Sindi Suzanne, Helman Elena, Bashir Ali, Raphael Benjamin J
Division of Applied Mathematics, Center for Computational Molecular Biology, Brown University, Providence, RI, USA.
Bioinformatics. 2009 Jun 15;25(12):i222-30. doi: 10.1093/bioinformatics/btp208.
Structural variants, including duplications, insertions, deletions and inversions of large blocks of DNA sequence, are an important contributor to human genome variation. Measuring structural variants in a genome sequence is typically more challenging than measuring single nucleotide changes. Current approaches for structural variant identification, including paired-end DNA sequencing/mapping and array comparative genomic hybridization (aCGH), do not identify the boundaries of variants precisely. Consequently, most reported human structural variants are poorly defined and not readily compared across different studies and measurement techniques.
We introduce Geometric Analysis of Structural Variants (GASV), a geometric approach for identification, classification and comparison of structural variants. This approach represents the uncertainty in measurement of a structural variant as a polygon in the plane, and identifies measurements supporting the same variant by computing intersections of polygons. We derive a computational geometry algorithm to efficiently identify all such intersections. We apply GASV to sequencing data from nine individual human genomes and several cancer genomes. We obtain better localization of the boundaries of structural variants, distinguish genetic from putative somatic structural variants in cancer genomes, and integrate aCGH and paired-end sequencing measurements of structural variants. This work presents the first general framework for comparing structural variants across multiple samples and measurement techniques, and will be useful for studies of both genetic structural variants and somatic rearrangements in cancer.
结构变异，包括DNA序列大片段的重复、插入、缺失和倒位，是人类基因组变异的重要因素。在基因组序列中测量结构变异通常比测量单核苷酸变化更具挑战性。目前用于识别结构变异的方法，包括双末端DNA测序/映射和阵列比较基因组杂交（aCGH），不能精确识别变异的边界。因此，大多数已报道的人类结构变异定义不明确，难以在不同研究和测量技术之间进行比较。
我们引入了结构变异的几何分析（GASV），这是一种用于识别、分类和比较结构变异的几何方法。该方法将结构变异测量中的不确定性表示为平面中的多边形，并通过计算多边形的交点来识别支持相同变异的测量。我们推导了一种计算几何算法来高效识别所有此类交点。我们将GASV应用于来自九个个体人类基因组和几个癌症基因组的测序数据。我们获得了结构变异边界的更好定位，区分了癌症基因组中遗传的与假定的体细胞结构变异，并整合了aCGH和双末端测序对结构变异的测量。这项工作提出了第一个用于跨多个样本和测量技术比较结构变异的通用框架，将对癌症中遗传结构变异和体细胞重排的研究有用。
