Wang Zihua, Andrews Peter, Kendall Jude, Ma Beicong, Hakker Inessa, Rodgers Linda, Ronemus Michael, Wigler Michael, Levy Dan
Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA.
Genome Res. 2016 Jun;26(6):844-51. doi: 10.1101/gr.201491.115. Epub 2016 Apr 14.
Copy number variants (CNVs) underlie a significant amount of genetic diversity and disease. CNVs can be detected by a number of means, including chromosomal microarray analysis (CMA) and whole-genome sequencing (WGS), but these approaches suffer from either limited resolution (CMA) or are highly expensive for routine screening (both CMA and WGS). As an alternative, we have developed a next-generation sequencing-based method for CNV analysis termed SMASH, for short multiply aggregated sequence homologies. SMASH utilizes random fragmentation of input genomic DNA to create chimeric sequence reads, from which multiple mappable tags can be parsed using maximal almost-unique matches (MAMs). The SMASH tags are then binned and segmented, generating a profile of genomic copy number at the desired resolution. Because fewer reads are necessary relative to WGS to give accurate CNV data, SMASH libraries can be highly multiplexed, allowing large numbers of individuals to be analyzed at low cost. Increased genomic resolution can be achieved by sequencing to higher depth.
拷贝数变异（CNV）构成了大量的遗传多样性和疾病的基础。CNV可以通过多种方法检测，包括染色体微阵列分析（CMA）和全基因组测序（WGS），但这些方法要么分辨率有限（CMA），要么对于常规筛查来说成本过高（CMA和WGS都是）。作为一种替代方法，我们开发了一种基于下一代测序的CNV分析方法，简称为SMASH，即短多重聚集序列同源性分析。SMASH利用输入基因组DNA的随机片段化来创建嵌合序列读数，从中可以使用最大几乎唯一匹配（MAM）解析多个可映射标签。然后对SMASH标签进行分类和分段，以所需分辨率生成基因组拷贝数图谱。由于相对于WGS而言，获得准确的CNV数据所需的读数较少，因此SMASH文库可以进行高度多重化，从而能够以低成本分析大量个体。通过进行更高深度的测序可以提高基因组分辨率。
