Centre for Research in Mathematics, Western Sydney University, Sydney, Australia.
School of Public Health, Imperial College, London, UK.
Bull Math Biol. 2018 Dec;80(12):3227-3246. doi: 10.1007/s11538-018-0514-3. Epub 2018 Oct 4.
Modellers of large-scale genome rearrangement events, in which segments of DNA are inverted, moved, swapped, or even inserted or deleted, have found a natural syntax in the language of permutations. Despite this, there has been a wide range of modelling choices, assumptions and interpretations that make navigating the literature a significant challenge. Indeed, even authors of papers that use permutations to model genome rearrangement can struggle to interpret each others' work, because of subtle differences in basic assumptions that are often deeply ingrained (and consequently sometimes not even mentioned). In this paper, we describe the different ways in which permutations have been used to model genomes and genome rearrangement events, presenting some features and limitations of each approach, and show how the various models are related. This paper will help researchers navigate the landscape of permutation-based genome rearrangement models and make it easier for authors to present clear and consistent models.
大规模基因组重排事件（如 DNA 片段倒位、移动、交换，甚至插入或缺失）的建模者在排列组合的语言中发现了一种自然的语法。尽管如此，建模者在选择、假设和解释方面仍存在广泛的差异，这使得文献的查阅成为一个重大的挑战。事实上，即使是使用排列组合来模拟基因组重排的论文作者，也可能难以理解彼此的工作，因为基本假设的细微差异往往根深蒂固（因此有时甚至没有提及）。在本文中，我们描述了排列组合在模拟基因组和基因组重排事件中的不同应用方式，展示了每种方法的一些特点和局限性，并展示了各种模型之间的关系。本文将帮助研究人员了解基于排列组合的基因组重排模型，并使作者更容易呈现清晰一致的模型。
