Structural variation analysis with and without a reference genome

有和没有参考基因组的结构变异分析

• 批准号: 10212425
• 负责人: Zechen Chong
• 金额: $ 36万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-07 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10212425
• 关键词: Address; Advanced Development; Algorithms; Architecture; Complex; Computational algorithm; Data; Detection; Evaluation; Event; Genes; Genetic Variation; Genome; Genomic Segment; Genomics; Genotype; Goals; Haplotypes; Health; Inherited; Knowledge; Laboratories; Methods; Mission; Mutation; Organism; Outcome; Pathogenicity; Phenotype; Predisposition; Public Health; Research; Source; Structure; Technology; Testing; United States National Institutes of Health; Variant; Work; analytical method; base; disease-causing mutation; improved; innovation; insight; nanopore; new technology; novel; reference genome; sequencing platform; tool; trait

Structural variations (SVs) analysis is very important because they are a major source of genetic variations and account for a wide range of phenotypes in many species. To better understand their contribution to diversity, divergence, and a variety of phenotypic traits, we should address two critical issues for SV analysis: accurate SV characterization and understanding their formation mechanisms. Without accurate SV results, we may miss the SV events that account for the phenotypes. Without understanding their formation mechanisms, we may not distinguish the phenotype associated SVs from other SVs. As the sequencing technology evolves, many new sequencing platforms such as PacBio, Oxford Nanopore, and 10X Genomics with longer sequencing reads appeared and have demonstrated great potential. However, the computational algorithms for SV analysis are inadequate for organisms both with and without a reference genome and SV mechanism analysis was merely based on short (<10bp mostly) breakpoint junction sequences due to technical limitations. As more of such data is being generated, there is an urgent need to fill in the gap by developing more accurate and efficient algorithms for SV discovery and establishing an innovative way to investigate SV formation mechanisms. The long-term goal of the laboratory is to comprehensively characterize all forms of SVs and understand their functional consequences and formation mechanisms. The goals of the next three years are to develop efficient algorithms to SV analysis for organisms both with and without a reference. We will focus on large insertions, inversions, and complex SVs which are always underrepresented. For organisms with a reference, we will develop a de novo assembly evaluation method to optimize existing tools and/or develop new assembly methods. Given these toolkits, the goals for the following two years are to study the SV formation mechanisms based on global genomic architecture. Our central hypothesis is that there may be some hotspots, signatures around the SV locus either inherited from paternal or maternal genomes causing the rearrangement formation susceptibility. We will test the hypothesis based on investigating a global and haplotype picture of SVs using the new sequencing platforms. It is expected that the research will contribute a suite of robust methods on the long-read sequencing data to identify all forms of SVs with high sensitivity and precision. Besides, it is expected that this work will provide novel insights into SV formation mechanisms. The proposed work is innovative in that the proposed computational approach will greatly improve the sensitivity and precision for SV detection using long sequencing reads under the circumstances of both with and without a reference genome. Also, the outcomes of this work may vertically advance the SV mechanism research. The proposed research is significant because it will facilitate the discovery of pathogenic variations and the establishment of the association between genotype and phenotype. It may also popularize the usage of new sequencing platforms to address novel scientific questions.

结构变异（SV）分析非常重要，因为它们是遗传变异的主要来源， 解释了许多物种中广泛的表型。为了更好地了解他们对多样性的贡献， 分歧和各种表型性状，我们应该解决SV分析的两个关键问题： 准确的SV表征和了解其形成机制。没有准确的SV 结果，我们可能会错过SV事件，占表型。如果不了解它们的形成 机制，我们可能无法区分表型相关的SV从其他SV。随着测序 技术的发展，许多新的测序平台，如PacBio，牛津纳米孔，和10 X基因组学 出现了更长的测序读数，并显示出巨大的潜力。但是，计算 SV分析的算法对于具有和不具有参考基因组的生物体都是不充分的，并且SV 机制分析仅基于短（大多<10 bp）的断点连接序列， 技术限制。随着越来越多的此类数据的生成，迫切需要通过以下方式填补差距： 开发更准确和有效的SV发现算法，并建立创新的方法， 研究SV形成机制。该实验室的长期目标是全面表征 所有形式的SV，并了解其功能后果和形成机制。的目标 未来三年的目标是开发有效的算法，以SV分析生物体，无论是有还是没有 参考我们将重点关注大插入，反转和复杂的SV，它们总是代表性不足。 对于有参考的生物，我们将开发一种从头组装评估方法，以优化现有的 工具和/或开发新的组装方法。有了这些工具包，今后两年的目标是 基于全局基因组结构研究SV的形成机制。我们的核心假设是， 可能是一些热点，SV基因座周围的签名，或者从父亲或母亲的基因组遗传 导致重排形成敏感性。我们将通过调查一个全球性的 和SV的单倍型图片。预计这项研究将 贡献了一套强大的方法对长读测序数据，以确定所有形式的SV与高 灵敏度和精确度。此外，预计这项工作将为SV形成提供新的见解 机制等所提出的工作是创新的，因为所提出的计算方法将大大 在以下情况下使用长测序读数提高SV检测的灵敏度和精确度： 无论是否有参考基因组。此外，这项工作的成果可能会垂直推进SV 机理研究这项研究具有重要意义，因为它将有助于发现致病性 变异和建立基因型和表型之间的关联。它也可能普及 使用新的测序平台来解决新的科学问题。