III: AF: Medium: Collaborative Research: Enabling Phylogenetic Inference for Modern Data Sets

生物学和生物医学研究中的许多重要课题都需要一种强大的系统发育树推断方法：用于病毒传播模型、基因功能推断以及评估人类微生物组的遗传多样性等。这些应用还依赖于评估树推理不确定性的严格方法;贝叶斯框架提供了评估和整合这种不确定性的原则性方法。目前可用的贝叶斯算法工具不能对大型现代数据集进行推断，这些数据集也可能随着新的测序结果变得可用而不断变化。特别是，最先进的方法几乎完全基于随机游走马尔可夫链蒙特卡罗（MCMC），使用统一选择的局部移动，即使这些局部移动中的大多数将大大恶化，即使是平庸的树。这种方法的收敛问题是有据可查的，因此目前的方法仅限于大约1000个序列，这个数字远远小于与现代生物医学相关的微生物和免疫数据集的大小。此外，所有当前的方法都需要在每次序列数据改变时从头开始推断。这项工作的更广泛的影响将在三个方向延伸：使贝叶斯遗传学的新应用，刺激计算机科学研究的新领域，并吸引新的人才到该领域。贝叶斯遗传学的应用，特别是贝叶斯遗传学，正在显着阻碍计算限制。 高通量测序技术可以返回数百万个序列用于人类微生物组、病毒、海洋微生物和抗体制造B细胞的研究，但这些不能用现有方法处理。 这些模型还需要更加现实，不需要假设独立的相互作用。 详细了解多维系统发育似然曲面的形状可能有助于改进拓扑结构。 这些小组还将调查一个分类单元集上的最优树何时包含一个分类单元子集上的最优树。 这些将有助于扩展系统发育推断的方法。这些算法见解将被纳入公开可用的推理包，目标是提供比目前可能的数量级更多的分类群的推理。

Many important subjects in biological and biomedical research require a robust means of phylogenetic tree inference: for models of viral transmission, for gene function inference, and for assessment of genetic diversity in the human microbiome, to name a few. These applications also depend on a rigorous means of assessing tree inference uncertainty; the Bayesian framework provides a principled means of assessing and integrating out this uncertainty. The currently available Bayesian algorithmic tools are not capable of performing inferences on large modern data sets, which also may be continually changing as new sequencing results become available. In particular, state-of-the-art methods are almost exclusively based on random-walk Markov chain Monte Carlo (MCMC) using uniformly selected local moves, even though most of these local moves will substantially worsen even a mediocre tree. Convergence problems with this approach are well documented, and thus current methods are limited to around 1000 sequences, a number much smaller than the size of microbial and immune data sets relevant to modern biomedicine. In addition, all current methods require inference to be started from scratch each time the sequence data changes. The broader impacts of this work will extend in three directions: enabling novel applications of Bayesian phylogenetics, stimulating new areas of computer science research, and attracting new talent to the field.Applications of phylogenetics, in particular Bayesian phylogenetics, are being significantly held back by computational limitations. High-throughput sequencing technologies can return millions of sequences for studies of the human microbiome, viruses, oceanic microbes and antibody-making B Cells but theses cannot be handled with current methods. The models also need to be more realistic, without assumptions of independent interactions. Understanding the shape of multidimensional phylogenetic likelihood surfaces in detail might help to improve the topology. The teams will also investigate when an optimal tree on a taxon sets contains the optimal tree on a taxon subset. These will help to expand the approach to phylogenetic inference. These algorithmic insights will be incorporated into publicly available inference packages with a goal to provide inference on an order of magnitude more taxa than currently possible.