EAGER: Estimating Phylogenetic Trees when Character Evolution is neither Independent nor Identically Distributed

一组物种的进化树或系统发育是解释它们从一个共同祖先进化的历史的树。为了估计这段历史，科学家们对现存的物种进行了观察，并试图找到最符合这一数据的树。目前最常见的观察类型是以生物分子序列的形式进行的，例如DNA或基因或蛋白质的蛋白质序列。这些序列被对齐，以便在给定序列中的相应位置显示尽可能多的相似性。排列的每一列称为一个站点或一个字符。在进化的标准模型中，树中的每个节点都有一个特定的特征状态，并将这种状态传递给它的子节点。然而，状态沿树的每条边都可能发生突变。标准模型还假定所有的特征都按照相同的、独立的随机过程进化。在这些假设下，推断树（以及边缘上的突变概率）所需的字符数量是已知的紧密界限。问题是这些假设在生物学上是不现实的。众所周知，选择压力在不同的位点起着不同的作用，一个性状的进化可以依赖于其他性状。需要更复杂的数学分析来推断这些条件下的树和依赖结构，这正是这个项目的主要目标。重建进化树的问题在生物学中具有中心重要性，因为进化是生物学的理论。计算机科学家对这一领域做出了贡献，但迄今为止，计算机科学家提供的解决方案过于简化了问题，无法在实际数据上产生可靠的解决方案。这个项目旨在迈出重要的一步，使树推理算法更加现实。

The evolutionary tree or phylogeny of a set of species is a tree that explains the history of their evolution from a common ancestor. To estimate this history, scientists make observations about species that are alive today and seek to find a tree that best fits this data. The most common types of observations these days are in the form of biomolecular sequences such as DNA or protein sequences for genes or proteins. These sequences are aligned so that corresponding positions in the given sequences exhibit as much similarity as possible. Each column of the alignment is called a site or a character. In the standard model of evolution each node in the tree has a certain state for the character and transmits this state to its children. However, the state is probabilistically mutated along each edge of the tree. The standard model also assumes that all characters evolve according to identical, independent stochastic processes. Tight bounds are known for the number of characters needed to infer the tree (and mutation probabilities on the edges) under these assumptions. The problem is that these assumptions are not biologically realistic. It is well known that selection pressure operates differently on different sites and that the evolution of one character can be dependent on other characters. Much more sophisticated mathematical analysis is needed to infer the tree and dependence structure under these conditions, and this is precisely the major goal of this project.The problem of reconstructing evolutionary trees is of central importance in biology since evolution is the theory in biology. Computer scientists have made contributions to this field, but the solutions provided by computer scientists so far simplify the problem too much to produce reliable solutions on realistic data. This project aims to take an important step towards making tree inference algorithms more realistic.