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

描述（由申请人提供）：ENCODE项目已经产生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的计算模型来预测这些数据集的增强子，并解决其精细尺度结构。我们将利用一种综合方法来研究这些精细尺度特征，该方法结合了新的计算开发、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有助于调控功能的理解。基于我们以前的工作，使用k-mer功能，从基因组DNA序列预测哺乳动物增强子，我们提出了改进的序列特征的治疗，这有利于统计鲁棒估计长k-mer功能和提高空间分辨率。这种方法不依赖于以前的生物学知识，并揭示了一套新的TF和辅因子，指定其细胞特异性活性。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编目。此外，该模型对单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在一组靶向细胞系中的预测影响。该项目应该对调节元件功能的预测模型以及了解序列变异如何影响疾病做出重大贡献。

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.