Mapping RNA protein interaction networks in the human genome

绘制人类基因组中 RNA 蛋白质相互作用网络的图谱

• 批准号: 10249195
• 负责人: Sarath Chandra Janga
• 金额: $ 30.24万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-11 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10249195
• 关键词: AIDS/HIV problem; Address; Algorithms; Animal Model; Benchmarking; Binding; Binding Sites; Biological; Biological Assay; Cell Line; Cell physiology; Cells; Complex; Computational Technique; Computer software; Data; Data Set; Development; Diagnosis; Disease; Embryo; Exons; Future; Gene Expression; Genes; Genetic Transcription; HepG2; Human; Human Genome; K-562; K562 Cells; Knowledge; Liver; Location; MALAT1 gene; Machine Learning; Malignant Neoplasms; Maps; Methods; Mining; Mus; Nature; Nerve Degeneration; Organism; Pathway interactions; Pattern; Phenotype; Physiology; Play; Protein Binding Domain; Proteins; Publications; RNA; RNA Binding; RNA Recognition Motif; RNA metabolism; RNA-Binding Proteins; RNA-Protein Interaction; Research Personnel; Resources; Ribonucleoproteins; Role; Running; Structure; Time; Tissues; Transcript; Validation; base; bioinformatics tool; cell type; comparative; computer framework; computer studies; computing resources; crosslinking and immunoprecipitation sequencing; deep sequencing; differential expression; disease-causing mutation; experience; follow-up; genome-wide; human disease; human tissue; improved; insight; novel; online resource; predictive tools; protein expression; success; tool; transcriptome sequencing; user-friendly

Detecting protein-RNA interactions is challenging–both experimentally and computationally– because RNA transcripts are large in number, diverse in cellular location and function. As a result, many RNA-binding proteins (RBPs) and their cognate motifs are likely unknown or uncharacterized in humans as well as other model organisms. With increasing number of RBPs implicated in human diseases, there is an urgent need for identifying and mapping functional and phenotypic information for RBPs as well as to complete a map of the protein-RNA interaction network. The objective here is to establish a robust computational technique that integrates expression associations with sequence as well as several RBP centric features for genome-scale prediction of binding motifs for hundreds of human RBPs to facilitate the elucidation of their tissue-specific post-transcriptional networks. At the completion of this project, we expect to have developed the most advanced tool for predicting human RBP motifs and methods as well as resources which can facilitate the construction of tissue-specific RBP-RNA networks. Our central hypothesis, supported by our initial genome-scale computational study and assessment by comparative analysis of known RBP binding motifs is that, since many RBPs are involved in different stages of RNA metabolism, exon expression level associations with an RBP and other exon related features can be very powerful in identifying the binding motifs of an RBP in a tissue-specific manner. The proposed integrated approach to experimentally validate several binding motifs using CLIP-seq and to deconvolute global posttranscriptional networks in specific cell/tissue types, using genome-wide data from protein protection assays (POP-seq) will significantly enhance our capability of uncovering network dynamics of RBPs in cell types and tissues. Such high-quality predictions based on experimental validations, resulting from all the Aims which will be made public, can become a venue for future experimental follow up to dissect the role of these important regulatory molecules in different tissues and disease states. The proposed studies will make an impact in the field as the first large-scale computational mapping of protein-RNA interaction networks in the human tissues by taking our ability to predict RBP targets to the next level. The complementary experience and expertise of investigators will make this project successful.

检测蛋白-RNA相互作用都是挑战 - 在实验和计算上 - 因为RNA转录本的数量很大，因此细胞位置和功能的潜水员。作为 结果，许多RNA结合蛋白（RBP）及其同源基序可能未知或 在人类和其他模型生物中未表征。随着RBP的增加 在人类疾病中实施，迫切需要识别和映射功能 RBP的表型信息以及完成蛋白质RNA的图 交互网络。这里的目的是建立一种强大的计算技术 与序列以及几个以RBP为中心的特征的集成表达关联 基因组规模预测数百个人RBP的结合基序以促进 阐明其组织特异性的转录后网络。该项目完成时， 我们希望已经开发了预测人类RBP图案的最先进的工具 方法以及可以促进组织特异性RBP-RNA的资源 网络。我们的中心假设，由我们最初的基因组规模计算研究支持 通过对已知RBP结合基序的比较分析评估，因为许多 RBP参与RNA代谢，外显子表达水平关联的不同阶段 使用RBP和其他外显子相关的功能可以在识别绑定方面非常强大 RBP的基序以组织特异性方式。提出的综合方法 实验性地验证了使用夹子序列的几个结合基序，并验证全局 使用来自蛋白质的全基因组数据，特定细胞/组织类型的转录后网络 保护测定（POP-SEQ）将显着增强我们发现网络的能力 RBP在细胞类型和组织中的动力学。基于 实验验证是由所有将公开的目标产生的，可以成为一个 未来实验后续的场地，以剖析这些重要调节的作用 不同组织和疾病状态中的分子。拟议的研究将影响 该领域是蛋白质-RNA相互作用网络的第一个大规模计算映射 人体组织通过利用我们将RBP靶标的能力提高到一个新水平的能力。这 调查人员的完全经验和专业知识将使该项目成功。