Detection and annotation of structural variants from long-read sequencing

项目总结 这个项目的主要目标是开发一套计算工具来检测结构变量(SVS) 通过长阅读测序，并便于其注释和临床解释。尽管读得不多 测序已经在研究和临床环境中得到了广泛的应用，但由于序列测定的原因，它识别SVS的能力有限 重复元素的存在。已知致病SVS可通过短读测序遗漏， 潜在地导致临床基因组/外显子组测序的低诊断率(~30-40%)。缺乏 临床解释SVS的可靠工具进一步限制了我们识别导致 人类疾病。为了应对这些挑战，我们将开发LinkedSV来从链接读取中检测SVS 10倍基因组学平台产生的基因组和外显子组测序数据，并开发LongSV进行检测 来自PacBio和Nonopore的SVS长读测序数据。我们还将开发LabelSV来分析光学 来自Bionano基因组的作图数据，并通过整合千碱基分辨率SV来表征复杂的SVS 来自光学测绘的呼叫和来自排序平台的基本分辨率SV呼叫。最后，基于我们之前的 开发ANNOVAR和InterVar工具，我们将开发一种计算方法来促进临床 对SVS的解读。通过整合基因剂量敏感性、突变耐受性和表型信息， 这种方法有助于临床解释候选SVS的疾病表型。总而言之，我们的方法 将简化SV检测和变体解释的工作流程。我们将分发和维护 用户友好的软件工具，以实现所提出的SV检测方法，并生成可重现的 符合ACMG(美国医学院)当前和未来版本的可追溯结果 遗传学和基因组学)/AMP(分子病理学协会)指南。我们相信我们的方法 将大大改进SV检测，实现对SVS的一致解释，并促进实施 基因组引导的精准医学。

PROJECT SUMMARY The overarching goal of this project is to develop a suite of computational tools to detect structural variants (SVs) by long-read sequencing, and to facilitate their annotation and clinical interpretation. Although short-read sequencing has been widely used in research and clinical settings, it has limited ability to identify SVs due to the presence of repeat elements. It is known that pathogenic SVs might be missed by short-read sequencing, potentially contributing to the low diagnostic rates (~30-40%) in clinical genome/exome sequencing. The lack of reliable tools for clinical interpretation of SVs further limits our ability to identify mutations that contribute to human diseases. To address these challenges, we will develop LinkedSV to detect SVs from linked-read genome and exome sequencing data generated by the 10X Genomics platform, and develop LongSV to detect SVs from PacBio and Nonopore long-read sequencing data. We will also develop LabelSV to analyze optical mapping data from Bionano Genomics, and to characterize complex SVs by integrating kilobase-resolution SV calls from optical mapping and base-resolution SV calls from sequencing platforms. Finally, based on our prior development of ANNOVAR and InterVar tools, we will develop a computational method to facilitate clinical interpretation of SVs. By integrating gene dosage sensitivity, mutation intolerance, and phenotype information, this method helps clinical interpretation of candidate SVs on disease phenotypes. Taken together, our methods will streamline the workflow for SV detection and variant interpretation. We will distribute and maintain user-friendly software tools to implement the proposed SV detection methods, and to generate reproducible and traceable results that conform to the current and future versions of ACMG (American College of Medical Genetics and Genomics) / AMP (Association for Molecular Pathology) guidelines. We believe that our methods will substantially improve SV detection, enable consistent interpretation of SVs, and facilitate the implementation of genome-guided precision medicine.