Computational Approaches for Structural Variation Studies in Genomes

描述（由申请人提供）：结构变异，包括大量DNA序列的重复、插入、缺失、倒位和易位，已被证明与各种人类疾病相关。这些变异也经常出现在癌症的体细胞改变中。在基因组序列中识别和表征结构变异是一项具有挑战性的任务。我们建议开发计算方法，以便对正常和患病基因组的结构变异进行全面研究。在目标1中，我们开发了一个通用的计算框架，用于跨多个样本和测量平台的结构变量的分类和比较，使用了一种新的几何和概率方法。在目标2中，我们设计了算法，以最大限度地提高新兴单分子测序技术在检测和组装复杂结构变异和重排转录本方面的有效性。在Aim 3中，我们开发了重建癌症基因组组织的算法，并研究了结构变异在体细胞进化过程中如何改变基因组组织。最后，在Aim 4中，我们研究了人类基因组中反转多态性的群体遗传学，包括它们对单倍型块结构的影响，以及选择下的反转是否会留下独特的遗传特征。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。这些研究的成功完成将有助于进一步研究结构变异在人类和癌症遗传学中的作用。

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.