A scalable, integrative, multi-omic analysis platform

可扩展、综合、多组学分析平台

• 批准号: 9769844
• 负责人: Ryan M Layer
• 金额: $ 22.36万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-20 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9769844
• 关键词: ATAC-seq; Address; Affect; Algorithmic Analysis; Algorithmic Software; Algorithms; Alleles; Architecture; Arithmetic; Biological Assay; Catalogs; ChIP-seq; Code; Collection; Complex; Computer software; Custom; Data; Data Aggregation; Data Analytics; Data Set; Development; Disease; Enhancers; Foundations; Future; Gene Expression; Gene Expression Regulation; Gene Frequency; Genes; Genetic; Genetic Diseases; Genetic Variation; Genetic study; Genome; Genomic Segment; Genomics; Genotype; Genotype-Tissue Expression Project; Haplotypes; Heritability; Heterozygote; Hour; Human Genome; Imagery; Individual; Internet; Light; Liquid substance; Metadata; Methods; Names; Phase; Phenotype; Population; Process; Publishing; Rare Diseases; Research; Research Training; Running; Scheme; Sequence Alignment; System; Technology; Tissues; Training; Trans-Omics for Precision Medicine; Utah; Variant; Work; base; cell type; cohort; data integration; disorder risk; epigenomics; experimental study; genetic variant; genome annotation; genome browser; genome sequencing; genomic data; improved; indexing; innovation; insight; multidimensional data; multiple omics; novel; operation; programs; rare variant; risk variant; search engine; statistics; tool; trait; transcriptome sequencing; web interface; whole genome

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.

项目摘要 尽管努力数十年，目前只能解释遗传疾病的一小部分。 关于这一差距的两个解释是，潜在的遗传变异很少见，目前未知，我们，我们 对我们确实拥有的变体的影响不足，特别是那些居住在 编码区域。解决这些问题需要更大的队列和更全基因组功能 测定（例如RNA-Seq，Chip-Seq，Atac-Seq等）。认识到公共中心等项目 遗传疾病（CC​​GD），精密医学的跨词（TopMed）程序和编码是 在许多不同的个体和组织中进行大量遗传数据收集。在 总体而言，这些数据将极大地提高我们了解变异如何影响基因组的能力 建筑学。挑战是这些数据是庞大，复杂，多维的，当前的方法 无法在此规模上操作。 该建议通过将数据分为两种不同类型的数据，基因型来解决这一挑战 和基因组注释以及开发用于存储和搜索每种类型的技术 独立。这两种高度估计的方法本身将非常有价值，然后将是 集成到一个单个系统中，该系统可以跨变异，基因表达和调节进行查询。为了 例如，考虑一个问题：“有没有从头变体具有差异的组织 富集与对照中的那些？“这个问题被分解为产生两个的基因型查询 一组变体：novos，以防万一。然后这些集合用作输入查询 基因组注释搜索所有组织中所有推定的增强子。 该建议建立在我最近发表的基因型查询工具（GQT）的基础上，该方法是 通过直接在压缩基因型索引上操作以及我的 过去的基因组算法研究和培训，我为此发表了多种小说 算法。到目前为止，我一直专注于方法，因此，该项目的K99阶段将包括 发展，它将独特地关注疾病队列的分析。额外的培训将是 独立研究计划的基础，该计划将释放大型基因组学的潜力和 功能数据集，为表型，基因型和功能之间的快速和流体整合提供 数据。