权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Computational methods for variant calling and haplotyping using long-read sequencing technologies

使用长读长测序技术进行变异调用和单倍型分析的计算方法

基本信息

批准号：
10441522
负责人：
Vikas Bansal
金额：
$ 38.16万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10441522
关键词：
Address Algorithms Bar Codes Benchmarking Biological Sciences Catalogs Characteristics Chromosome Mapping Clinical Code Complex Computing Methodologies DNA Data Data Set Detection Diploidy Disease Evolution Exhibits Gene Duplication Generations Genes Genetic Diseases Genetic Variation Genetic study Genome Genomics Genotype Goals Haplotypes Hereditary Nonpolyposis Colorectal Neoplasms Human Genetics Human Genome Individual Libraries Link Malignant Neoplasms Maps Massive Parallel Sequencing Medical Medical Genetics Mendelian disorder Methods Modeling Mutation PMS2 gene Phase Population Population Genetics Population Study Preparation Repetitive Sequence Sequence Homologs Sequence Homology Single Nucleotide Polymorphism Software Tools Technology Variant base computerized tools disease-causing mutation genetic analysis genome sequencing hearing impairment human disease human genome sequencing human reference genome improved innovation insertion/deletion mutation multiple datasets nanopore next generation sequencing prevent public health relevance single molecule tool variant detection virtual whole genome

项目摘要

Project Summary/Abstract In this project, we propose to develop computational methods and tools for whole-genome haplotyping and small variant calling using long-read sequencing technologies such as Pacific Biosciences and Oxford Nanopore and linked-read technologies. Haplotype information is crucial for interpretation of genetic variation in individual genomes, disease mapping, clinical genomics and several other analysis of human genetic variation. The lack of phase or haplotype information in human genomes sequenced using short reads is a major barrier in identifying disease associations with compound heterozygous mutations. More than 600 genes overlap segmental duplications with high sequence identity and variants in more than 100 such genes have been associated with rare Mendelian disorders and complex diseases including cancer. The inability to detect variants with high accuracy in duplicated regions of the genome using short-read sequencing technologies reduces the ability to identify disease causing mutations in medical genetics studies. In Aim 1, we will develop a general computational method for long-read based diploid genotyping that will enable accurate haplotyping for single nucleotide variants and short indels using long-read and linked-reads as well as accurate small variant calling using SMS technologies. In Aim 2, we will develop computational methods for sensitive mapping of SMS reads and accurate variant calling in repetitive regions of the human genome that are currently excluded from benchmark small variant call sets for reference human genomes. Finally, in Aim 3, we will leverage the methods from Aims 1 and 2 to perform variant calling on multiple genomes sequenced using SMS technologies to catalog variant PSVs and leverage this catalog to improve read mapping and variant calling accuracy of short-read sequencing in repetitive regions of the genome. We will implement the methods in robust and computationally efficient software tools and benchmark their accuracy using publicly available long- read sequence datasets for multiple human genomes of diverse ancestries.

项目总结/摘要在这个项目中，我们建议开发全基因组单体型分析的计算方法和工具，使用长读序测序技术（如Pacific Biosciences和Oxford）的小变异识别纳米孔和链接读取技术。单倍型信息是解释遗传变异的关键在个体基因组、疾病图谱、临床基因组学和人类遗传学的其他几种分析中，变化量在使用短读段测序的人类基因组中缺乏相位或单体型信息是一个潜在的问题。在确定疾病与复合杂合突变的关系方面存在主要障碍。600多个基因在100多个这样的基因中具有高序列同一性的重叠片段重复和变体，与罕见的孟德尔疾病和包括癌症在内的复杂疾病有关。无法检测使用短读测序技术在基因组重复区域中具有高准确性的变体降低了在医学遗传学研究中识别致病突变的能力。在目标1中，我们将开发一种基于长读段的二倍体基因分型的通用计算方法，对于单核苷酸变体和短插入缺失，使用长读段和连接读段以及精确的小插入缺失，使用SMS技术的变体呼叫。在目标2中，我们将开发敏感映射的计算方法的SMS读取和准确的变异呼叫在人类基因组的重复区域，目前是从参考人类基因组的基准小变异调用集合中排除。在目标3中，我们将利用目标1和2中的方法，对使用SMS测序的多个基因组进行变异识别技术来对变体PSV进行编目，并利用该编目来改进读段映射和变体调用在基因组的重复区域中短读测序的准确性。我们将在强大和计算效率高的软件工具，并使用公开的长期可用的基准，读取不同祖先的多个人类基因组的序列数据集。