Structural variation analysis with and without a reference genome

结构变异(SVS)分析非常重要，因为它们是遗传变异的主要来源 在许多物种中解释了广泛的表型。为了更好地了解它们对多样性的贡献， 分歧和各种表型性状，我们应该解决两个关键问题进行SV分析： 准确的SV表征和了解其形成机制。没有精确的SV 结果，我们可能会错过解释表型的SV事件。在不了解其形成的情况下 机制，我们可能无法将表型相关的SVS与其他SVS区分开来。因为测序 随着技术的发展，许多新的测序平台，如PacBio、牛津纳米孔和10X基因组学 随着更长的测序，读数出现了，并展示了巨大的潜力。然而，计算性的 对于有或没有参考基因组和SV的生物体来说，SV分析的算法都是不合适的 机制分析仅基于短的(主要为10个碱基)断点连接序列 技术限制。随着更多此类数据的产生，迫切需要通过以下方式来填补这一空白 开发更准确、更高效的SV发现算法，并建立一种创新的方式 研究SV的形成机制。该实验室的长期目标是全面确定 所有形式的SVS，并了解其功能后果和形成机制。世界银行的目标是 未来三年将开发有效的算法，对有无病毒的生物体进行SV分析 参考资料。我们将专注于大插入、倒置和复杂的SVS，它们总是表示得很少。 对于有参考价值的生物，我们将开发一种从头开始的组装评估方法，以优化现有的 工具和/或开发新的组装方法。考虑到这些工具包，接下来两年的目标是 基于全球基因组结构研究SV的形成机制。我们的中心假设是 可能是一些热点，SV基因座周围的特征要么来自父系基因组，要么来自母系基因组 导致重排形成的敏感性。我们将基于对全球 并使用新的测序平台绘制了SVS的单倍型图像。预计这项研究将 在长时间的测序数据上贡献一套稳健的方法来识别所有形式的高SVS 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 机械装置。建议的工作是创新的，因为建议的计算方法将大大 在以下情况下使用长序列读数提高SV检测的灵敏度和精确度 无论有没有参考基因组。此外，这项工作的结果可能垂直推进SV 机理研究。这项拟议的研究具有重要意义，因为它将有助于发现致病原 变异和建立基因和表型之间的关联。它还可以普及 使用新的测序平台来解决新的科学问题。

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.