Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
Wellcome Centre for Human Genetics, University of Oxford, Oxford OX37BN, United Kingdom.
Mol Biol Evol. 2022 Apr 11;39(4). doi: 10.1093/molbev/msac058.
Research over the past two decades has made substantial inroads into our understanding of somatic mutations. Recently, these studies have focused on understanding their presence in homeostatic tissue. In parallel, agent-based mechanistic models have emerged as an important tool for understanding somatic mutation in tissue; yet no common methodology currently exists to provide base-pair resolution data for these models. Here, we present Gattaca as the first method for introducing and tracking somatic mutations at the base-pair resolution within agent-based models that typically lack nuclei. With nuclei that incorporate human reference genomes, mutational context, and sequence coverage/error information, Gattaca is able to realistically evolve sequence data, facilitating comparisons between in silico cell tissue modeling with experimental human somatic mutation data. This user-friendly method, incorporated into each in silico cell, allows us to fully capture somatic mutation spectra and evolution.
过去二十年的研究在我们对体细胞突变的理解上取得了重大进展。最近,这些研究的重点是了解它们在稳态组织中的存在。与此同时,基于代理的机械模型已成为理解组织中体细胞突变的重要工具;然而,目前还没有通用的方法为这些模型提供碱基分辨率数据。在这里,我们提出了 Gattaca,这是第一个在通常缺乏细胞核的基于代理的模型中以碱基分辨率引入和跟踪体细胞突变的方法。Gattaca 利用包含人类参考基因组、突变上下文和序列覆盖/错误信息的细胞核,能够真实地进化序列数据,促进计算细胞组织模型与实验性人类体细胞突变数据之间的比较。这种用户友好的方法被整合到每个计算细胞中,使我们能够全面捕获体细胞突变谱和进化。