Hide and Seek：为数千个同源性丰富的序列放置和寻找最佳树
Hide and seek: placing and finding an optimal tree for thousands of homoplasy-rich sequences
摘要：
从序列数据中找到最优进化树通常是一个棘手的问题，并且通常没有办法知道从某些搜索中得到的最优树到底有多接近最优。当我们有许多分类群，并且数据具有高度的同质性时，这个问题似乎特别严重，其中单个字符需要许多变化才能适应最好的树。然而，最近的一个数学结果提供了一个精确的工具，以产生一个短期的高同质性字符的任何给定的树，使这棵树是可证明的最佳树下的最大简约准则。这是第一次提供了一种严格的方法来测试树搜索算法对同质性丰富的数据，在那里我们提前知道什么是“最好的”树。在这个简短的说明中，我们只考虑一个搜索程序（TNT），但表明它能够正确定位32，768个分类群的全局最优树，即使数据集中的字符平均需要1148个状态变化才能适合这个树，字符数只有57个。
英文摘要：
Finding optimal evolutionary trees from sequence data is typically an intractable problem, and there is usually no way of knowing how close to optimal the best tree from some search truly is. The problem would seem to be particularly acute when we have many taxa and when that data has high levels of homoplasy, in which the individual characters require many changes to fit on the best tree. However, a recent mathematical result has provided a precise tool to generate a short number of high-homoplasy characters for any given tree, so that this tree is provably the optimal tree under the maximum parsimony criterion. This provides, for the first time, a rigorous way to test tree search algorithms on homoplasy-rich data, where we know in advance what the `best' tree is. In this short note we consider just one search program (TNT) but show that it is able to locate the globally optimal tree correctly for 32,768 taxa, even though the characters in the dataset requires, on average, 1148 state-changes each to fit on this tree, and the number of characters is only 57.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
