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

项目摘要 尽管近年来单分子测序（SMS）技术已经进步，使得常规测序成为可能。 由于人类基因组的测序和组装，需要新的软件来利用SMS在人类中的潜力， 遗传学长期目标是帮助我们更好地理解人类多样性的复杂变化， 在疾病中的作用。为了实现这一点，我们将开发方法来（1）检测SMS读数的变化，（2）组装 SMS从头组装中缺失的重复序列，以及（3）大型HTS中的基因型复杂变异 使用轻量级的数据结构。虽然针对SMS数据的几年算法开发已经 导致了一个软件生态系统来检测SMS基因组的变化，这是需要继续 目前的发展是，灵敏度和特异性还不足以用于疾病研究，重要的几类 目前的组装方法不能解决变异，从测序SMS获得的知识 基因组必须用于改善在严重依赖于短时间的大型疾病研究中可以发现的东西。 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 是一种在重排序列上显式优化的比对算法，一种组装方法， 利用重复拷贝之间的微小差异来解析基因组功能。将支持软件 通过Bioconda安装和分布式测试用例。一旦通过短信发现变种， 在短读数据中轻松进行基因分型。我们将开发方法来生成SMS变化的数据库， 用短读数据查询。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 这项工作的一个重要目的是使SMS基因组能够用于疾病研究， 变化，并通过增加在大型短读数据集中发现的变化量。

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.