An important goal in microbial computational genomics is to identify crucial events in the evolution of a gene that severely alter the duplication, loss, and mobilization patterns of the gene within the genomes in which it disseminates. In this article, we formalize this microbiological goal as a new pattern-matching problem in the domain of gene tree and species tree reconciliation, denoted “Reconciliation-Scenario Altering Mutation (RSAM) Discovery.” We propose an time algorithm to solve this new problem, wheremandnare the number of vertices of the input gene tree and species tree, respectively, andkis a user-specified parameter that bounds from above the number of optimal solutions of interest. The algorithm first constructs a hypergraph representing thekhighest scoring reconciliation scenarios between the given gene tree and species tree, and then interrogates this hypergraph for subtrees matching a prespecified RSAM pattern. Our algorithm is optimal in the sense that the number of hypernodes in the hypergraph can be lower bounded by. We implement the new algorithm as a tool, called RSAM-finder, and demonstrate its application to the identification of RSAMs in toxins and drug resistance elements across a data set spanning hundreds of species.

中文翻译：

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一种识别和解情景改变突变赋予环境适应的新范式

微生物计算基因组学的一个重要目标是确定基因进化中的关键事件，这些事件严重改变了基因在其传播的基因组内的复制、丢失和动员模式。在本文中，我们将这一微生物目标正式化为基因树和物种树协调领域中的新模式匹配问题，表示为“协调-场景改变突变 (RSAM) 发现”。我们提出一个时间算法来解决这个新问题，其中m和n分别是输入基因树和物种树的顶点数，k是用户指定的参数，它从感兴趣的最优解的数量之上开始。该算法首先构建一个表示给定基因树和物种树之间k 个最高得分协调场景的超图，然后查询该超图以查找与预先指定的 RSAM 模式匹配的子树。我们的算法是最优的，因为超图中的超节点数量可以下限为. 我们将新算法实现为一种称为 RSAM-finder 的工具，并展示了其在跨越数百种物种的数据集中识别毒素和耐药性元素中的 RSAM 的应用。