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: 9906038
• 负责人: Mark Chaisson
• 金额: $ 38.9万
• 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 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9906038
• 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. ​

标题：代表泛基因组中的结构单倍型和复杂的遗传变异 图表。 项目概要 泛基因组图 (PGG) 参考必须忠实反映拷贝数不同的结构单倍型， 顺序和方向，目前在线性参考序列中很难表示。 重点关注复制变量最多和最复杂的区域，包括片段重复 (SD)、倒置、 短串联重复序列/可变数量串联重复序列（拷贝数可变重复序列，CNVR）和 参考基因组中经常被排除或折叠的其组合。 该项目旨在开发能够构建全染色体参考的工具基础设施 包含所有这些困难类别的序列的单倍型有四个具体目标。 开发从单倍型定相从头组装构建 PGG 的方法，确保图表反映 拷贝数变异和重复结构，包括 CNVR 和 SD 其次，我们将开发软件。 这将扩展 SD 组装方法，以促进 PGG 中 SD 基因座的管理。我们将使用 SD 组装。 检测重复的各个副本特有的变体，称为旁系同源特异变体（PSV），以及 提供通过描述不同副本的 PGG 重建本地单倍型路径的软件。 我们将设计新方法来利用单细胞模板链 DNA 测序数据 (Strand-seq) 映射 到 PGG 以便将染色体长度的“结构单倍型”穿过图表。 软件工具将允许对包含全谱结构变异的单倍型进行物理解析， 凭借 PSV，结构单倍型也将嵌入，包括倒位和倒置重复。 第四，我们将开发一个可扩展的开源软件框架，以系统地解决问题。 评估如何在 PGG 中包含单核苷酸变异、短插入缺失和结构变异类别 影响短读数据的变异检测这将能够优化编码的复杂性。 用于短读长变异检测的 PGG 此外还将提供多态性和多态性的全面视图。 我们将开发检测等位基因特异性 k-mers 的工具并演示如何进行。 它能够根据短读数据集的 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达的框架，我们将专注于提高效率， 可扩展性和计算简便性，可满足遗传学和基因组领域广泛用户的需求 该提案将确保人类基因组最复杂的区域被编码到 最终评估 PGG 和潜在的遗传变异与疾病的关联。 ​