AF: Medium: Statistical Inference of Complex Evolutionary Histories

基因是所有生命形式的基本组成部分。了解基因如何进化并使其功能多样化将有助于阐明生物学中的许多现象和过程，包括疾病如何出现以及如何治疗疾病。基因经历的进化事件范围从小规模的（例如，一个核苷酸被另一个核苷酸取代）到大规模的（例如，一个基因被复制，导致同一基因的多个副本）。准确识别不同基因家族的这些进化事件是本项目的重点。特别是，该项目旨在设计数学模型、计算技术和软件产品，以便根据各种进化过程绘制基因随时间的轨迹。该项目将对生物学和生物医学产生影响，将产生可公开使用的软件产品，使新的分析成为可能，并将在计算机科学、统计学和生物学的交叉领域培养学生。推断物种的准确进化历史或系统发生是进化生物学的一项主要工作，对生物学的各个方面都有影响。这种推断过去是通过从被调查物种的基因组中对某个感兴趣的区域进行测序，建立该区域的谱系或基因树，并宣布该树为物种系统发育来进行的。在后基因组时代，这种做法已被利用数百个基因组区域所取代。虽然这种新的实践有望产生更准确的物种系统发育估计，但它也带来了一个新的重大挑战，即解释可能同时作用于不同基因组区域的不同进化过程。特别是，三个进化过程在后基因组进化分析中一直很突出：不完全谱系分类（ILS）、水平转移（或基因流动）和基因复制/丢失（GDL）。目前，还没有统计方法可以同时解释这三个过程来推断基因和基因组的进化关系。该项目的首要目标是为这项任务开发数学模型和算法技术。该项目将产生数学和算法结果，以及开源软件，该软件将从全基因组数据中推断物种系统发育，同时考虑ILS、基因流和GDL。

Genes are an essential building block of all forms of life. Understanding how genes evolve and diversify their function would contribute significantly to elucidating many phenomena and processes in biology, including how diseases emerge and how to treat them. Genes undergo evolutionary events that range from small-scale ones (e.g., one nucleotide is replaced by another) to large-scale ones (e.g., a gene gets duplicated resulting in more than one copy of the same gene). Accurately identifying these evolutionary events for different gene families is the focus of this project. In particular, the project is aimed at devising mathematical models, computational techniques, and software products for mapping the trajectory of a gene through time in light of a variety of evolutionary processes. The project will have impact on biology and biomedicine, will result in publicly available software products that enable new analyses, and will train students at the intersection of computer science, statistics, and biology. Inferring accurate evolutionary histories, or phylogenies, of species is a major endeavor in evolutionary biology, and has implications on all aspects of biology. This inference used to be conducted by sequencing a certain region of interest from the genomes of species under investigation, building a genealogy, or gene tree, for the region, and declaring the tree to be the species phylogeny. In the post-genomic era, this practice has been replaced by utilizing hundreds of genomic regions. While this new practice promises to yield more accurate estimates of the species phylogeny, it also gives rise to a new major challenge, namely accounting for the different evolutionary processes that could be acting simultaneously on the different genomic regions. In particular, three evolutionary processes have been prominent in post-genomic evolutionary analysis: incomplete lineage sorting (ILS), horizontal transfer (or, gene flow), and gene duplication/loss (GDL). Currently, no statistical methods exist for the task of inferring evolutionary relationships of genes and genomes while accounting for all these three processes simultaneously. The overarching goal of the project is to develop mathematical models and algorithmic techniques for this task. The proposed project will produce mathematical and algorithmic results, as well as open-source software that would enable species phylogeny inference from genome-wide data while simultaneously accounting for ILS, gene flow, and GDL.