Performing a statistical test requires a null hypothesis. In cancer genomics, a key challenge is the fast generation of accurate somatic mutational landscapes that can be used as a realistic null hypothesis for making biological discoveries. Here we present SigProfilerSimulator, a powerful tool that is capable of simulating the mutational landscapes of thousands of cancer genomes at different resolutions within seconds. Applying SigProfilerSimulator to 2144 whole-genome sequenced cancers reveals: (i) that most doublet base substitutions are not due to two adjacent single base substitutions but likely occur as single genomic events; (ii) that an extended sequencing context of ± 2 bp is required to more completely capture the patterns of substitution mutational signatures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinformatics tools for detecting driver genes. SigProfilerSimulator’s breadth of features allows one to construct a tailored null hypothesis and use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analysis for biological discoveries. SigProfilerSimulator is freely available at https://github.com/AlexandrovLab/SigProfilerSimulator with an extensive documentation at https://osf.io/usxjz/wiki/home/ .

中文翻译：

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使用SigProfilerSimulator生成癌症突变景观的现实零假设

进行统计检验需要无效假设。在癌症基因组学中，一个关键的挑战是快速生成准确的体细胞突变态势，可将其用作进行生物学发现的现实无效假设。在这里，我们介绍SigProfilerSimulator，这是一个功能强大的工具，能够在几秒钟内以不同的分辨率模拟成千上万个癌症基因组的突变态势。将SigProfilerSimulator用于2144个全基因组测序癌症表明：（i）大多数双峰碱基取代不是由于两个相邻的单碱基取代，而是可能作为单个基因组事件发生；（ii）需要更广泛的±2 bp的测序环境，以更完整地捕获人类癌症中替代突变特征的模式；（iii）有关检测驱动基因的常用生物信息学工具的假阳性发现率的信息。SigProfilerSimulator的功能广度允许人们构建量身定制的原假设，并将其用于评估其他生物信息学工具的准确性或用于生物学发现的下游统计分析。SigProfilerSimulator可从https://github.com/AlexandrovLab/SigProfilerSimulator免费获得，其大量文档位于https://osf.io/usxjz/wiki/home/。