Computational Approaches for Structural Variation Studies in Genomes

描述（由申请人提供）：结构变异，包括DNA序列大块的重复、插入、缺失、倒位和易位，已显示与各种人类疾病相关。这些变异也经常作为癌症中的体细胞改变而发生。识别和表征基因组序列中的结构变体是一项具有挑战性的任务。我们建议开发计算方法，使全面的研究正常和患病基因组的结构变异。在目标1中，我们开发了一个通用的计算框架，用于使用新的几何和概率方法对多个样本和测量平台的结构变体进行分类和比较。在目标2中，我们设计算法，以最大限度地提高新兴的单分子测序技术的有效性，用于检测和组装复杂的结构变异和重排的转录本。在目标3中，我们开发了重建癌症基因组组织的算法，并研究了结构变异如何在体细胞进化过程中改变基因组组织。最后，在目标4中，我们研究了人类基因组中倒位多态性的群体遗传学，包括它们对单倍型块结构的影响以及选择下的倒位是否留下了独特的遗传标记。我们将与几位生物医学研究人员合作，将这些方法应用于人类，癌症，小鼠和病原体基因组的数据。成功完成拟议的研究将有助于未来研究结构变异在人类和癌症遗传学中的作用。 公共卫生相关性：识别与疾病相关的遗传差异和导致癌症的后天突变是基因组学的主要挑战。其中一类重要的突变是结构变异，包括DNA序列大片段的复制、插入、缺失、倒位和易位。这些变异与包括自闭症和癌症在内的几种疾病有关。新的基因组技术使这些变异的大规模测量成为可能，但需要新的计算方法来最大限度地提高这些测量的信息。我们将开发一些算法，以促进结构变体的识别和表征。这些方法将有助于发现遗传变异，从而为各种疾病提供更好的诊断和/或个性化治疗。

DESCRIPTION (provided by applicant): Structural variants, including duplications, insertions, deletions, inversions, and translocations of large blocks of DNA sequence, have been shown to be associated with various human diseases. These variants also frequently occur as somatic alterations in cancer. Identifying and characterizing structural variants in a genome sequence is a challenging task. We propose to develop computational methods to enable comprehensive studies of structural variation in normal and diseased genomes. In Aim 1 we develop a general computational framework for classification and comparison of structural variants across multiple samples and measurement platforms using a novel geometric and probabilistic approach. In Aim 2 we design algorithms to maximize the effectiveness of emerging single-molecule sequencing technologies for detecting and assembling complex structural variants and rearranged transcripts. In Aim 3 we develop algorithms to reconstruct the organization of cancer genomes and investigate how structural variants alter genome organization during somatic evolution. Finally, in Aim 4, we study the population genetics of inversion polymorphisms in the human genome, including their effects on haplotype block structure and whether inversions under selection leave distinctive genetic signatures. We will apply these approaches to data from human, cancer, mouse, and pathogen genomes in collaboration with several biomedical researchers. Successful completion of the proposed studies will facilitate future research of the role of structural variation in human and cancer genetics. PUBLIC HEALTH RELEVANCE: Identifying the inherited genetic differences associated with disease and the acquired mutations that lead to cancer are major challenges in genomics. One important class of such mutations are structural variants, which include duplications, insertions, deletions, inversions, and translocations of large blocks of DNA sequence. These variants have been implicated in several diseases including autism and cancer. New genome technologies are enabling large-scale measurement of these variants, but demand novel computational methods to maximize the information from these measurements. We will develop a number of algorithms to facilitate the identification and characterization of structural variants. These approaches will aid in the discovery of genetic variants that will provide better diagnostics and/or personalized treatments for various diseases.