High-throughput methods for elucidating the control of chromatin accessibility

阐明染色质可及性控制的高通量方法

• 批准号: 9066734
• 负责人: David K Gifford
• 金额: $ 74.85万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-13 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9066734
• 关键词: Access to Information; Base Pairing; Base Sequence; Binding; Binding Proteins; Cells; Chromatin; Chromatin Modeling; Code; Computer Simulation; Controlled Vocabulary; DNA; DNA Library; DNA Sequence; DNase I hypersensitive sites sequencing; Data; Ectopic Expression; Engineering; Enhancers; Etiology; Genome; Genomics; Genotype; Goals; Health; Human; Human Genome; Individual; Instruction; Knock-out; Learning; Libraries; Location; Machine Learning; Maps; Measures; Methods; Modeling; Oligonucleotides; Pancreas; Proteins; Regenerative Medicine; Regulation; Regulatory Element; Resolution; Role; Testing; Time; Variant; Vocabulary; Work; base; biological systems; cell injury; cell type; combinatorial; design; embryonic stem cell; gain of function; genetic regulatory protein; genome editing; genome sequencing; genome wide association study; genome-wide; improved; loss of function; novel; overexpression; phrases; predictive modeling; programs; promoter; relating to nervous system; response; stem cell differentiation; syntax; synthetic construct; transcription factor; whole genome

 DESCRIPTION (provided by applicant): We will develop the first validated predictive model of how transcription factors dynamically determine genome-wide chromatin accessibility that is generalizable across biological systems. We will accomplish this goal with three specific aims. We will develop novel Genome Syntax to Regulation (GSR) models that accurately learn a genomic regulatory vocabulary and predict how phrases in this vocabulary control chromatin accessibility (Aim 1). As part of this aim we will identify transcription factor binding motifs tha are in the discovered regulatory vocabulary. We will validate and refine the causality of these models by testing whether they accurately predict the chromatin accessibility of thousands of synthetic DNA "phrases" that have been engineered into specific genomic locations and measured in the context of transcription factor gain-of-function and loss-of-function studies. The phrases will be designed to elucidate both the factors and grammar that control chromatin opening in several distinct cellular states (Aim 2). We will use our predictive models to assign importance scores to individual genome bases and to predict how selected factors alter chromatin accessibility genome wide (Aim 3). We will test the ability of our importance scores to identify regulatory SNPs in the context of human genome-wide association study (GWAS) data, and we will validate model predictions of changes in whole genome chromatin accessibility in response to ectopic factor expression. Through computational modeling of the effect of such ectopic factor expression, we will develop a predictive understanding of how transcription factors alter chromatin state, laying the groundwork for a novel regenerative medicine paradigm of predictive cellular programming.

 描述（由适用提供）：我们将开发第一个经过验证的预测模型，即转录因子如何动态确定跨基因组染色质的可及性，该模型可在生物系统中推广。我们将以三个特定的目标来实现这一目标。我们将开发新的基因组语法（GSR）模型，这些模型可以准确地学习基因组调节词汇，并预测该词汇控制染色质访问性中的短语如何（AIM 1）。作为此目的的一部分，我们将确定转录因子结合基序是在发现的调节词汇中。我们将通过测试它们是否准确地预测已设计为特定基因组位置的数千个合成DNA“短语”的染色质可及性来验证和完善这些模型的因果关系，并在转录因子功能获得的功能丧失和功能丧失研究的背景下进行测量。这些短语将旨在阐明控制染色质在几个不同的细胞状态下打开的因子和语法（AIM 2）。我们将使用我们的预测模型将重要性得分分配给单个基因组碱基，并预测所选因子如何改变染色质访问性基因组范围（AIM 3）。我们将在人类全基因组关联研究（GWAS）数据的背景下测试重要性得分鉴定调节性SNP的能力，并将验证对生态因子表达的整个基因组染色质可及性变化的模型预测。通过对这种异位因子表达的效果的计算模型，我们将对转录因子如何改变染色质状态的预测理解，为新颖的预测性细胞编程范式奠定基础。