Mutational signature analysis is a recent computational approach for interpreting somatic mutations in the genome. Its application to cancer data has enhanced our understanding of mutational forces driving tumorigenesis and demonstrated its potential to inform prognosis and treatment decisions. However, methodological challenges remain for discovering new signatures and assigning proper weights to existing signatures, thereby hindering broader clinical applications. Here we present Mutational Signature Calculator (MuSiCal), a rigorous analytical framework with algorithms that solve major problems in the standard workflow. Our simulation studies demonstrate that MuSiCal outperforms state-of-the-art algorithms for both signature discovery and assignment. By reanalyzing more than 2,700 cancer genomes, we provide an improved catalog of signatures and their assignments, discover nine indel signatures absent in the current catalog, resolve long-standing issues with the ambiguous ‘flat’ signatures and give insights into signatures with unknown etiologies. We expect MuSiCal and the improved catalog to be a step towards establishing best practices for mutational signature analysis.

突变特征分析是解释基因组体细胞突变的最新计算方法。其在癌症数据中的应用增强了我们对驱动肿瘤发生的突变力量的理解，并证明了其为预后和治疗决策提供信息的潜力。然而，发现新特征并为现有特征分配适当的权重仍然存在方法学挑战，从而阻碍了更广泛的临床应用。在这里，我们介绍突变特征计算器 (MuSiCal)，这是一个严格的分析框架，其算法可解决标准工作流程中的主要问题。我们的模拟研究表明，MuSiCal 在签名发现和分配方面均优于最先进的算法。通过重新分析 2,700 多个癌症基因组，我们提供了改进的签名及其分配目录，发现了当前目录中缺少的 9 个插入缺失签名，解决了模糊“平坦”签名的长期存在问题，并深入了解了病因不明的签名。我们期望 MuSiCal 和改进的目录成为建立突变特征分析最佳实践的一步。