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

EAGER：当性状进化既不独立也不同分布时估计系统发育树

• 批准号: 1137084
• 负责人: Sampath Kannan
• 金额: $ 30万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1137084&HistoricalAwards=false
• 关键词: EAGER; Estimating; Phylogenetic; Trees; when

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.

一组物种的进化树或进化树是解释它们从共同祖先进化的历史的树。为了估计这段历史，科学家们对今天存活的物种进行了观察，并试图找到一棵最适合这些数据的树。目前最常见的观测类型是生物分子序列，如基因或蛋白质的DNA或蛋白质序列。比对这些序列，使得给定序列中的相应位置表现出尽可能多的相似性。路线的每一列称为一个地点或一个字符。在标准的进化模型中，树中的每个节点都有一个特定的角色状态，并将这个状态传递给它的孩子。然而，状态是概率性地沿着树的每个边沿着突变的。标准模型还假设所有角色都是按照相同的、独立的随机过程进化的。 在这些假设下，推断树所需的字符数（以及边缘上的突变概率）的严格界限是已知的。问题是，这些假设在生物学上并不现实。众所周知，选择压力在不同地点的作用不同，一个性状的进化可能依赖于其他性状。在这些条件下，需要更复杂的数学分析来推断树和依赖结构，而这正是该项目的主要目标。重建进化树的问题在生物学中至关重要，因为进化是生物学的理论。计算机科学家在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，无法在现实数据上产生可靠的解决方案。这个项目的目标是朝着使树推理算法更现实的方向迈出重要的一步。