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.

结构变异（SV）分析非常重要，因为它们是遗传变异的主要来源，并且 解释了许多物种的广泛表型。为了更好地了解他们对多样性的贡献， 由于存在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 表征并了解其形成机制。没有准确的 SV 结果，我们可能会错过导致表型的 SV 事件。在不了解它们的形成的情况下 机制，我们可能无法将表型相关的 SV 与其他 SV 区分开来。作为测序 随着技术的发展，出现了许多新的测序平台，例如 PacBio、Oxford Nanopore 和 10X Genomics 更长的测序读数出现并表现出巨大的潜力。然而，计算 SV 分析算法对于有或没有参考基因组和 SV 的生物体来说都是不够的 机制分析仅基于短（主要<10bp）断点连接序列，因为 技术限制。随着越来越多的此类数据产生，迫切需要通过以下方式填补空白： 开发更准确、更高效的 SV 发现算法，并建立创新方法 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。的目标 未来三年将开发有效的算法来对有或没有基因的生物体进行 SV 分析 参考。我们将重点关注大型插入、倒置和复杂的 SV，这些都是代表性不足的。 对于有参考的生物体，我们将开发一种从头组装评估方法来优化现有的 工具和/或开发新的组装方法。有了这些工具包，接下来两年的目标是 基于全局基因组结构研究SV形成机制。我们的中心假设是 可能是 SV 基因座周围的一些热点、特征，遗传自父本或母本基因组 导致重排形成的易感性。我们将通过调查全球范围来检验这一假设 和使用新测序平台的 SV 的单倍型图片。预计该研究将 贡献一套针对长读长测序数据的稳健方法，以识别所有形式的具有高 灵敏度和精确度。此外，预计这项工作将为 SV 的形成提供新的见解 机制。所提出的工作具有创新性，因为所提出的计算方法将极大地 提高长测序reads情况下SV检测的灵敏度和精度 有或没有参考基因组。此外，这项工作的成果可能会垂直推进 SV 机制研究。拟议的研究意义重大，因为它将有助于发现致病菌 变异以及基因型和表型之间关联的建立。也可能会普及 使用新的测序平台来解决新的科学问题。