基因组高通量测序数据结构变异识别算法

Structural Variation Detection Algorithm in High-throughput Sequencing Data

遗传变异是生命的基本特征,遗传变异与表型差异之间的关系,是现代生物学的一个基本问题。由基因决定生物体的遗传特征和主要个体差异的观念正在逐渐改变,过去几年的许多研究显示,基因组中大尺度的结构变异与个体的表型差异和疾病等有一定的关联。有关遗传变异和表型多样性的研究,需要比较生物体个体基因组间的各种不同。利用NGS数据全面分析结构变异的技术目前仍然不成熟。因此本文根据生物学知识,利用高通量测序数据,对植物基因组结构变异的识别问题深入系统的研究,提出新的结构变异识别方法和精确的断点预测方法。

Structural variations are genomic rearrangements that contribute significantly to evolution,natural variation between organisms,and are often involved in biological phenotypes and genetic disorders.Traditional microscope and array based methods are used for the detection of larger events or copy number variations.Next generation sequencing has enabled the detection of all types of structural variants from genome accurately.In practice,a significant challenge lies in the development of computational methods that are able to identify these structural variants based on the generated high-throughput sequencing data.In this project,the paper focuses on the design and implementation of the algorithms for multiple donor plant genomic structural variation identification with the combination of assembly,pair end mapping,split read and depth of coverage analyzing.