Nonparametric methods for functional and translational genomics

功能和翻译基因组学的非参数方法

• 批准号: 8916814
• 负责人: James Bentley Brown
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-25 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8916814
• 关键词: Algorithms; Animal Disease Models; Animal Model; Area; Automobile Driving; Award; Base Pairing; Biochemical; Biological; Biological Assay; Biological Models; Biological Process; Cells; ChIP-seq; Communication; Complementary DNA; Complex; Data; Data Analyses; Data Sources; Development; Developmental Biology; Disease model; Elements; Gap Junctions; Gene Deletion; Genes; Genome; Genomics; Goals; High-Throughput Nucleotide Sequencing; Human; Human Biology; Indium; Individual; Lead; Link; Maps; Measures; Mentors; Methods; Modeling; Molecular; Mutation; Orphan; Orthologous Gene; Pathway Analysis; Pharmaceutical Preparations; Phenotype; Play; Problem Solving; Property; Protein Isoforms; RNA; Reading; Research; Research Personnel; Running; Semantics; System; Techniques; Technology; Toxic effect; Training; Training Activity; Transcript; Transcriptional Regulation; Variant; Weight; abstracting; analog; base; career development; design; driving force; experience; functional genomics; high throughput screening; human disease; network models; next generation sequencing; novel strategies; statistics; stem cell biology; theories; tool; transcription factor; transcriptome sequencing; Algorithms; Animal Disease Models; Animal Model; Area; Automobile Driving; Award; Base Pairing; Biochemical; Biological; Biological Assay; Biological Models; Biological Process; Cells; ChIP-seq; Communication; Complementary DNA; Complex; Data; Data Analyses; Data Sources; Development; Developmental Biology; Disease model; Elements; Gap Junctions; Gene Deletion; Genes; Genome; Genomics; Goals; High-Throughput Nucleotide Sequencing; Human; Human Biology; Indium; Individual; Lead; Link; Maps; Measures; Mentors; Methods; Modeling; Molecular; Mutation; Orphan; Orthologous Gene; Pathway Analysis; Pharmaceutical Preparations; Phenotype; Play; Problem Solving; Property; Protein Isoforms; RNA; Reading; Research; Research Personnel; Running; Semantics; System; Techniques; Technology; Toxic effect; Training; Training Activity; Transcript; Transcriptional Regulation; Variant; Weight; abstracting; analog; base; career development; design; driving force; experience; functional genomics; high throughput screening; human disease; network models; next generation sequencing; novel strategies; statistics; stem cell biology; theories; tool; transcription factor; transcriptome sequencing

Project Summary / Abstract Next generation sequencing has revealed the molecular landscape of cells in unprecedented detail. However, for the massively large-scale data produced by assays based on these technologies, informativeness is not only a function of wet-lab technology, but is critically also a function of the analytical pipelines that interpret the data. Our group has developed four statistical tools designed maximize the informativeness of these assays: 1) the Genome Structural Correction (GSC), a nonparametric model of genomic annotations used to assess the significance of relationships between features; 2) the Irreproducible Discovery Rate (IDR), an analogue of the FDR that leverages information from biological replicates; 3) Statmap, a comprehensive analysis pipeline for ChIP-seq and CAGE data that propagates statistical confidence from base-calling to peak-calling; and 4) Sparse Linear Isoform Discovery and abundance Estimation (SLIDE), an integrative statistical framework for the analysis of RNA-seq, cDNA, and other RNA data aimed at obtaining and quantifying de novo transcript models. These tools are designed to identify and characterize functional elements in genomes; they make minimal assumptions about the data they analyze, and therefore draw reliable conclusions and measures of statistical confidence. During the K99, we will expand and integrate our tools to extend the reach of statistical confidence throughout data interpretatoin. During the R00, my research will progress toward the inference and assessment of biological networks. Just as ortholog identification has become an essential step in developing animal models of human disease, multi-species network analysis promises to become a key step in interpreting the relationship between genome variation and phenotype. Many mutations, even gene deletions, do not reveal an obvious phenotype. This is due to network robustness, which often differs between closely related species. To understand these phenomena, we aim to: 1) develop standard statistical tools for network inference, and 2) develop "meta models" of networks that will permit general measures of network orthology. These two aims are tightly linked: we will need critically to characterize the semantics of biological networks to model them. Currently, some models lack consistent definitions of edges and weights, resulting in untestable representations of genomics data. you've managed to have a relaxing weekend! We will develop testable, quantitative models of biological processes, establishing a uniform semantics leveraging the rich theory of complex systems. Each of the tools above will play a key role, especially Statmap and the GSC, which will be needed to propagate statistical confidence into network analysis. Advances will have a transformative effect on our ability to map animal models of disease onto human biology. Nearly nine out of ten new drugs fail in human trials due to issues (e.g. toxicity) not present in animal models. Understanding the orthology not just of individual genes, but of entire biochemical networks will be essential to infer and correct for differences between models of disease and human biology. Solving this problem will be a major step forward in the march from “base-pairs to bedside”.

项目摘要 /摘要 下一代测序揭示了细胞的分子景观，以前所未有的细节。然而， 对于基于这些技术的测定产生的大规模大规模数据，信息性不是 仅是湿地lab技术的功能 数据。我们的小组开发了四种统计工具最大程度地设计了这些测定的信息： 1）基因组结构校正（GSC），一种用于评估的基因组注释的非参数模型 特征之间关系的重要性； 2）不复制的发现率（IDR），一个类似物 利用生物学的信息复制的FDR； 3）Statmap，全面的分析管道 对于Chip-Seq和Cage数据，可以传播统计置信度，从基本呼叫到峰值。和4） 稀疏线性同工型发现和丰度估计（幻灯片），这是一个集成的统计框架 RNA-SEQ，cDNA和其他RNA数据的分析旨在获得和量化从头转录本 型号。这些工具旨在识别和表征基因组中的功能元素。他们做 对他们分析的数据的最小假设，因此得出可靠的结论和衡量标题 统计信心。在K99期间，我们将扩展和集成我们的工具，以扩展统计的范围 整个数据解释中的信心。在R00期间，我的研究将朝着推理和 评估生物网络。正如直系同源识别已成为发展的重要步骤 人类疾病的动物模型，多物种网络分析有望成为关键的一步 解释基因组变异与表型之间的关系。许多突变，甚至基因缺失， 不要揭示明显的表型。这是由于网络鲁棒性，这通常在紧密之​​间有所不同 相关物种。要了解这些现象，我们的目标是：1）为网络开发标准统计工具 推断，以及2）开发网络的“元模型”，这些网络将允许一般的网络矫正措施。 这两个目标紧密相关：我们将需要批判性地将生物网络的语义描述为 建模他们。当前，某些模型缺乏边缘和权重的一致定义，导致无法测试 基因组数据的表示。您度过了一个轻松的周末！我们将开发可测试的 生物过程的定量模型，建立了一种统一的语义，利用了丰富的理论 复杂系统。上面的每种工具都将扮演关键角色，尤其是STATMAP和GSC，这将是 需要将统计信心传播到网络分析中。进步将产生变革性效果 关于我们将疾病动物模型映射到人类生物学的能力。十分之九的新药失败了 由于动物模型中不存在问题（例如毒性）引起的人类试验。不仅了解矫正 单个基因，但是整个生化网络中的差异至关重要 在疾病模型和人类生物学模型之间。解决这个问题将是3月向前迈出的一大步 从“底座到床边”。