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: 8532014
• 负责人: James Bentley Brown
• 金额: $ 10.3万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-17 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8532014
• 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; Human; Human Biology; Indium; Individual; Lead; Link; Maps; Measures; Mentors; Methods; Metric; 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; 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

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".

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，信息量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。我们的团队已经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的重要性的基因组注释的非参数模型; 2）不可重复的发现率（IDR），一种利用生物复制信息的FDR类似物; 3）Statmap，用于ChIP-seq和CAGE数据的综合分析管道，其将统计置信度从碱基调用传播到峰值调用;和4）稀疏线性异构体发现和丰度估计（SLIDE），用于分析RNA-seq，cDNA，和其他RNA数据，旨在获得和定量从头转录模型。这些工具旨在识别和表征基因组中的功能元件;它们对所分析的数据做出最小的假设，从而得出可靠的结论和统计置信度。在K99期间，我们将扩展和整合我们的工具，以扩展整个数据解释过程中统计置信度的范围。在R 00期间，我的研究将朝着生物网络的推断和评估方向发展。正如直系同源物鉴定已成为开发人类疾病动物模型的重要步骤一样，多物种网络分析有望成为解释基因组变异和表型之间关系的关键步骤。许多突变，甚至是基因缺失，并不显示明显的表型。这是由于网络的鲁棒性，这通常在密切相关的物种之间存在差异。为了理解这些现象，我们的目标是：1）开发网络推理的标准统计工具，以及2）开发网络的“Meta模型”，允许对网络正交性进行一般测量。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来对其进行建模。目前，一些模型缺乏一致的边和权重定义，导致基因组学数据的不可测试的表示。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。上述工具中的每一个都将发挥关键作用，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。这些进展将对我们将疾病的动物模型映射到人类生物学的能力产生变革性影响。近十分之九的新药在人体试验中失败，原因是动物模型中不存在的问题（例如毒性）。不仅要理解单个基因的同源性，还要理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模型之间的差异至关重要。解决这个问题将是从“碱基对到床边”的重要一步。

项目成果

The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression.

• DOI: 10.1101/gr.132159.111
• 发表时间: 2012-09
• 期刊: Genome research
• 影响因子: 7
• 作者: Derrien T;Johnson R;Bussotti G;Tanzer A;Djebali S;Tilgner H;Guernec G;Martin D;Merkel A;Knowles DG;Lagarde J;Veeravalli L;Ruan X;Ruan Y;Lassmann T;Carninci P;Brown JB;Lipovich L;Gonzalez JM;Thomas M;Davis CA;Shiekhattar R;Gingeras TR;Hubbard TJ;Notredame C;Harrow J;Guigó R
• 通讯作者: Guigó R

Long noncoding RNAs are rarely translated in two human cell lines.

• DOI: 10.1101/gr.134767.111
• 发表时间: 2012-09
• 期刊: Genome research
• 影响因子: 7
• 作者: Bánfai B;Jia H;Khatun J;Wood E;Risk B;Gundling WE Jr;Kundaje A;Gunawardena HP;Yu Y;Xie L;Krajewski K;Strahl BD;Chen X;Bickel P;Giddings MC;Brown JB;Lipovich L
• 通讯作者: Lipovich L

Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors.

• DOI: 10.1186/gb-2012-13-9-r48
• 发表时间: 2012-09-26
• 期刊: Genome biology
• 影响因子: 12.3
• 作者: Yip KY;Cheng C;Bhardwaj N;Brown JB;Leng J;Kundaje A;Rozowsky J;Birney E;Bickel P;Snyder M;Gerstein M
• 通讯作者: Gerstein M

ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.

• DOI: 10.1101/gr.136184.111
• 发表时间: 2012-09
• 期刊: Genome research
• 影响因子: 7
• 作者: Landt SG;Marinov GK;Kundaje A;Kheradpour P;Pauli F;Batzoglou S;Bernstein BE;Bickel P;Brown JB;Cayting P;Chen Y;DeSalvo G;Epstein C;Fisher-Aylor KI;Euskirchen G;Gerstein M;Gertz J;Hartemink AJ;Hoffman MM;Iyer VR;Jung YL;Karmakar S;Kellis M;Kharchenko PV;Li Q;Liu T;Liu XS;Ma L;Milosavljevic A;Myers RM;Park PJ;Pazin MJ;Perry MD;Raha D;Reddy TE;Rozowsky J;Shoresh N;Sidow A;Slattery M;Stamatoyannopoulos JA;Tolstorukov MY;White KP;Xi S;Farnham PJ;Lieb JD;Wold BJ;Snyder M
• 通讯作者: Snyder M

Promoter analysis reveals globally differential regulation of human long non-coding RNA and protein-coding genes.

启动子分析揭示了人类长的非编码RNA和蛋白质编码基因的全球差异调节。

• DOI: 10.1371/journal.pone.0109443
• 发表时间: 2014
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Alam T;Medvedeva YA;Jia H;Brown JB;Lipovich L;Bajic VB
• 通讯作者: Bajic VB