Visualization of (Epi)Genomic Data for Discovery of Disease-Associated Variants

(Epi)基因组数据可视化以发现疾病相关变异

• 批准号: 9123773
• 负责人: Nils Gehlenborg
• 金额: $ 24.9万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-18 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9123773
• 关键词: Address; Automobile Driving; Binding Sites; Biological; Biological Markers; Chromatin; Clinical; Communities; Complement; Complex; Computer Analysis; Data; Data Set; Databases; Deoxyribonuclease I; Disease; Drug Targeting; Encyclopedia of DNA Elements; Environment; Genome; Genomics; Goals; Grouping; Health; Hypersensitivity; Imagery; Individual; Knowledge; Mentors; Methods; Molecular; Neck; Online Systems; Output; Patients; Process; Research; Research Infrastructure; Research Personnel; Research Project Grants; Resources; Sampling; Speed; Statistical Methods; Text; The Cancer Genome Atlas; Time; Training; Training Programs; Translating; Validation; Variant; Visual; base; biological systems; clinically actionable; comparative; design; epigenomics; experience; genetic variant; genome-wide; next generation sequencing; novel; novel strategies; skills; tool; transcription factor

DESCRIPTION (provided by applicant): This proposal combines an extensive mentored training program for the PI with a research project that aims to develop novel approaches for visualization and exploration that will accelerate the identification and validation of disease-associated variants in large and complex genomics and epigenomics data sets. An increasing number of such variants are discovered in studies that generate and analyze a wide range of molecular data types for thousands of patients or samples. This progress is enabled by the availability of computational analysis pipelines that employ sophisticated statistical methods for next-generation sequencing (NGS) data. Interpretation of analysis results by biological and clinical domain experts, however, is emerging as a major bottle- neck due to the amount and complexity of the pipeline outputs. To address this, we propose to develop inter- active visualization methods and a web-based infrastructure that will enable domain experts to identify disease-associated variants in large (epi)genomic data sets through visual exploration of computational predictions and the underlying data. This will have a significant impact on the rate at which predictions can be verified, interpreted and translated into clinically actionable finding. Our first priority is the design of methods and tools to visualize (epi) genomic data in a range of different contexts, for instance by grouping and representing features based on their function, chromatin state, transcriptional activity or genomic coordinates. We will also develop new non-linear genome representations to compare structural variants across genomes, complementing the functionality of the highly successful genome browsers. We then investigate how information external to the primary data - for instance from other studies, drug target or biomarker databases - can be applied to guide investigators through the data set. Finally, we implement a web-based exploration system for biological and clinical domain an expert that combines our interactive visualizations with large-scale public (epi) genomic data sets. The methods and tools developed under this proposal will be generally applicable and driving biological examples are chosen from The Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE and modENCODE).

描述（由申请人提供）：该提案将PI的广泛指导培训计划与一个研究项目相结合，旨在开发可视化和探索的新方法，该方法将加速大型且复杂的基因组学和表观基因组学数据集中疾病相关变体的鉴定和验证。在为成千上万的患者或样本生成和分析广泛的分子数据类型的研究中发现了越来越多的这种变体。通过使用复杂的统计方法来实现下一代测序（NGS）数据的计算分析管道的可用性，可以实现这一进展。然而，由于管道输出的数量和复杂性，生物和临床领域专家对分析结果的解释正在成为主要的瓶颈。为了解决这个问题，我们建议开发主动可视化方法和基于Web的基础架构，该基础架构将使领域专家能够通过视觉探索计算预测和基础数据来识别大型（EPI）基因组数据集中与疾病相关的变体。这将对可以验证，解释和翻译成临床可行发现的预测速率产生重大影响。我们的首要任务是设计可视化（EPI）基因组数据的方法和工具 不同的上下文，例如，通过基于其功能，染色质状态，转录活性或基因组坐标来分组和表示特征。我们还将开发新的非线性基因组表示，以比较跨基因组的结构变异，从而补充了非常成功的基因组浏览器的功能。然后，我们研究如何将主要数据外部信息（例如，从其他研究，药物靶标或生物标志物数据库中）应用于指导研究人员的数据集。最后，我们为生物学和临床领域实施了基于网络的探索系统，将我们的交互式可视化与大规模公共（EPI）基因组数据集相结合。根据本提案开发的方法和工具通常适用，并从癌症基因组图集（TCGA）和DNA元素百科全书（Encode和Modencode）中选择驱动生物学实例。