Representing structural haplotypes and complex genetic variation in pan-genome graphs

表示泛基因组图中的结构单倍型和复杂的遗传变异

• 批准号: 10337078
• 负责人: Mark Chaisson
• 金额: $ 30万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-10 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10337078
• 关键词: Address; Affect; Alleles; Attention; Cells; Chromosomes; Complement; Complex; Computer software; Copy Number Polymorphism; DNA sequencing; Data; Data Set; Development; Disease; Ensure; Felis catus; Future; Genetic; Genetic Variation; Genome; Genotype; Goals; Graph; Haplotypes; Human; Human Characteristics; Human Genetics; Human Genome; Individual; Infrastructure; Large-Scale Sequencing; Length; Maps; Measures; Methodology; Methods; Minisatellite Repeats; Phase; Population; Resolution; Short Tandem Repeat; Single Nucleotide Polymorphism; Single base substitution; Software Framework; Software Tools; Structure; Variant; base; data standards; design; genome sciences; improved; insertion/deletion mutation; interoperability; novel; open source; pan-genome; paralogous gene; reconstruction; reference genome; software development; success; tool; user-friendly; variant detection; working group

Title: Representing structural haplotypes and complex genetic variation in pan-genome graphs. PROJECT SUMMARY A pan-genome graph (PGG) reference must faithfully reflect structural haplotypes that differ in copy number, order, and orientation, which are currently poorly represented in a linear reference sequence. This effort focuses on the most copy variable and complex regions, including segmental duplications (SDs), inversions, short tandem repeats/variable number tandem repeats (copy-number-variable repeats, CNVRs) and combinations thereof that are frequently excluded or collapsed in reference genomes. The overarching goal of this project is to develop the tool infrastructure enabling the construction of whole-chromosome reference haplotypes that include all of these difficult classes of sequence. There are four specific aims. First, we will develop methods to construct PGGs from haplotype-phased de novo assemblies, ensuring the graph reflects both copy number variation and repeat structure, including CNVRs and SD. Second, we will develop software that will expand SD assembly methods to facilitate the curation of SD loci in PGGs. We will use SD assembly to detect variants specific to individual copies of a duplication, called paralog-specific variants (PSVs), and provide software to reconstruct local haplotype paths through the PGG that describe the different copies. Third, we will design novel methods to exploit single-cell template strand DNA sequencing data (Strand-seq) mapped to PGGs in order to thread chromosome-length "structural haplotypes" through the graph. Therefore, our software tool will allow the physical resolution of haplotypes comprising the full spectrum of structural variation, including inversions and inverted duplications. By virtue of the PSVs, the structural haplotypes will also embed sequence-resolved SDs. Fourth, we will develop a scalable open-source software framework to systematically assess how the inclusion of single-nucleotide variants, short indels, and structural variant classes in the PGG affects variant detection with short-read data. This will enable the optimization of the complexity encoded in the PGG for short-read variant detection. It will additionally provide a comprehensive view on polymorphic and fixed k-mers in human populations. We will develop tools to detect allele-specific k-mers and demonstrate how that enables the rapid genotyping of variants in the PGG based on k-mer composition of a short-read dataset. Once the framework for enhanced genome representation is established, we will focus on improving efficiency, scalability, and computational ease to cater to the needs of a broad range of users in genetics and genome science. This proposal will ensure that the most complex regions of the human genome are encoded into the PGG and that underlying genetic variation is ultimately assessed for association with disease. ​

泛基因组中代表结构单倍型和复杂遗传变异 图表。 项目总结 泛基因组图(PGG)参考必须忠实地反映拷贝数不同的结构单倍型， 顺序和取向，它们目前在线性参考序列中表示得很差。这一努力 重点研究复制最可变和最复杂的区域，包括片段复制(SD)、倒置、 短串联重复序列/可变数目串联重复序列(拷贝数可变重复序列，CNVR)和 它们的组合在参考基因组中经常被排除或崩溃。的首要目标是 这个项目是为了开发工具基础设施，使之能够构建全染色体参考 包括所有这些困难的序列类别的单倍型。有四个具体目标。首先，我们将 开发从单倍型阶段从头组装构建PGG的方法，确保图形反映 拷贝数变异和重复结构，包括CNVR和SD。第二，我们将开发软件 这将扩展SD组装方法，以促进PGG中SD基因座的筛选。我们将使用SD组件 要检测复制的各个副本的特定变体，称为平行对数特定变体(PSV)，以及 提供软件以通过描述不同拷贝的PGG重建本地单倍型路径。第三, 我们将设计新的方法来利用单细胞模板链DNA测序数据(Strand-Seq) 为了将染色体长度的“结构单倍型”贯穿整个图表。因此，我们的 软件工具将允许包括结构变异的全谱的单倍型的物理分辨率， 包括倒置和倒置复制。凭借PSV，结构单倍型也将嵌入 序列解析的十二烷基硫酸酯。第四，我们将开发一个可扩展的开源软件框架，以系统地 评估如何在PGG中包括单核苷酸变体、短INDELs和结构变体类别 影响读取时间较短的数据的变量检测。这将能够优化编码在 用于短读变异检测的PGG。此外，它还将提供有关多态和 修复了人类群体中的k-MERS。我们将开发工具来检测等位基因特异的k-MERS，并演示如何 这使得能够基于短读数据集的k-mer组成对PGG中的变异进行快速基因分型。 一旦建立了增强基因组表示的框架，我们将专注于提高效率， 可伸缩性和计算简易性，以满足广泛的遗传学和基因组用户的需求 科学。这一提议将确保人类基因组中最复杂的区域被编码到 这种潜在的遗传变异最终被评估为与疾病有关。 ​