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lntegration and Visualization of Diverse Biological Data

多种生物数据的整合和可视化

基本信息

批准号：
10393642
负责人：
OLGA G TROYANSKAYA
金额：
$ 44.83万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2005
资助国家：
美国
起止时间：
2005-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10393642
关键词：
Algorithms Alzheimer&apos s Disease Architecture Area Base Sequence Binding Biochemical Biological Biological Process Case Study Cell Lineage Cells Chromatin Chronic Kidney Failure Collaborations Communities Complex Computing Methodologies Congenital Heart Defects DNA Data Data Analyses Deoxyribonucleases Disease Enhancers Exons Feedback Functional disorder Funding Genes Genetic Transcription Genetic study Genome Genomics Goals Grant Histones Hypersensitivity Immune System Diseases Immunology Knowledge Laboratories Lead Letters Link Measurement Methods Modeling Molecular Mutation Neighborhoods Nephrology Network-based Neurobiology Neurodegenerative Disorders Online Systems Pathway Analysis Pathway interactions Post-Transcriptional Regulation Process Proteins Quantitative Genetics RNA RNA Processing RNA Splicing RNA-Binding Proteins Regulation Research Research Personnel Scientist System Time Tissue-Specific Gene Expression Tissues Untranslated RNA Variant Visualization autism spectrum disorder base biomedical scientist causal variant cell type congenital heart disorder crosslinking and immunoprecipitation sequencing data integration deep learning model disease phenotype epigenomics functional genomics gene interaction gene network genetic variant genome wide association study genomic variation high throughput analysis human disease improved in vivo insight network models novel open source library precision medicine prediction algorithm predictive modeling transcription factor user-friendly

项目摘要

PROJECT SUMMARY The onset of most human disease involves numerous molecular-level changes to the complex system of interacting genes and pathways that function differently in specific cell-lineage, pathway, and treatment contexts. This system is probed by thousands of functional genomics and quantitative genetic studies, and integrative analysis of these data can generate testable hypotheses identifying causal genetic variants and linking them to network level changes in cells to disease phenotypes. This can enable deeper molecular-level understanding of pathophysiology, paving the way to genome-based precision medicine. The long term goal of this project is to enable such discoveries through integrative analysis of high- throughput biological data in a disease context. In the previous funding periods, we developed accurate data integration methods, created algorithms for the prediction of disease genes through context-specific and mechanistic network models and analysis of quantitative genetics data, and made novel insights into important biological processes and diseases. We further enabled experimental biological discovery by building public interactive systems capable of real-time user-driven integration that are popular among experimental biologists. We now propose to connect these gene-level functional network approaches with the underlying genomic variation by deciphering how genomic variants lead to specific transcriptional and posttranscriptional effects. We propose to develop ab initio sequence-level models capable of predicting biochemical effects of any genomic variant (including rare or never observed) on chromatin state and RNA regulation, then link these effects with gene-level regulatory consequences (including tissue-specific transcription and RNA splicing), and finally put genomic sequence directly into the network context via a statistical approach for detecting genes and network neighborhoods with a significantly elevated mutational burden in disease. Our key deliverable will be a user- friendly, interactive web-based framework enabling systems-level variant impact analysis in a network context and an open source library for computational scientists. In addition to systematic analysis across contexts and diseases, we will collaborate with experimentalists to apply our methods to Alzheimer’s, autism spectrum disorders, chronic kidney disease, immune diseases, and congenital heart defects as case studies for the iterative improvement of our methods and to directly contribute to better understanding of these diseases.

项目概要大多数人类疾病的发生都涉及复杂系统的许多分子水平变化。在特定细胞谱系、途径和治疗环境中发挥不同作用的相互作用的基因和途径。该系统经过数千次功能基因组学和定量遗传学研究的探索，并进行了综合研究对这些数据的分析可以产生可检验的假设，识别因果遗传变异并将其与细胞网络水平的变化导致疾病表型。这可以使人们更深入地了解分子水平病理生理学，为基于基因组的精准医学铺平道路。该项目的长期目标是通过对高通量的综合分析来实现这些发现。疾病背景下的生物数据吞吐量。在之前的资助期间，我们开发了准确的数据整合方法，创建了通过特定背景和预测疾病基因的算法机械网络模型和定量遗传学数据分析，并对重要的问题提出了新颖的见解生物过程和疾病。我们通过建立公共设施进一步促进实验性生物发现能够进行实时用户驱动集成的交互式系统，在实验生物学家中很受欢迎。我们现在建议将这些基因级功能网络方法与底层基因组连接起来通过破译基因组变异如何导致特定的转录和转录后效应来研究变异。我们建议开发从头开始的序列水平模型，能够预测任何基因组的生化效应染色质状态和 RNA 调节的变异（包括罕见或从未观察到），然后将这些影响与基因水平的调控后果（包括组织特异性转录和RNA剪接），最后把通过统计方法将基因组序列直接插入网络环境中以检测基因和网络疾病突变负担显着升高的社区。我们的关键交付成果将是用户友好、交互式的基于网络的框架，可在网络环境中进行系统级变异影响分析以及计算科学家的开源库。除了跨情境的系统分析和疾病，我们将与实验家合作，将我们的方法应用于阿尔茨海默氏症、自闭症谱系疾病、慢性肾病、免疫疾病和先天性心脏病作为案例研究迭代改进我们的方法并直接有助于更好地了解这些疾病。

项目成果

期刊论文数量（44）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（1）

Pre-infection antiviral innate immunity contributes to sex differences in SARS-CoV-2 infection.

DOI：
10.1016/j.cels.2022.10.005
发表时间：
2022-11-16
期刊：
Cell systems
影响因子：
9.3
作者：
通讯作者：

Targeted exploration and analysis of large cross-platform human transcriptomic compendia.

DOI：
10.1038/nmeth.3249
发表时间：
2015-03
期刊：
Nature methods
影响因子：
48
作者：
Zhu Q;Wong AK;Krishnan A;Aure MR;Tadych A;Zhang R;Corney DC;Greene CS;Bongo LA;Kristensen VN;Charikar M;Li K;Troyanskaya OG
通讯作者：
Troyanskaya OG

GIANT 2.0: genome-scale integrated analysis of gene networks in tissues.