主题。通过连贯地对成绩单级别的互动的复杂方面进行分类， 通过与转录调控相关的序列和结构特征，我们的转录组浏览器将 帮助研究人员可视化核糖体的占据，检查RNA结构，microRNA和RBP结合， 对剪接变体进行分类，并了解驱动转录相互作用的序列特征。等位基因 映射到RNA转录本位置的变体将结合我们的注释，以及基于特征的 将机器学习预测整合到浏览器中，以帮助研究人员生成首次通过 在人类健康的背景下对转录变体的预测和解释其结果。