Knowing the activity of the mutational processes shaping a cancer genome may provide insight into tumorigenesis and personalized therapy. It is thus important to characterize the signatures of active mutational processes in patients from their patterns of single base substitutions. However, mutational processes do not act uniformly on the genome, leading to statistical dependencies among neighboring mutations. To account for such dependencies, we develop the first sequence-dependent model, SigMa, for mutation signatures. We apply SigMa to characterize genomic and other factors that influence the activity of mutation signatures in breast cancer. We show that SigMa outperforms previous approaches, revealing novel insights on signature etiology. The source code for SigMa is publicly available at https://github.com/lrgr/sigma.

中文翻译：

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隐藏的马尔可夫模型导致癌症中突变特征活动的高分辨率图。

了解形成癌症基因组的突变过程的活动可能会提供有关肿瘤发生和个性化治疗的见解。因此，重要的是根据患者单碱基取代的模式表征患者体内活性突变过程的特征。但是，突变过程不能均匀地作用于基因组，从而导致相邻突变之间的统计依赖性。为了解决这种依赖性，我们为突变签名开发了第一个序列依赖性模型SigMa。我们应用SigMa来表征基因组和其他因素，这些因素会影响乳腺癌中突变标志的活性。我们显示SigMa优于以前的方法，揭示了对病因的新颖见解。SigMa的源代码可从https://github.com/lrgr/sigma公开获得。