Scalable detection and interpretation of structural variation in human genomes

人类基因组结构变异的可扩展检测和解释

• 批准号: 10341175
• 负责人: Aaron R Quinlan
• 金额: $ 69.2万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10341175
• 关键词: Acute; Affect; Algorithmic Software; Algorithms; All of Us Research Program; Alleles; Area; Automobile Driving; Biological Assay; Chromatin Structure; Chromosome Structures; Clip; Cloud Computing; Code; Communities; Complex; Computer software; Copy Number Polymorphism; DNA; DNA Sequence; Data; Data Reporting; Detection; Development; Disease; Environment; Error Sources; Exhibits; Family Study; Funding; Future; Gene Duplication; Gene Expression; Gene Fusion; Gene Structure; Genetic; Genetic Diseases; Genetic Variation; Genome; Genomics; Genotype; Goals; Human; Human Genome; Individual; Laboratories; Large-Scale Sequencing; Location; Maps; Methods; Modeling; Noise; Nucleotides; Paint; Pathogenicity; Performance; Phenotype; Population; Positioning Attribute; Prevalence; Process; Reciprocal Translocation; Research; Running; Sampling; Sea; Sensitivity and Specificity; Sequence Alignment; Series; Signal Transduction; Software Tools; Source; Speed; Structure; Systematic Bias; Techniques; Technology; Training; Trans-Omics for Precision Medicine; United States National Institutes of Health; Untranslated RNA; Variant; algorithm development; base; convolutional neural network; deep learning; deep learning model; developmental disease; dosage; exome; experience; genome analysis; genome sequencing; genome-wide; human disease; improved; innovation; insertion/deletion mutation; insight; large datasets; machine learning model; method development; nanopore; novel; prevent; research and development; software development; success; tool; variant detection; whole genome

PROJECT SUMMARY Structural variation (SV), is a diverse class of genome variation that includes copy number variants (CNVs) such as deletions and duplications, as well as balanced rearrangements, such as inversions and reciprocal translocations. A typical human genome harbors >4,000 SVs larger than 300bp and their large size increases the potential to delete or duplicate genes, disrupt chromatin structure, and alter expression. Despite their prevalence and potential for phenotypic consequence, SVs remain notoriously difficult to detect and genotype with high accuracy. Much of this difficulty is driven by the fact DNA sequence alignment “signals” indicating SVs are far more complex than for single-nucleotide and insertion deletion variants. Unlike SNP alignments that vary only in allele state, alignments supporting SVs vary in state (supports an alternate structure or not) alignment location, and type. Consequently, the accuracy of SV discovery is much lower than that of SNPs and INDELs. Furthermore, SV pipelines scale poorly and are difficult to run. These challenges are a barrier for single genome analysis and studies of families must invest substantial effort into eliminating a sea of false positives. These problems become exponentially more acute for large-scale sequencing efforts such as TOPmed, the Centers for Common Disease Genetics, and the All of Us program. Software efficiency is key to scalability for such projects. However, of equal importance is comprehensive, accurate discovery. Building upon more than a decade of software development experience and analyzing SV in diverse disease contexts, we have invested significant effort into understanding the causes of the insufficient accuracy for SV discovery. These efforts, together with our research and development experience in this area, give us unique insight into improving the accuracy and scalability of SV discovery. Our goal is to narrow the accuracy gap between SNP/INDEL variation and structural variation discovery. These developments will empower studies of human genomes in diverse contexts and will therefore have broad impact. Our goals are to: 1. Develop a deep learning model to correct systematic variation in sequence depth. This new machine learning model will correct systematic biases in DNA sequence depth and dramatically improve the discovery of deletions and duplications. 2. Improve the speed, scalability, and accuracy of SV detection and genotyping. Using new algorithms, we will bring the accuracy of SV detection much closer to that of SNP and INDEL discovery and allow accurate SV discovery to be deployed at scale. 3. Create a map of genomic constraint for SV from population-scale genome analysis. We will deploy our new methods to detect and genotype structural variation among tens of thousands of human genomes. The resulting SV map will empower the creation of a model of genomic constraint for SV and enable new software to predict deleterious SVs, especially in the noncoding genome.

项目摘要 结构变化（SV）是一种潜水类基因组变异，其中包括拷贝数变体（CNV） 例如删除和复制，以及平衡的重排，例如反转和相互 易位。典型的人类基因组港口> 4,000 svs大于300bp，其大尺寸增加 删除或重复基因，破坏染色质结构并改变表达的潜力。尽管他们 出现表型后果的流行率和潜力，SV仍然难以检测和基因型 具有很高的精度。大部分困难是由DNA序列对准“信号”驱动的，表明 SV比单核苷酸和插入删除变体要复杂得多。与SNP对齐不同 仅在等位基因状态下，支持SVS的一致性在状态下有所不同（是否支持替代结构） 对齐位置和类型。因此，SV发现的准确性远低于SNP和SNP的准确性 indels。此外，SV管道规模较差，难以运行。这些挑战是一个障碍 单个基因组分析和对家庭的研究必须大力消除虚假的海洋 积极的。对于大规模测序工作，例如 最高的是普通疾病遗传学中心和我们所有人的计划。软件效率是关键 此类项目的可扩展性。但是，同等重要的是全面，准确的发现。 基于十多年的软件开发经验并分析潜水员的SV 疾病环境，我们已经投入了大量精力来理解准确性不足的原因 SV发现。这些努力，以及我们在这一领域的研发经验，给我们 对提高SV发现的准确性和可扩展性的独特见解。我们的目标是缩小准确性 SNP/indel变化与结构变化发现之间的差距。这些发展将授权 对潜水员背景下人类基因组的研究将产生广泛的影响。我们的目标是： 1。开发一个深度学习模型，以纠正序列深度的系统变化。这台新机器 学习模型将纠正DNA序列深度的系统偏见，并显着改善 发现删除和重复。 2。提高SV检测和基因分型的速度，可伸缩性和准确性。使用新算法， 我们将使SV检测的准确性更接近SNP和Indel Discovery，并允许 精确的SV发现将大规模部署。 3。根据人群规模的基因组分析，为SV创建基因组约束图。我们将部署 我们在数以万计的人类基因组中检测和基因型结构变异的新方法。 由此产生的SV地图将赋予SV基因组约束模型的创建，并启用新的 预测有害SV的软件，尤其是在非编码基因组中。