Department of Software Engineering, Ningxia University, Yinchuan 750021, China.
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China.
Bioinformatics. 2020 Feb 15;36(4):1281-1282. doi: 10.1093/bioinformatics/btz713.
Allele dropout (ADO) and unbalanced amplification of alleles are main technical issues of single-cell sequencing (SCS), and effectively emulating these issues is necessary for reliably benchmarking SCS-based bioinformatics tools. Unfortunately, currently available sequencing simulators are free of whole-genome amplification involved in SCS technique and therefore not suited for generating SCS datasets. We develop a new software package (SCSsim) that can efficiently simulate SCS datasets in a parallel fashion with minimal user intervention. SCSsim first constructs the genome sequence of single cell by mimicking a complement of genomic variations under user-controlled manner, and then amplifies the genome according to MALBAC technique and finally yields sequencing reads from the amplified products based on inferred sequencing profiles. Comprehensive evaluation in simulating different ADO rates, variation detection efficiency and genome coverage demonstrates that SCSsim is a very useful tool in mimicking single-cell sequencing data with high efficiency.
SCSsim is freely available at https://github.com/qasimyu/scssim.
Supplementary data are available at Bioinformatics online.
等位基因丢失 (ADO) 和等位基因不平衡扩增是单细胞测序 (SCS) 的主要技术问题，有效地模拟这些问题对于可靠地对基于 SCS 的生物信息学工具进行基准测试是必要的。不幸的是，目前可用的测序模拟器不涉及 SCS 技术中的全基因组扩增，因此不适合生成 SCS 数据集。我们开发了一个新的软件包 (SCSsim)，可以以最小的用户干预以并行方式有效地模拟 SCS 数据集。SCSsim 首先通过模拟用户控制下的基因组变异来构建单细胞的基因组序列，然后根据 MALBAC 技术对基因组进行扩增，最后根据推断的测序谱从扩增产物中生成测序reads。在模拟不同的 ADO 率、变异检测效率和基因组覆盖度方面的综合评估表明，SCSsim 是一种非常有用的工具，可高效模拟单细胞测序数据。
SCSsim 可在 https://github.com/qasimyu/scssim 上免费获得。
补充数据可在 Bioinformatics 在线获得。
