Sneddon Tam P, Church Deanna M
NCBI, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Methods Mol Biol. 2012;838:273-89. doi: 10.1007/978-1-61779-507-7_13.
Genomic structural variation (SV) can be thought of on a continuum from a single base pair insertion/deletion (INDEL) to large megabase-scale rearrangements involving insertions, deletions, duplications, inversions, or translocations of whole chromosomes or chromosome arms. These variants can occur in coding or noncoding DNA, they can be inherited or arise sporadically in the germline or somatic cells. Many of these events are segregating in the population and can be considered common alleles while others are new alleles and thus rare events. All species studied to date harbor structural variants and these may be benign, contributing to phenotypes such as sensory perception and immunity, or pathogenic resulting in genomic disorders including DiGeorge/velocardiofacial, Smith-Margenis, Williams-Beuren, and Prader-Willi syndromes. As structural variants are identified, validated, and their significance, origin, and prevalence are elucidated, it is of critical importance that these data be collected and collated in a way that can be easily accessed and analyzed. This chapter describes current structural variation online resources (see Fig. 1 and Table 1), highlights the challenges in capturing, storing, and displaying SV data, and discusses how dbVar and DGVa, the genomic structural variation databases developed at NCBI and EBI, respectively, were designed to address these issues.
基因组结构变异（SV）可以被看作是一个连续体，从单个碱基对的插入/缺失（INDEL）到涉及整条染色体或染色体臂的插入、缺失、重复、倒位或易位的大型兆碱基规模的重排。这些变异可发生在编码或非编码DNA中，它们可以是遗传性的，也可以在生殖细胞或体细胞中散发性出现。这些事件中的许多在人群中是分离的，可以被认为是常见等位基因，而其他的则是新的等位基因，因此是罕见事件。迄今为止研究的所有物种都存在结构变异，这些变异可能是良性的，有助于形成如感官感知和免疫等表型，或者是致病性的，导致包括DiGeorge/心脏颜面综合征、史密斯-马吉尼斯综合征、威廉姆斯-贝伦综合征和普拉德-威利综合征在内的基因组疾病。随着结构变异被识别、验证，以及它们的意义、起源和流行情况被阐明，以一种易于访问和分析的方式收集和整理这些数据至关重要。本章描述了当前的结构变异在线资源（见图1和表1），强调了在捕获、存储和显示SV数据方面的挑战，并讨论了分别由美国国立医学图书馆（NCBI）和欧洲生物信息研究所（EBI）开发的基因组结构变异数据库dbVar和DGVa是如何设计来解决这些问题的。
