单碱基替代（SBS）突变特征已成为癌症基因组学的标准实践。在没有全新特征提取的情况下，参考特征分配允许用户估计在个体恶性肿瘤中预先确定的SBS特征的活性。已开发了几种针对此目的的工具，每个工具具有不同的方法。然而，由于缺乏标准化，特征分配可能存在工具间的变异性。我们对三种分配策略和五种SBS特征分配工具进行了深入研究。我们观察到分配策略的选择可以显著影响结果和解释。尽管工具提出了不同的建议，但Refit表现最佳，减少了过拟合并最大限度地重建了原始突变谱。即使在统一应用了Refit之后，工具在特征分配上仍然显著变化，无论是在定性（Jaccard指数=0.38-0.83）还是定量（Kendall tau-b = 0.18-0.76）方面。这种现象在“平坦”特征（如同源重组缺陷特征SBS3）中尤为明显。一种合奏方法（EnsembleFit）可以利用所有五个工具的输出，在BRCA1/2缺陷型乳腺癌中提高SBS3分配的准确性。在为数千个全癌肿瘤生成合成突变谱后，使用癌症体细胞突变目录和非标准参考特征集，EnsembleFit平均减少了15.9-24.7%的特征活性分配误差。我们还发布了EnsembleFit网络门户（https://www.ensemblefit.pittlabgenomics.com），供用户使用任何策略和工具组合生成或下载基于合奏的SBS特征分配。总的来说，我们展示了工具和策略之间的特征分配异质性是非可忽略的，并提出了一个可行的合奏解决方案。© 作者2023。由牛津大学出版社出版。

Single-base substitution (SBS) mutational signatures have become standard practice in cancer genomics. In lieu of de novo signature extraction, reference signature assignment allows users to estimate the activities of pre-established SBS signatures within individual malignancies. Several tools have been developed for this purpose, each with differing methodologies. However, due to a lack of standardization, there may be inter-tool variability in signature assignment. We deeply characterized three assignment strategies and five SBS signature assignment tools. We observed that assignment strategy choice can significantly influence results and interpretations. Despite varying recommendations by tools, Refit performed best by reducing overfitting and maximizing reconstruction of the original mutational spectra. Even after uniform application of Refit, tools varied remarkably in signature assignments both qualitatively (Jaccard index = 0.38-0.83) and quantitatively (Kendall tau-b = 0.18-0.76). This phenomenon was exacerbated for 'flat' signatures such as the homologous recombination deficiency signature SBS3. An ensemble approach (EnsembleFit), which leverages output from all five tools, increased SBS3 assignment accuracy in BRCA1/2-deficient breast carcinomas. After generating synthetic mutational profiles for thousands of pan-cancer tumors, EnsembleFit reduced signature activity assignment error 15.9-24.7% on average using Catalogue of Somatic Mutations In Cancer and non-standard reference signature sets. We have also released the EnsembleFit web portal (https://www.ensemblefit.pittlabgenomics.com) for users to generate or download ensemble-based SBS signature assignments using any strategy and combination of tools. Overall, we show that signature assignment heterogeneity across tools and strategies is non-negligible and propose a viable, ensemble solution.© The Author(s) 2023. Published by Oxford University Press.