AitF: Full: Collaborative Research: Graph-theoretic algorithms to improve phylogenomic analyses

了解地球生命的历史？物种是如何从共同祖先进化而来的是生物学研究的主要目标。这些进化树很难以高精度构建，因为几乎所有最精确的方法都需要解决计算困难的优化问题。此外，研究表明，单个基因的进化树可能与物种的进化树不同，目前的方法不能提供足够的基因组规模数据的准确性。因此，大型进化树，覆盖大部分？生命之树？很难高精度地计算。该项目将开发能够实现高度准确的树种估计的方法。关键的方法是开发新的分而治之的策略，即数据集被划分为重叠的子集，物种树构建在子集上，然后子集物种树合并在一起成为一个完整的数据集上的树。这些方法将与强大的统计估计方法相结合，以潜在地改变进化生物学家分析数据的能力。该项目还将为开发的新方法提供开放源码软件，并在国家会议上向生物学家提供使用该软件的培训。该项目还将为两名博士生（一名在伊利诺伊州，一名在伯克利）提供跨学科培训，并将在线提供计算生物学课程材料。理解进化，以及它如何作用于物种和基因，是生物数据分析的一个主要部分。统计估计方法通常提供最佳的准确性，但无法扩展到现代生物学所需的数据集大小。此外，物种树估计受到整个基因组进化树的异质性的挑战，并且目前没有方法能够为基因组规模的数据提供高度准确的物种树。这些挑战使得必须开发新的方法，以便在这些复杂的进化场景下进行高精度的大规模进化树估计。 该项目将开发新的算法策略，以解决三个关键问题：超树估计，在基因树异质性的存在下的物种树估计，以及将统计方法扩展到大型数据集。除了开发图论算法外，项目团队还将在基因和序列进化的随机模型下，使用弦图理论和概率分析为这些方法建立数学保证。

Understanding the history of life on earth ? how species evolved from their common ancestor ? is a major goal of biological research. These evolutionary trees are very hard to construct with high accuracy, because nearly all of the most accurate approaches require the solution to computationally hard optimization problems. Furthermore, research has shown that the evolutionary tree for a single gene can be different from the evolutionary tree for the species, and current methods do not provide adequate accuracy on genome-scale data. As a result, large evolutionary trees, covering big portions of ?The Tree of Life?, are very difficult to compute with high accuracy. This project will develop methods that can enable highly accurate species tree estimation. The key approach is the development of novel divide-and-conquer strategies, whereby a dataset is divided into overlapping subsets, species trees are constructed on the subsets, and then the subset species trees are merged together into a tree on the full dataset. These approaches will be combined with powerful statistical estimation methods, to potentially transform the capability of evolutionary biologists to analyze their data. This project will also provide open source software for the new methods that are developed, and provide training in the use of the software to biologists at national meetings. The project will also contribute to interdisciplinary training for two doctoral students, one at Illinois and one at Berkeley, and course materials for computational biology will be made available online. Understanding evolution, and how it has operated on species and on genes, is a major part of biological data analysis. Statistical estimation approaches often provide the best accuracy, but cannot scale to dataset sizes that are required for modern biology. In addition, species tree estimation is challenged by the heterogeneity of evolutionary trees across the genome, and no current methods are able to provide highly accurate species trees for genome-scale data. These challenges make it essential that new methods be developed in order to make highly accurate large-scale evolutionary tree estimation possible under these complex evolutionary scenarios. This project will develop novel algorithmic strategies to address three key problems: supertree estimation, species tree estimation in the presence of gene tree heterogeneity, and scaling statistical methods to large datasets. In addition to developing graph-theoretic algorithms, the project team will establish mathematical guarantees for these methods using chordal graph theory and probabilistic analysis, under stochastic models of gene and sequence evolution.