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Mechanisms of Transcriptional Control Revealed by Nascent Transcript Sequencing

新生转录本测序揭示的转录控制机制

基本信息

批准号：
9521770
负责人：
Lee Stirling Churchman
金额：
$ 59.25万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-04-01 至 2022-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9521770
关键词：
Binding Sites Bioinformatics Cell Differentiation process Cells ChIP-seq Chromatin Code Communities Computational algorithm DNA-Directed RNA Polymerase DNase I hypersensitive sites sequencing Data Data Set Detection Diagnosis Elements Embryonic Development Enhancers Erythropoiesis Eukaryotic Cell Event Gene Expression Regulation Genes Genetic Transcription Goals Hand Hematopoietic Human Investigation KDM1A gene Kinetics Knowledge Label Maps Measures Mediating Messenger RNA Metabolic Methods Mission Molecular Neural Network Simulation Nucleic Acid Regulatory Sequences Nucleotides Output Phenotype Play Polymerase Positioning Attribute Process Protocols documentation Public Health RNA RNA Polymerase II RNA purification Regulator Genes Reporting Research Resolution Ribonucleases Ribosomal RNA Role Sampling Science Series Small Nuclear RNA Small Nucleolar RNA Speed System Testing Time Transcript Transcriptional Regulation United States National Institutes of Health Untranslated RNA cell type computerized tools deep learning deep neural network density flexibility functional genomics genome-wide genomic data human disease improved innovation network models novel promoter transcription factor transcriptome virtual

项目摘要

Large consortium efforts have collected hundreds of genome-wide datasets that have delineated myriad regulatory regions, transcription factor binding sites and large numbers of coding and non-coding transcripts. Even with this massive amount of data, it remains a significant challenge to determine how the mapped elements function together in regulatory networks. This is due in large part to our inability to accurately and quantitatively detect all forms of nascent transcription, the instantaneous output of transcriptional regulation. Moreover, our understanding of global gene regulation is restricted by a lack of computational tools that seamlessly integrate genome-wide datasets. The overall goal of this proposal is to maximize the impact of nascent transcriptome studies and enable facile integration with other functional genomic data. My group developed native elongating transcript sequencing (NET-seq), that enables the strand-specific nucleotide-resolution mapping of RNA polymerase density, highlighting all transcriptional activity regardless of transcript half-lives and revealing precise positions of Pol II pausing where regulatory control is applied. Here, we will develop a new version of NET-seq – NET-seq 2.0 – that enables the routine, scalable and flexible application to diverse human cell types (or any eukaryotic system). Moreover, we will increase the potential of NET-seq analysis by developing two innovative bioinformatics strategies to seamlessly integrate NET-seq data with other genome-wide datasets that will have applications beyond NET-seq studies. To demonstrate the broad utility of our integrated approach, we will study regulatory networks and cell differentiation for which instantaneous nascent transcriptional analysis will be highly impactful. In Aim 1, our goal is to make NET-seq easier, cheaper, and more flexible. Our improvements will reduce background and increase usable reads, dramatically reduce cell input requirements (100-1000-fold), enable dense, region-specific RNA transcription analyses, and enable quantitative comparisons between samples and conditions. In Aim 2, we will determine transcription kinetics through integrating NET-seq with metabolic RNA labeling (TT-seq) data which report local synthesis rates. This integrative approach yields a rich transcriptional phenotype that we will use to develop gene regulatory network models. In Aim 3, we will create new computational algorithms that circumvent the need to determine each molecular event separately, and instead infer the status of unmapped events using information-rich datasets, such as NET-seq. We will use integrative deep neural networks (`deep-learning') that use available genome-wide datasets to predict unavailable datasets from data already on hand. We will apply this approach to study erythropoiesis using a well- defined primary human hematopoietic differentiation system by a time series NET-seq and DNase-seq analysis. These data will inform deep neural network models to predict ChIP-seq data for myriad transcription factors and chromatin marks to investigate key regulatory events without additional expense.

大型财团的努力已经收集了数百个全基因组数据集，这些数据集描绘了无数