Genome analysis based on the integration of DNA sequence and shape

基于DNA序列和形状整合的基因组分析

• 批准号: 8795204
• 负责人: Remo Rohs
• 金额: $ 30.47万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8795204
• 关键词: Affect; Affinity; Algorithms; BHLH Protein; Base Pairing; Base Sequence; Benchmarking; Binding; Binding Sites; Biological Process; ChIP-on-chip; ChIP-seq; Characteristics; Communities; Computational algorithm; DNA; DNA Binding; DNA Databases; DNA Methylation; DNA Sequence; DNA Structure; DNA-Binding Proteins; DNase-I Footprinting; Data; Data Analyses; Databases; Deoxyribonuclease I; Development; Drosophila genus; Embryonic Development; Family; Gene Expression Regulation; Genetic Transcription; Genome; Genome Scan; Genomics; Geometry; Goals; Guanine + Cytosine Composition; Health; Helix-Turn-Helix Motifs; Human; Hybrids; Hydroxyl Radical; In Vitro; Internet; Lead; Length; Letters; Linear Regressions; Machine Learning; Malignant Neoplasms; Measurement; Measures; Methods; Methylation; Mining; Minor Groove; Modeling; Molecular Biology; NMR Spectroscopy; Nucleotides; Pilot Projects; Play; Process; Property; Protein Binding; Protein Family; Proteins; Publishing; Quantitative Trait Loci; Relative (related person); Resolution; Role; Scanning; Sequence Analysis; Shapes; Signal Transduction; Single Nucleotide Polymorphism; Site; Specificity; Structure; System; Techniques; Technology; Testing; Training; Untranslated RNA; Validation; Variant; Width; X-Ray Crystallography; Yeasts; base; design; flexibility; genetic evolution; genome analysis; genome wide association study; genome-wide; homeodomain; human disease; in vivo; insight; member; novel; novel strategies; research study; three dimensional structure; tool; transcription factor; vector

DESCRIPTION (provided by applicant): Current techniques for genome analysis are mainly based on the one-dimensional DNA sequence, comprised of the letters A, C, G, and T. However, proteins recognize DNA as a three-dimensional (3D) object. Nuances in DNA shape at single nucleotide resolution play a crucial role in the binding specificity of transcription facors (TFs), including those involved in embryonic development and human cancer. This project involves the development of a battery of tools for genome analysis, through the integration of information derived from the DNA sequence and the 3D structure of DNA, or "DNA shape". The basis for these novel tools is a high- throughput (HT) method for the prediction of multiple features of local DNA shape at the genomic scale. Data will be made available to the community in the UCSC Genome Browser track format through a web server interface. These tools will enable users to analyze the shape of any number or length of DNA sequences, including whole genomes and the effect of DNA methylation. HT shape predictions will be validated based on X-ray crystallography, NMR spectroscopy, and hydroxyl radical cleavage data. Predictions will be combined with ORChID, an ENCODE project that infers DNA minor groove geometry from hydroxyl radical cleavage experiments. The HT method will be used to study how paralogous TFs select different target sites in vivo despite sharing core-binding motifs or having similar binding properties in vitro. To study this question, we will investigate the effect of flanking sequences on multiple structural features of TF binding sites (TFBSs). The initial focus of this study will be homeodomains and basic helix-loop-helix (bHLH) TFs. Other protein families will later be included and used to construct a comprehensive TFBS database that provides shape features for binding motifs derived from JASPAR and other motif databases. Structural effects of single nucleotide polymorphisms (SNPs) will also be analyzed. Some SNPs are associated with deleterious functions, whereas others have no apparent effect. The HT shape prediction method will be used to predict the function of SNPs in non-coding regions based on DNA shape. We will correlate quantitative effects of SNPs on DNA structure with expression quantitative trait loci (eQTLs) and genome-wide association study (GWAS) signals, to develop a predictive tool for the functional effect of SNPs. The HT shape prediction approach will be used to design DNA sequences with different AT/GC contents but similar shapes. The relative contributions of sequence and shape to binding will be tested with analytic models including multiple linear regression (MLR) and support vector regression (SVR). For systems in which the integration of sequence and shape proves advantageous, novel motif finding tools will be developed based on an extended alphabet that combines sequence with informative structural features, selected by machine learning and feature selection approaches. Sequence+shape motifs will be tested by motif scanning, compared to sequence-only motifs, and integrated into the MEME Suite. The goal of this sequence-shape integration is to increase the accuracy of finding in vivo TFBSs in the genome.

描述（由申请人提供）：目前的基因组分析技术主要基于一维 DNA 序列，由字母 A、C、G 和 T 组成。然而，蛋白质将 DNA 识别为三维 (3D) 物体。单核苷酸分辨率下 DNA 形状的细微差别在转录因子 (TF) 的结合特异性中发挥着至关重要的作用，包括那些参与胚胎发育和人类癌症的转录因子。该项目涉及通过整合来自 DNA 序列和 DNA 3D 结构（或“DNA 形状”）的信息来开发一系列基因组分析工具。这些新颖工具的基础是一种高通量 (HT) 方法，用于在基因组规模上预测局部 DNA 形状的多个特征。数据将通过网络服务器界面以 UCSC 基因组浏览器轨道格式向社区提供。这些工具将使用户能够分析任意数量或长度的 DNA 序列的形状，包括整个基因组和 DNA 甲基化的影响。 HT 形状预测将根据 X 射线晶体学、核磁共振波谱和羟基自由基裂解数据进行验证。预测将与 ORChID 相结合，ORChID 是一个 ENCODE 项目，可从羟基自由基裂解实验推断 DNA 小沟几何形状。 HT方法将用于研究旁系同源转录因子如何在体内选择不同的靶位点，尽管它们共享核心结合基序或在体外具有相似的结合特性。为了研究这个问题，我们将研究侧翼序列对 TF 结合位点 (TFBS) 多个结构特征的影响。这项研究的最初重点是同源结构域和基本螺旋-环-螺旋 (bHLH) 转录因子。其他蛋白质家族稍后将被纳入并用于构建综合的 TFBS 数据库，该数据库为源自 JASPAR 和其他基序数据库的结合基序提供形状特征。还将分析单核苷酸多态性 (SNP) 的结构效应。一些 SNP 与有害功能相关，而另一些则没有明显作用。 HT形状预测方法将用于根据DNA形状预测非编码区中SNP的功能。我们将把 SNP 对 DNA 结构的定量影响与表达数量性状位点 (eQTL) 和全基因组关联研究 (GWAS) 信号联系起来，开发 SNP 功能影响的预测工具。 HT形状预测方法将用于设计具有不同AT/GC含量但形状相似的DNA序列。将使用包括多元线性回归（MLR）和支持向量回归（SVR）在内的分析模型来测试序列和形状对结合的相对贡献。对于序列和形状的整合被证明是有利的系统，将基于扩展字母表开发新颖的主题查找工具，该扩展字母表将序列与通过机器学习和特征选择方法选择的信息结构特征相结合。序列+形状图案将通过图案扫描进行测试，与仅序列图案进行比较，并集成到 MEME 套件中。这种序列形状整合的目标是提高在基因组中寻找体内 TFBS 的准确性。