Motivation Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. To evaluate the mutational signatures operative in a cancer genome, one first needs to quantify their activities by estimating the number of mutations imprinted by each signature. Results Here we present SigProfilerAssignment, a desktop and an online computational framework for assigning all types of mutational signatures to individual samples. SigProfilerAssignment is the first tool that allows both analysis of copy-number signatures and probabilistic assignment of signatures to individual somatic mutations. As its computational engine, the tool uses a custom implementation of the forward stagewise algorithm for sparse regression and nonnegative least squares for numerical optimization. Analysis of 2,700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures. Availability SigProfilerAssignment is available under the BSD 2-clause license at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

---

使用 SigProfilerAssignment 将突变特征分配给单个样本和单个体细胞突变

突变特征的动机分析是了解塑造癌症基因组进化的突变过程的有效方法。为了评估癌症基因组中起作用的突变特征，首先需要通过估计每个特征所印记的突变数量来量化其活性。结果在这里，我们展示了 SigProfilerAssignment，一个桌面和在线计算框架，用于将所有类型的突变特征分配给单个样本。 SigProfilerAssignment 是第一个既可以分析拷贝数特征，又可以将特征概率分配给个体体细胞突变的工具。作为其计算引擎，该工具使用前向阶段算法的自定义实现进行稀疏回归，并使用非负最小二乘法进行数值优化。对 2,700 个带噪声和不带噪声的合成癌症基因组的分析表明，SigProfilerAssignment 优于四种常用的分配突变特征的方法。可用性 SigProfilerAssignment 可在 https://github.com/AlexandrovLab/SigProfilerAssignment 的 BSD 2 条款许可下使用，并可通过 https://cancer.sanger.ac.uk/signatures/assignment/ 进行 Web 实现。补充信息 补充数据可在生物信息学在线获取。