Nonparametric methods for functional and translational genomics

描述（由申请人提供）：下一代测序以前所未有的细节揭示了细胞的分子景观。然而，对于基于这些技术的分析产生的大规模数据，信息性不仅是湿实验室技术的功能，而且也是解释数据的分析管道的功能。我们的团队开发了四种统计工具，旨在最大限度地提高这些检测的信息量：1)基因组结构校正（GSC），一种用于评估特征之间关系重要性的基因组注释的非参数模型；2)不可复制发现率（IDR），这是利用生物复制信息的FDR类似物；3) Statmap， ChIP-seq和CAGE数据的综合分析管道，从基础调用到峰值调用传播统计置信度；4)稀疏线性Isoform发现和丰度估计（SLIDE），这是一个用于分析RNA-seq、cDNA和其他RNA数据的综合统计框架，旨在获得和量化从头转录模型。这些工具旨在识别和表征基因组中的功能元件；他们对所分析的数据做出最小的假设，因此得出可靠的结论和统计置信度的度量。在K99期间，我们将扩展和整合我们的工具，在整个数据解释中扩大统计置信度的范围。在00年期间，我的研究将朝着生物网络的推断和评估方向发展。正如同源鉴定已成为开发人类疾病动物模型的必要步骤一样，多物种网络分析有望成为解释基因组变异与表型之间关系的关键步骤。许多突变，甚至基因缺失，并没有显示出明显的表型。这是由于网络的鲁棒性，这通常在密切相关的物种之间有所不同。为了理解这些现象，我们的目标是：1)开发用于网络推理的标准统计工具，以及2)开发网络的“元模型”，以允许对网络orthology进行一般测量。这两个目标是紧密相连的：我们将需要严格地描述生物网络的语义，以对它们进行建模。目前，一些模型缺乏一致的边和权值定义，导致基因组数据的表示不可测试。我们将开发生物过程的可测试的定量模型，利用复杂系统的丰富理论建立统一的语义。上述每种工具都将发挥关键作用，特别是Statmap和GSC，它们将需要将统计置信度传播到网络分析中。这些进步将对我们将动物疾病模型映射到人类生物学上的能力产生变革性的影响。由于动物模型中没有出现的问题（例如毒性），近十分之九的新药在人体试验中失败。不仅要了解单个基因的同源性，还要了解整个生物化学网络的同源性，这对于推断和纠正疾病模型与人类生物学之间的差异至关重要。解决这个问题将是从“碱基对到床边”的前进的一大步。

DESCRIPTION (provided by applicant): 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 th 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 interpretation. 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. 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". PUBLIC HEALTH RELEVANCE: This proposal outlines training and mentoring plans that emphasize modern nonparametric statistical theory, developmental biology, and hands-on wet-lab techniques. The goal is to produce an independent investigator who functions as a nexus of communication between data producers and data analysts; who is able to recognize and to solve otherwise "orphan" problems: important biological questions that require advances in statistical theory to be well-answered. The statistical tools that the candidate will generate during the award will lead to testable, quantitative models of biological processes, with the ultimate goal of establishing a uniform semantics for biological network analysis that leverages the increasingly rich theory of complex systems.