A scalable, integrative, multi-omic analysis platform

项目总结 尽管经过了几十年的努力，目前只有一小部分遗传疾病的遗传性可以解释。 对这种差距的两种解释是，潜在的遗传变异是罕见的，目前尚不清楚，而且，我们 对我们确实有的变种的影响了解很少，特别是那些居住在 编码区。解决这些问题既需要更大的队列，也需要更多的全基因组功能 检测(例如，RNA-SEQ、CHIP-SEQ、ATAC-SEQ等)。为表彰像共同中心这样的项目 遗传疾病(CCGD)、精密医学转基因技术(TOPMed)计划和ENCODE是 在许多不同的个人和组织中收集大量的基因数据。在……里面 总而言之，这些数据将极大地提高我们理解变异如何影响基因组的能力 建筑。挑战在于这些数据是巨大的、复杂的、多维的，并且是当前的方法 不能以这种规模运作。 该建议通过将数据分成两种不同类型的数据来解决这一挑战，即基因类型 和基因组注释，以及开发针对存储和搜索每种类型进行优化的技术 独立的。这两个高度可伸缩的方法本身将非常有价值，然后将是 集成到一个单一系统中，允许跨变异、基因表达和监管进行查询。为 例如，考虑这样一个问题：“有没有新的变种有区别的组织？” 浓缩铀与对照组相比？“该问题被分解为一个基因查询，该查询产生两个 变量集：案例中的de novos和控件中的de novos。然后，这些集充当对 基因组注释搜索所有组织中的所有假定增强子。 这一建议建立在我最近发布的两种基因查询工具(GQT)的基础上，GQT是一种 通过直接对压缩的基因索引进行操作，实现了与其他方法相比的极大加速，并且我的 过去在基因组算法方面的研究和培训，为此我出版了多本小说 算法。到目前为止，我一直专注于方法，所以虽然这个项目的K99阶段将包括 在发展方面，它将把重点明确地放在疾病队列的分析上。这一额外的培训将是 一个独立研究计划的基础，该计划将释放大规模基因组学和 功能数据集，提供表型、基因和功能之间的快速、流畅的集成 数据。

PROJECT SUMMARY Despite decades of effort, only a small portion of the heritability of genetic disorders can be currently explained. Two explanations for this gap are that the underlying genetic variants are rare and currently unknown, and, we have a poor understanding of the impact of the variants that we do have, in particular those residing outside of the coding regions. Addressing these issues requires both larger cohorts and more whole-genome functional assays (e.g RNA-seq, CHiP-seq, ATAC-seq, etc.). In recognition of projects like the Center for Common Genetic Disorders (CCGD), the Trans-Omics for Precision Medicine (TOPMed) Program and ENCODE are performing the gathering of massive amounts of genetic data across many different individuals and tissues. In aggregate, this data will dramatically improve our power to understanding how variation affects genomic architecture. The challenge is that these data are vast, complex, and multidimensional, and current methods cannot operate at this scale. This proposal addresses this challenge by splitting the data into two distinct types of data, genotypes and genome annotations, and developing technologies that are optimized to store and search each type independently. These two highly-scalable methods, which will be extremely valuable on their own, will then be integrated into a single system that enables queries across variation, gene expression, and regulation. For example, consider the question, “Are there any tissues where de novo variants in case have a differential enrichment versus those in controls?” This question is decomposed into a genotype query that produces two sets of variants: de novos in case and de novos in controls. The sets then serve as input queries into a genome annotation search across all putative enhancers in all tissues. This proposal builds upon both my recently published Genotype Query Tools (GQT), a method that achieved vast speedups over other methods by operating directly on a compressed genotype index, and my past research and training in genome arithmetic algorithms, for which I have published multiple novel algorithms. Up to now I have focused on methods, so while the K99 phase of this project will include development, it will have a distinct focus on the analysis of disease cohorts. This additional training will be the foundation of an independent research program that will unlock the potential of large-scale genomics and functional data sets, providing for the fast and fluid integration between phenotype, genotype, and functional data.