Background: Many forms of variations exist in the genome, which are the main causes of individual phenotypic differences. The detection of variants, especially those located in the tumor genome, still faces many challenges due to the complexity of the genome structure. Thus, the performance assessment of variation detection tools using next-generation sequencing platforms is urgently needed.

Method: We have created a software package called the Multi-Variation Simulator of Cancer genomes (MVSC) to simulate common genomic variants, including single nucleotide polymorphisms, small insertion and deletion polymorphisms, and structural variations (SVs), which are analogous to human somatically acquired variations. Three sets of variations embedded in genomic sequences in different periods were dynamically and sequentially simulated one by one.

Results: In cancer genome simulation, complex SVs are important because this type of variation is characteristic of the tumor genome structure. Overlapping variations of different sizes can also coexist in the same genome regions, adding to the complexity of cancer genome architecture. Our results show that MVSC can efficiently simulate a variety of genomic variants that cannot be simulated by existing software packages.

Conclusion: The MVSC-simulated variants can be used to assess the performance of existing tools designed to detect SVs in next-generation sequencing data, and we also find that MVSC is memory and time-efficient compared with similar software packages.

背景：基因组中存在多种形式的变异，这是个体表型差异的主要原因。由于基因组结构的复杂性，变异的检测，尤其是位于肿瘤基因组中的变异，仍面临许多挑战。因此，迫切需要使用下一代测序平台的变异检测工具的性能评估。

方法：我们已经创建了一个称为癌症基因组多变量模拟器（MVSC）的软件包，用于模拟常见的基因组变异，包括单核苷酸多态性，小的插入和缺失多态性以及结构变异（SV），它们在人体上与人类相似获得的变化。动态和顺序地逐一模拟了嵌入在不同时期的基因组序列中的三组变异。

结果：在癌症基因组模拟中，复杂的SV非常重要，因为这种类型的变异是肿瘤基因组结构的特征。不同大小的重叠变异也可以共存于同一基因组区域，从而增加了癌症基因组架构的复杂性。我们的结果表明，MVSC可以有效地模拟现有软件包无法模拟的各种基因组变异。

结论：MVSC模拟的变体可用于评估旨在检测下一代测序数据中SV的现有工具的性能，并且我们还发现MVSC与类似软件包相比具有更高的存储效率和时间效率。