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: 10576268
• 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 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10576268
• 关键词: Acute; Affect; 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; Genome Mappings; Genomics; Genotype; Goals; Human; Human Genome; Individual; Investments; Laboratories; Large-Scale Sequencing; Learning; 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; Specificity; Speed; Structure; Systematic Bias; Techniques; Technology; Training; Trans-Omics for Precision Medicine; United States National Institutes of Health; Untranslated RNA; Variant; algorithm development; convolutional neural network; deep learning; deep learning model; developmental disease; dosage; empowerment; 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; sobriety; 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 结构变异（SV）是包括拷贝数变异（CNVs）在内的多种基因组变异 如缺失和重复，以及平衡重排，如倒置和互惠 易位一个典型的人类基因组含有超过4,000个大于300 bp的SV，并且它们的大尺寸会增加 删除或复制基因、破坏染色质结构和改变表达的潜力。尽管他们 尽管SV的流行率和表型后果的潜在性，但SV仍然非常难以检测和基因分型， 具有高精度。这一困难的大部分是由DNA序列比对“信号”指示的事实驱动的。 SV比单核苷酸和插入缺失变体复杂得多。与SNP比对不同 仅在等位基因状态中变化，支持SV的比对在状态中变化（支持替代结构或不支持替代结构） 对齐位置和类型。因此，SV发现的准确性远低于SNP， INDEL。此外，SV管道的规模很小，难以运行。这些挑战是一个障碍， 单基因组分析和家庭研究必须投入大量的努力，以消除虚假的海洋， 积极的。对于大规模测序工作，这些问题变得指数级地更加严重，例如 TOPmed，常见疾病遗传学中心和我们所有人计划。软件效率是关键 这类项目的可扩展性。然而，同样重要的是全面、准确的发现。 基于十多年的软件开发经验， 在疾病背景下，我们投入了大量精力来了解准确性不足的原因， 用于SV发现。这些努力，加上我们在这一领域的研发经验， 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 SNP/INDEL变异和结构变异发现之间的差距。这些发展将使 在不同的背景下研究人类基因组，因此将产生广泛的影响。我们的目标是： 1.开发深度学习模型来纠正序列深度的系统性变化。这款新机 学习模型将纠正DNA序列深度的系统偏差，并显着提高 发现缺失和重复。 2.提高SV检测和基因分型的速度、可扩展性和准确性。使用新的算法， 我们将使SV检测的准确性更接近SNP和INDEL发现的准确性， 准确的SV发现将大规模部署。 3.从群体规模的基因组分析中创建SV的基因组约束图。我们将部署 我们的新方法来检测和基因型之间的结构变异成千上万的人类基因组。 由此产生的SV图谱将使SV的基因组约束模型的创建成为可能，并使新的 软件来预测有害的SV，特别是在非编码基因组中。

项目成果

trfermikit: a tool to discover VNTR-associated deletions.

• DOI: 10.1093/bioinformatics/btab805
• 发表时间: 2022-02-07
• 期刊: Bioinformatics (Oxford, England)
• 作者: McHale P;Quinlan AR
• 通讯作者: Quinlan AR

Annotation of structural variants with reported allele frequencies and related metrics from multiple datasets using SVAFotate.

• DOI: 10.1186/s12859-022-05008-y
• 发表时间: 2022-11-16
• 期刊: BMC BIOINFORMATICS
• 影响因子: 3
• 作者: Nicholas, Thomas J.;Cormier, Michael J.;Quinlan, Aaron R.
• 通讯作者: Quinlan, Aaron R.