Background: Many forms of variation 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 genome structure. Thus, the performance assessment of variation detection tools using next-generation sequencing platforms is urgently needed.
Methods and results: 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 variation. Three sets of variations embedded in genomic sequences in different periods are dynamically and sequentially simulated one by one. 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.