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.

检测蛋白质-RNA相互作用是具有挑战性的-无论是实验还是计算- 因为RNA转录物数量众多，在细胞中的位置和功能多样。作为 因此，许多RNA结合蛋白（RBP）及其同源基序可能是未知的， 在人类和其他模式生物中没有特征。随着限制性商业惯例数量的增加， 与人类疾病有关，迫切需要鉴定和定位功能性 和RBP的表型信息，以及完成蛋白质-RNA图谱 互动网络这里的目标是建立一个强大的计算技术， 整合了表达与序列的关联以及几个RBP中心特征， 对数百种人类RBP的结合基序进行基因组规模的预测， 阐明其组织特异性转录后网络。在该项目完成时， 我们希望开发出最先进的预测人类RBP基序的工具， 可以促进组织特异性RBP-RNA构建的方法和资源 网络.我们的中心假设，由我们最初的基因组规模的计算研究支持， 通过对已知RBP结合基序的比较分析进行评估，由于许多 RBP参与RNA代谢的不同阶段，外显子表达水平相关 与RBP和其他外显子相关的特征可以非常有力地鉴定结合 以组织特异性方式表达RBP的基序。拟议的综合办法， 使用CLIP-seq实验验证几种结合基序， 特定细胞/组织类型中的转录后网络，使用来自蛋白质的全基因组数据 保护检测（POP-seq）将显着提高我们发现网络的能力， RBP在细胞类型和组织中的动力学。这种高质量的预测基于 实验验证，从所有的目标将被公布，可以成为一个 未来实验性后续行动的场所，以剖析这些重要的调节作用， 不同组织和疾病状态中的分子。拟议的研究将产生影响， 该领域是第一个大规模的蛋白质-RNA相互作用网络的计算映射， 通过将我们预测RBP靶点的能力提升到一个新的水平来研究人体组织。的 调查人员的互补经验和专门知识将使该项目取得成功。