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: 10029410
• 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/10029410
• 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.

结构变化（SVS）分析非常重要，因为它们是遗传变异的主要来源，并且 在许多物种中占多种表型。为了更好地了解他们对多样性的贡献， 分歧和各种表型特征，我们应该解决SV分析的两个关键问题： 准确的SV表征和理解其形成机制。没有准确的SV 结果，我们可能会错过解释表型的SV事件。不理解他们的形成 机制，我们可能不会将相关SV与其他SV区分开。作为测序 技术发展，许多新的测序平台，例如PACBIO，牛津纳米孔和10倍基因组学 较长的测序读数出现，并且表现出巨大的潜力。但是，计算 有或没有参考基因组和SV的生物，用于SV分析的算法不足 机理分析仅基于由于短（<10bp）的重点连接序列 技术限制。随着更多此类数据的生成，迫切需要通过 为SV发现开发更准确，更有效的算法，并建立一种创新的方式 研究SV形成机制。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。目标的目标 接下来的三年将开发有效的有和没有A的生物的SV分析的有效算法 参考。我们将专注于始终不足的大型插入，反转和复杂的SV。 对于具有参考的生物，我们将开发一种从头组装评估方法来优化现有 工具和/或开发新的装配方法。鉴于这些工具包，接下来两年的目标是 研究基于全球基因组结构的SV形成机制。我们的中心假设是 可能是一些热点，在父亲或母体基因组继承的SV基因座周围的签名 引起重排的形成敏感性。我们将基于研究全球的假设来检验该假设 以及使用新的测序平台的SVS的单倍型图片。期望研究将 在长阅读测序数据上贡献一套可靠的方法，以识别所有形式的SVS 灵敏度和精度。此外，预计这项工作将为SV形成提供新颖的见解 机制。拟议的工作具有创新性，因为拟议的计算方法将极大地 在使用长测序读取的情况下，提高SV检测的灵敏度和精度 有和没有参考基因组。此外，这项工作的结果可能会垂直提高SV 机理研究。拟议的研究很重要，因为它将促进致病性的发现 基因型和表型之间的变化以及建立关联。它也可能普及 使用新的测序平台来解决新的科学问题。