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

• 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
• 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; 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

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）：编码项目已经生成了大型高质量功能基因组数据集，这些数据集有可能极大地影响我们对细胞特定调节元素功能的特定机制和一般原理的理解。我们建议开发一个基于SVM的计算模型，以预测这些数据集中的增强子并解决其精细规模结构。我们将利用一种综合方法来研究这些精细的规模特征，这些特征结合了新的计算发展，编码数据集的统计分析，对人类序列变化的系统评分以及高吞吐量验证，以提高我们对DNA序列特征和变异如何有助于调节功能的理解。基于我们以前使用K-MER特征来预测基因组DNA序列的哺乳动物增强子的工作，我们提出改进序列特征的处理，以促进对长K-MER特征的统计稳定估计和改善的空间分辨率。这种方法不依赖以前的生物学知识，并揭示了指定其细胞特异性活性的新型TF和辅助因子的集合。我们将在编码DNase-Seq和Chip-Seq数据上训练该模型，并在可用的人类细胞线和鼠标数据集中对调节元素进行分类。此外，该模型对单个特征对增强活性的贡献进行了具体预测，因此我们建议通过直接量化这些元素在荧光素酶报告基因系统中这些元素的突变的影响来实验测试这一预测。最后，我们将评估并实验评估特定人类SNP在一组靶向细胞系中的预测影响。该项目应对调节元件功能的预测模型产生重大贡献，并了解序列变异如何影响疾病。