Detection and annotation of structural variants from long-read sequencing

长读长测序结构变异的检测和注释

基本信息

批准号：
10378720
负责人：
Kai Wang
金额：
$ 44万
依托单位：
CHILDREN'S HOSP OF PHILADELPHIA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10378720
关键词：
Address Adult Affect Alleles American Bar Codes Bioinformatics CLIA certified Clinic Clinical Complex Computing Methodologies Coupled DNA Sequence Alteration Data Data Coordinating Center Databases Detection Development Diagnostic Disease Elements Future Gene Dosage Generations Genes Genetic Genome Genomics Goals Guidelines Hereditary Disease Individual Length Link Malignant Neoplasms Maps Masks Measures Medical Genetics Methods Mosaicism Mutation Neurology Optics Pathogenicity Patients Pediatric Hospitals Pennsylvania Phenotype Philadelphia Play Positioning Attribute Procedures Records Repetitive Sequence Reproducibility Research Resolution Role Sequence Alignment Software Tools Technology Time Twin Multiple Birth Universities Variant annotation system base chromothripsis clinical sequencing computational suite computerized tools cost design detection method disease phenotype exome sequencing genome sequencing genomic platform human disease implementation facilitation improved individual patient innovation insertion/deletion mutation medical schools molecular pathology nanopore novel personalized genomic medicine precision medicine prevent sequencing platform tool user friendly software variant detection whole genome

项目摘要

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.

项目摘要该项目的总体目标是开发一套计算工具来检测结构变体（SVS）通过长阅读测序，并促进其注释和临床解释。虽然简短阅读测序已广泛用于研究和临床环境中，由于该测序的能力有限存在重复元素。众所周知，病原SV可能会通过短读测序遗漏，临床基因组/外显子组测序中有可能导致低诊断率（约30-40％）。缺乏可靠的SV临床解释工具进一步限制了我们识别有助于的突变的能力人类疾病。为了应对这些挑战，我们将开发LinkedSV，以检测链接阅读的SV 10倍基因组平台生成的基因组和外显子组测序数据，并开发LONGSV来检测来自PACBIO和NONOPORE长阅读测序数据的SV。我们还将开发labelsv来分析光学映射来自Bionano基因组学的数据，并通过整合千目标SV来表征复杂的SV 来自光学映射和基础分辨率SV调用来自测序平台的呼叫。最后，根据我们的先前开发Annovar和Intervar工具，我们将开发一种计算方法来促进临床 SVS的解释。通过整合基因剂量敏感性，突变不耐症和表型信息，这种方法有助于对疾病表型的候选SV进行临床解释。总之，我们的方法将简化用于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来实现建议的SV检测方法，并生成可重复的和可追溯的结果符合ACMG的当前和未来版本（美国医学院遗传学和基因组学） / AMP（分子病理协会）指南。我们相信我们的方法将基本上改善SV检测，实现对SV的一致解释并促进实施基因组引导的精密医学。