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

了解地球上生命的历史？物种是如何从它们的共同祖先进化而来的？是生物学研究的一个主要目标。这些进化树很难高精度地构建，因为几乎所有最准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不同于该物种的进化树，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，覆盖生命树很大一部分的大型进化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。关键方法是开发新的分治策略，即将数据集划分为重叠的子集，在子集上构建物种树，然后将子集物种树合并在一起形成完整数据集上的一棵树。这些方法将与强大的统计估计方法相结合，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提供跨学科培训，一名在伊利诺伊州，一名在伯克利，计算生物学的课程材料将在网上提供。了解进化，以及它是如何对物种和基因起作用的，是生物数据分析的重要部分。统计估计方法通常提供最好的准确性，但不能扩展到现代生物学所需的数据集大小。此外，基因组进化树的异质性对物种树估计提出了挑战，目前还没有一种方法能够为基因组规模的数据提供高度准确的物种树。这些挑战使得开发新的方法以使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成为可能。这个项目将开发新的算法策略来解决三个关键问题：超树估计，存在基因树异质性的物种树估计，以及大规模数据集的比例统计方法。除了开发图论算法外，该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.