Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Quantitative Computational Biology, Baylor College of Medicine, Houston, TX, USA.
Methods Mol Biol. 2022;2493:21-27. doi: 10.1007/978-1-0716-2293-3_2.
Accurate detection of somatic mutations in genetically heterogeneous tumor cell populations using next-generation sequencing remains challenging. We have developed MuSE, Mutation calling using a Markov Substitution model for Evolution, a novel approach for modeling the evolution of the allelic composition of tumor and normal tissue at each reference base. It adopts a sample-specific error model to depict inter-tumor heterogeneity, which greatly improves the overall accuracy. Here, we describe the method and provide a tutorial on the installation and application of MuSE.
使用下一代测序技术准确检测遗传异质性肿瘤细胞群体中的体细胞突变仍然具有挑战性。我们开发了 MuSE，即使用进化的马尔可夫替换模型进行突变调用，这是一种用于模拟肿瘤和正常组织在每个参考碱基等位组成进化的新方法。它采用特定于样本的误差模型来描述肿瘤间异质性，从而大大提高了整体准确性。在这里，我们描述了该方法，并提供了 MuSE 的安装和应用教程。
