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.

项目概要该项目的总体目标是开发一套计算工具来检测结构变异（SV）通过长读长测序，并促进其注释和临床解释。虽然读得短测序已广泛应用于研究和临床环境中，但由于重复元素的存在。众所周知，短读长测序可能会漏掉致病性 SV，可能导致临床基因组/外显子组测序诊断率低（~30-40%）。缺乏对 SV 进行临床解释的可靠工具进一步限制了我们识别导致突变的能力人类疾病。为了应对这些挑战，我们将开发 LinkedSV 来检测链接读取中的 SV 10X Genomics平台生成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 长读长测序数据的 SV。我们还将开发 LabelSV 来分析光学映射来自 Bionano Genomics 的数据，并通过集成千碱基分辨率 SV 来表征复杂的 SV 来自光学映射的调用和来自测序平台的碱基分辨率 SV 调用。最后，根据我们之前的 ANNOVAR 和 InterVar 工具的开发，我们将开发一种计算方法来促进临床 SV 的解释。通过整合基因剂量敏感性、突变不耐受性和表型信息，该方法有助于对疾病表型的候选 SV 进行临床解释。综合起来，我们的方法将简化 SV 检测和变异解释的工作流程。我们将分发和维护用户友好的软件工具来实现所提出的 SV 检测方法，并生成可重复的和符合 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显着改善 SV 检测，实现 SV 的一致解释，并促进实施基因组引导的精准医学。

项目成果

期刊论文数量（17）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions.

PrecisionFDA Truth Challenge V2：从难以绘制区域的短读和长读中调用变体。

DOI：
发表时间：
2022-05-11
期刊：
影响因子：
0
作者：
Olson, Nathan D;Wagner, Justin;McDaniel, Jennifer;Stephens, Sarah H;Westreich, Samuel T;Prasanna, Anish G;Johanson, Elaine;Boja, Emily;Maier, Ezekiel J;Serang, Omar;Jáspez, David;Lorenzo;Muñoz;Rubio
通讯作者：
Rubio

LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data.

LinkedSV 用于从链接读取外显子组和基因组测序数据中检测镶嵌结构变异。

DOI：
发表时间：
2019
期刊：
影响因子：
16.6
作者：
Fang, Li;Kao, Charlly;Gonzalez, Michael V;Mafra, Fernanda A;Pellegrino da Silva, Renata;Li, Mingyao;Wenzel, Sören;Wimmer, Katharina;Hakonarson, Hakon;Wang, Kai
通讯作者：
Wang, Kai

An international virtual hackathon to build tools for the analysis of structural variants within species ranging from coronaviruses to vertebrates.

一场国际虚拟黑客马拉松，旨在构建用于分析从冠状病毒到脊椎动物等物种内结构变异的工具。

DOI：
10.12688/f1000research.51477.1
发表时间：
2021-03-26
期刊：
F1000Research
影响因子：
0
作者：
Ann M. Mc Cartney;M. Mahmoud;M. Jochum;D. P. Agustinho;B. Zorman;Ahmad Al Khleifat;Fawaz Dabbaghie;Rupesh K. Kesharwani;Moritz Smolka;Moez Dawood;Dreycey Albin;E. Aliyev;Hakeem G. Almabrazi;Ahmed Arslan;Advait Balaji;S. Behera;Kimberley J. Billingsley;Daniel L. Cameron;Joyjit Daw;Eric T. Dawson;W. D. Coster;Haowei Du;Christopher Dunn;Rocío Esteban;Angad Jolly;D. Kalra;Chunxiao Liao;Yunxi Liu;Tsung;James M. Havrilla;Michael M. Khayat;Maximillian G. Marin;Jean Monlong;S. Price;A. Gener;Jingwen Ren;S. Sagayaradj;Nicolae Sapoval;Claude Sinner;Daniela C. Soto;Arda Soylev;Arun K. Subramaniyan;Najeeb Syed;Neha Tadimeti;Pamella Tater;Pankaj Vats;Justin N. Vaughn;K. Walker;Gaojianyong Wang;Qi;ong Zeng;ong;Shangzhe Zhang;Tingting Zhao;Bryce Kille;Evan Biederstedt;Mark J. P. Chaisson;A. English;Zev N. Kronenberg;T. Treangen;T. Hefferon;Chen;Ben Busby;F. Sedlazeck
通讯作者：
F. Sedlazeck

LongGF: computational algorithm and software tool for fast and accurate detection of gene fusions by long-read transcriptome sequencing.

LongGF：通过长读长转录组测序快速准确检测基因融合的计算算法和软件工具。