New algorithms for inferring multiple alternative consensus trees and supertrees

每个基因都有自己的进化历史，这与其他基因的进化历史有很大的不同。例如，某些个体基因可能受到特定水平基因转移和重组事件的影响。因此，每个基因的进化史应该用它自己的系统发育树来表示，这可能显示出与物种树（或生命树）不同的进化模式，物种树（或生命树）解释了垂直下降的主要模式。因此，多共识树和超树的推理问题就有了意义。这些可选择的共识树和超树代表了最重要的网状进化模式，表征了一组给定的基因系统发生。它们通常比单一共识树或通过传统方法推断的超树更好地解决。因此，使用多重共识树或超树，我们有可能从给定的基因系统发育集中提取更多可信的信息。这个计算生物学研究项目的目标是为解决系统发育树分类这一具有挑战性的问题建立数学和统计基础，并创建一个新的开源平台，允许生物学家使用我们的新方法。我们将定义新的优化标准，用于使用聚类算法和几个重要的树度量来构建多个备选共识树和超树。我们提出了以下三个主轴来创建一个研究基因进化史的计算框架：(1)开发新的有效算法来分类和构建一组合理的多个替代共识树和超级树，以表征现有数据；(2)探索氨基酰基- trna合成酶的进化模式；(3)设计并实现一个新的开源软件平台，该平台包含本研究计划框架内开发的所有算法以及真实和模拟数据。本研究计划的长期目标是开发一种创新的系统和自动化的方法来重建受网状进化事件影响的不同物种群体的遗传进化的不同情景。例如，拟议的研究将有助于推断生命之树的多个备选子树，这些子树汇集了所有生物体的生物多样性和进化关系的关键信息。

Each gene has its own evolutionary history which can substantially differ from the evolutionary histories of other genes. For example, some individual genes can be affected by specific horizontal gene transfer and recombination events. Thus, the evolutionary history of each gene should be represented by its own phylogenetic tree which may display different evolutionary patterns from the species tree (or Tree of Life) that accounts for the main patterns of vertical descent. Thus, the problem of inference of multiple consensus trees and supertrees becomes relevant. These alternative consensus trees and supertrees represent the most important reticulate evolutionary patterns characterizing a given set of gene phylogenies. They are often be much better resolved than a single consensus tree or a supertree inferred by traditional methods. Hence, using multiple consensus trees or supertrees we have the potential to extract much more plausible information from the given set of gene phylogenies. The goal of this computational biology research program is to establish the mathematical and statistical foundation for solving the challenging problem of classification of phylogenetic trees, and to create a new open-source platform allowing biologists use our new methods. We will define new optimization criteria for building multiple alternative consensus trees and supertrees using clustering algorithms and several important tree metrics. We propose the three following main axes for creating a computational framework for studying the evolutionary history of genes: (1) developing new effective algorithms to classify and build a plausible set of multiple alternative consensus trees and supertrees characterizing the data at hand, (2) exploring the patterns of evolution of Aminoacyl-tRNA synthetases, and (3) designing and implementing a new open-source software platform containing all the algorithms developed in the framework of this research program along with the real and simulated data. The long-term goal of this research program is to develop an innovative systematic and automated approach to reconstruct different scenarios of genetic evolution of different groups of species affected by reticulate evolutionary events. For example, the proposed research will contribute to the inference of multiple alternative subtrees of the Tree of Life that assembles the key information on the biodiversity and evolutionary relationships of all living organisms.