Terraces, large phylogenetic trees, and trait evolution

科学家们正在使用大量的DNA序列数据集和新的计算技术，将所有200万种生物放入一个大合成中，即生命的进化树。进化树可以根据近亲物种中存在的特征来预测生物体所具有的特征。这在医学、保护生物学和作物改良等领域具有重要的实际效益。虽然从几个基因的DNA中构建小树很简单，但将这一努力扩大到所有生命仍然存在许多障碍。最严重的问题之一是数据缺失。在某些情况下，即使是少数物种的几个基因序列缺失，也会产生缺乏详细分辨率的进化树。这项资助是生物学家、数学家和计算机科学家之间的合作，目的是了解数据缺失导致问题的情况，并开发克服这些问题的方法。这项研究将产生数学上可证明的结果和新的软件，以帮助生物学家建立更可靠的大型进化树。这些产品将用开花植物的真实数据进行测试，开花植物是生命之树中最多样化的分支之一，拥有超过25万个物种。由于该项目产生的高质量进化树将通过提供清晰的进化知识来为保护规划和优先排序提供信息，从而满足社会需求。研究小组将为全国各地的研究生组织一个讲习班，培训他们使用这些新工具，并将为高中学生开发包括生命之树的计算机可视化在内的教育材料。从数据库中挖掘的序列数据中构建非常大的系统发育树可能具有挑战性，因为最近发现了梯田问题——“树空间”中潜在的大片区域，由于缺少数据，所有的树都具有完全相同的最优性得分。本研究首先侧重于更好地从概念上理解梯地对大树建设的四个问题影响：(i)模糊性增加，（ii）从自举或贝叶斯后验概率获得的有偏见的置信度评估，（iii）树木搜索算法的障碍，以及（iv）对依赖树木的比较推断的下游影响。接下来，研究将开发分析方法和软件实现来克服这些问题。最后，本文将对这些新方法进行测试，将其应用于27个大型开花植物系统发育系统中，每个系统发育系统中有1000多个物种，作为下游比较推断的范例，在其中的一个子集中检查性状进化。

Scientists are using massive data sets of DNA sequences and new computing technologies to place all 2 million biological species into a grand synthesis, the evolutionary tree of life. Evolutionary trees permit predictions about the traits that organisms possess based on traits present in closely related species. This has important practical benefits in areas like medicine, conservation biology, and crop improvement. Although building small trees from DNA from just a few genes is straightforward, many obstacles remain to scaling up this effort to all life. One of the most serious is missing data. Under some conditions, even a few gene sequences missing for a handful of species can produce evolutionary trees that lack detailed resolution. This grant is a collaboration among biologists, mathematicians and computer scientists to understand the circumstances in which missing data cause problems, and to develop methods to overcome them. The research will generate mathematically provable results and new software to help biologists build more reliable large evolutionary trees. These products will be tested with real world data sampled from flowering plants, one of the most diverse branches of the tree of life, with over 250,000 species. The high quality evolutionary trees generated because of this project will provide a societal need by providing clear evolutionary knowledge to inform conservation planning and prioritization. The research team will organize a workshop for graduate students around the country to train them in the use of these new tools, and will develop educational materials for high school students involving computer visualization of the tree of life.The construction of very large phylogenetic trees from sequence data mined from databases can be challenging because of the recently discovered problem of terraces--potentially vast regions in "tree space" in which all trees have precisely the same optimality score due to missing data. This research focuses first on developing a better conceptual understanding of four problematic impacts of terraces on large tree construction: (i) increased ambiguity, (ii) biased confidence assessments obtained from bootstrapping or Bayesian posterior probabilities, (iii) impediments to tree search algorithms, and (iv) downstream effects on comparative inferences that rely on trees. Next, the research will develop analytical methods and software implementations to overcome these problems. Finally, it will test these new methods by applying them to 27 large-scale phylogenies newly constructed within flowering plants, each with 1000+ species, examining trait evolution, as an exemplar of downstream comparative inference, in a subset of these.