ABI Innovation: Collaborative Research: Novel Methodologies for Genome-scale Evolutionary Analysis of Multi-locus Data

莱斯大学、密歇根大学和德克萨斯大学奥斯汀分校获得了合作资助，用于开发和实施算法和软件工具，以分析基因谱系并从中推断物种系统发育。基因谱系，也被称为基因树，模拟了基因在进化过程中如何复制并从一代传递到下一代。物种系统发育模拟了物种如何产生和分化。一个物种的系统发育传统上是通过三个步骤来推断的：(1)从所研究的物种中选出一个基因组区域进行测序；(2)推断出基因组区域的“基因树”；(3)将基因树声明为物种树。然而，最近对不同生物群体的进化基因组分析表明，不同基因组区域的进化历史可能彼此不一致，也可能与物种不一致。此外，进化过程，如水平基因转移，导致网络状，而不是树状，物种系统发育。这个联合项目将开发精确的计算方法，以确定基因树不一致的原因，并从基因树推断物种系统发育（树和网络），尽管它们不一致。将特别强调方法的效率，以便它们能够分析基因组规模的数据集。所有的方法都将被实施并进行广泛的性能测试。所有开发的方法都将在软件包中公开提供，这些软件包是我们在各自的小组中开发的。这些材料将被整合到pi在各自机构定期教授的课程中。最后但并非最不重要的是，该项目将以一个为期两天的研讨会结束，该研讨会向来自全国各地的学生和博士后研究员开放，由研究人员介绍所开发的方法，以及使用工具分析数据的实践教程。

Rice University, the University of Michigan, and the University of Texas at Austin are awarded collaborative grants to develop and implement algorithms and software tools for the analysis of gene genealogies and inference of species phylogenies from them. A gene genealogy, also known as gene tree, models how genes replicate and get transmitted from one generation to the next during evolution. A species phylogeny models how species arise and diverge. A species phylogeny is traditionally inferred by a three-step process: (1) a genomic region from the set of species under study is sequenced; (2) a "gene tree" is inferred for the genomic region; and, (3) the gene tree is declared to be the species tree. However, recent evolutionary genomic analyses of various groups of organisms have demonstrated that different genomic regions may have evolutionary histories that disagree with each other as well as with that of the species. Further, evolutionary processes such as horizontal gene transfer, result in network-like, rather than tree-like, species phylogenies. This joint project will develop accurate computational methods for determining the causes of gene tree discordance, and inferring species phylogenies (trees as well as networks) from gene trees despite their discordance. Special emphasis will be put on the efficiency of the methods so that they allow for analysis of genome-scale data sets. All methods will be implemented and extensively tested for performance. All methods developed will be made publicly available in software packages that we have been developing in the respective groups. The material will be integrated into courses that the PIs regularly teach at their respective institutions. Last but not least, the project will culminate with a two-day workshop, open to students and post-doctoral fellows from around the country, with presentations by the investigators on the methodologies developed, as well as hands-on tutorials on using the tools in analyzing data.