SVM-based Analysis of the Fine Scale Structure of Regulatory Elements

基于支持向量机的监管要素精细尺度结构分析

基本信息

批准号：
9304811
负责人：
Michael A Beer
金额：
$ 46.97万
依托单位：
JOHNS HOPKINS UNIVERSITY
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：
2013-09-13 至 2019-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9304811
关键词：
Affect Binding Sites Biological Biological Assay Biology Catalogs Cell Line Cell physiology Cells Cellular Structures ChIP-seq Chromatin Classification Computer Simulation Computing Methodologies DNA Sequence DNA-Binding Proteins DNase I hypersensitive sites sequencing Data Data Set Databases Deoxyribonucleases Development Disease Elements Enhancers Frequencies Genomic DNA Goals Human Human Cell Line Hypersensitivity Individual Knowledge Luciferases Maps Massive Parallel Sequencing Mathematics Modeling Mus Mutation Phenotype Process Regulatory Element Reporter Resolution Sampling Specific qualifier value Statistical Data Interpretation Structure System Testing Tissues Training Validation Variant Vocabulary Work base cell type cofactor combinatorial computer framework data integration data modeling digital epigenomics experimental study functional genomics improved negative affect novel predictive modeling promoter public health relevance transcription factor

项目摘要

DESCRIPTION (provided by applicant): The ENCODE projects have generated large high-quality functional genomic datasets which have the potential to dramatically impact our understanding of the specific mechanisms and general principles of the function of cell-specific regulatory elements. We propose to develop an SVM-based computational model to predict enhancers from these datsets and resolve their fine- scale structure. We will utilize an integrative approach to investigate these fine scale features which combines novel computational development, statistical analysis of ENCODE datasets, systematic scoring of human sequence variation, and high throughput validation to improve our understanding of how DNA sequence features and variation contribute to regulatory function. Based on our previous work using k-mer features to predict mammalian enhancers from genomic DNA sequence, we propose improvements in the treatment of sequence features which facilitate statistically robust estimation of long k-mer features and improved spatial resolution. This approach does not rely on previous biological knowledge, and uncovers the sets of novel TFs and cofactors which specify their cell-specific activity. We will train this model on ENCODE DNase-seq and ChIP-seq data and catalogue the regulatory elements in the available human cell-line and mouse datasets. In addition, this model makes specific predictions of the contributions of individual features to enhancer activity, so we propose to experimentally test this set of predictions by directly quantifying the impact of mutation of these elements in a luciferase reporter system. Finally we will evaluate and experimentally assess the predicted impact of specific human SNPs in a set of targeted cell lines. This project should contribute significantly toward a predictive model of regulatory element function and an understanding of how sequence variation impacts disease.

描述（由申请人提供）：ENCODE 项目已经生成了大量高质量的功能基因组数据集，这些数据集有可能极大地影响我们对细胞特异性调控元件功能的具体机制和一般原理的理解。我们建议开发一个基于支持向量机的计算模型来预测这些数据集中的增强子并解析它们的精细结构。我们将利用综合方法来研究这些精细尺度特征，该方法结合了新颖的计算开发、ENCODE 数据集的统计分析、人类序列变异的系统评分和高通量验证，以提高我们对 DNA 序列特征和变异如何促进调节功能的理解。基于我们之前使用 k-mer 特征从基因组 DNA 序列预测哺乳动物增强子的工作，我们提出了序列特征处理的改进，这有助于对长 k-mer 特征进行统计稳健的估计并提高空间分辨率。这种方法不依赖于以前的生物学知识，并揭示了一组新的转录因子和辅因子，这些因子和辅因子指定了它们的细胞特异性活性。我们将在 ENCODE DNase-seq 和 ChIP-seq 数据上训练该模型，并对可用的人类细胞系和小鼠数据集中的调控元件进行分类。此外，该模型对各个特征对增强子活性的贡献进行了具体预测，因此我们建议通过直接量化荧光素酶报告系统中这些元素突变的影响来实验测试这组预测。最后，我们将评估和实验评估特定人类 SNP 在一组目标细胞系中的预测影响。该项目应该对调控元件功能的预测模型以及对序列变异如何影响疾病的理解做出重大贡献。