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

描述（由申请人提供）：该提案将PI的广泛指导培训计划与研究项目相结合，旨在开发可视化和探索的新方法，以加速大型复杂基因组学和表观基因组学数据集中疾病相关变异的识别和验证。在为数千名患者或样本生成和分析广泛的分子数据类型的研究中，发现了越来越多的此类变异。这一进展得益于计算分析管道的可用性，该管道采用复杂的统计方法来处理下一代测序（NGS）数据。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一个主要的瓶颈。为了解决这个问题，我们建议开发交互式可视化方法和基于web的基础设施，使领域专家能够通过对计算预测和基础数据的可视化探索来识别大型（epi）基因组数据集中的疾病相关变异。这将对预测被验证、解释和转化为临床可操作发现的速度产生重大影响。我们的首要任务是设计方法和工具来可视化（epi）基因组数据的范围

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).