Detection and genotyping complex human genetic variation using single-molecule sequencing

使用单分子测序对复杂的人类遗传变异进行检测和基因分型

• 批准号: 10447193
• 负责人: Mark Chaisson
• 金额: $ 41.25万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10447193
• 关键词: Algorithms; Complex; Computer software; Copy Number Polymorphism; DNA Sequence Rearrangement; Data; Data Set; Databases; Detection; Development; Diagnostic; Disease; Ecosystem; Frequencies; Genetic Diseases; Genetic Predisposition to Disease; Genetic Variation; Genome; Genotype; Goals; Haplotypes; Heritability; High-Throughput Nucleotide Sequencing; Human; Human Genetics; Human Genome; Individual; Knowledge; Length; Maps; Methods; Minisatellite Repeats; Minor; Optics; Population; Repetitive Sequence; Research; Resources; Role; Sensitivity and Specificity; Sequence Alignment; Single Nucleotide Polymorphism; Software Tools; Structure; Technology; Testing; Trans-Omics for Precision Medicine; Variant; Work; algorithm development; base; cohort; contig; genome sequencing; improved; innovation; insertion/deletion mutation; light weight; novel; pan-genome; single molecule; variant detection

Project summary Although single-molecule sequencing (SMS) technologies have advanced in recent years to enable routine sequencing and assembly of human genomes, new software is required to utilize the potential of SMS in human genetics. The long term goal is to help improve our understanding of complex variation in human diversity and its role in disease. To achieve this, we will develop methods to (1) detect variation in SMS reads, (2) assemble duplicated sequences missing from SMS de novo assemblies, and (3) genotype complex variation in large HTS datasets using lightweight data structures. While several years of algorithm development for SMS data have resulted in an software ecosystem to detect variation in SMS genomes, the rationale for the need to continue development is that sensitivity and specificity are not yet sufficient for disease studies, important classes of variation are not resolved by current assembly approaches, and the knowledge gained from sequencing SMS genomes must be used to improve what can be discovered in large disease studies that rely heavily on short read data such as those conducted under TOPMed. The algorithmic innovations we will provide for SMS data are an alignment algorithm that explicitly optimizes over rearranged sequences, an assembly approach that exploits minor differences between duplication copies to resolve genome function. Software will be supported through Bioconda installation and distributed test cases. Once a variant is discovered by SMS, it may be more easily genotyped in short read data. We will develop methods to generate databases of SMS variation that may be queried with short read data. To aid in development of assembly algorithms for duplicated sequences, we will generate a public resource of SMS data for individuals with known copy number polymorphisms. The significance of this work is to enable SMS genomes to be used in disease studies, both by uncovering previously hidden variation, and by increasing the amount of variation found in large short-read datasets.

项目概要 尽管单分子测序 (SMS) 技术近年来取得了进步，使得常规测序成为可能 人类基因组的测序和组装，需要新的软件来利用短信在人类中的潜力 遗传学。长期目标是帮助提高我们对人类多样性和复杂变异的理解 它在疾病中的作用。为了实现这一目标，我们将开发方法来（1）检测短信读取的变化，（2）组装 SMS de novo 组装中缺少重复序列，以及 (3) 大型 HTS 中的基因型复杂变异 使用轻量级数据结构的数据集。虽然短信数据算法的多年开发已经 产生了一个软件生态系统来检测 SMS 基因组的变异，这是需要继续下去的理由 发展的一个问题是敏感性和特异性还不足以用于疾病研究，重要的类别 当前的组装方法无法解决变异问题，并且从 SMS 测序中获得的知识 基因组必须用于改进严重依赖短时间的大型疾病研究中可以发现的内容 读取数据，例如在 TOPMed 下进行的数据。我们将为短信数据提供的算法创新 是一种显式优化重排序列的比对算法，是一种组装方法 利用重复拷贝之间的微小差异来解决基因组功能。软件将得到支持 通过 Bioconda 安装和分布式测试用例。一旦通过短信发现变种，可能会导致更多 很容易在短读数据中进行基因分型。我们将开发生成短信变体数据库的方法，这些数据库可能 通过短读数据进行查询。为了帮助开发重复序列的组装算法，我们将 为具有已知拷贝数多态性的个体生成 SMS 数据的公共资源。意义 这项工作的目的是通过揭示以前隐藏的信息，使 SMS 基因组能够用于疾病研究 变异，以及增加大型短读数据集中发现的变异量。