Comp Bio: Collaborative Research: EMT: "Efficient Techniques for Reconstructing Horizontal Gene Transfer in Bacteria"

在真核生物中，进化主要是一个垂直下降的过程;换句话说，遗传物质从祖先遗传给后代。另一方面，在细菌中，遗传物质也可以在远亲生物体之间“水平”转移。这个过程被称为水平基因转移（HGT），据信在各种细菌中普遍存在，在基因组多样化以及其他细菌相关问题（例如抗生素耐药性的获得）中发挥着重要作用。在这个项目中，我们提出了一套方法，使高效和准确的基于同源性的细菌基因组中的HGT的重建。在基于遗传学的方法中，为一组细菌基因组中的每个基因构建一棵树，并对基因树的不一致进行量化和分析以检测HGT。这种方法面临两个主要挑战：1。基因树可能不仅由于HGT而不一致，而且由于其他过程，例如基因复制和丢失，谱系分选等，因此，在对HGT事件进行任何估计之前，必须开发一个确定基因树不一致原因的框架。比较和协调基因树，以获得估计的HGT事件和率是计算上非常困难的tasks.To改善这两个挑战，我们建议开发一个随机框架，将人口遗传学理论与进化事件，物种之间的行为来分类基因树的分歧。此外，我们将开发数学标准和算法技术来比较基因树，量化它们的分歧，并推断HGT事件的数量和位置。所有模型和算法都将在一个软件包Sequoia中实现，该软件包将通过开源机制公开提供。

In eukaryotic organisms, evolution is mostly a process of vertical descent; in other words, genetic material is inherited from ancestors to descendants. In bacteria, on the other hand, genetic material may also be transferred "horizontally" among distantly related organisms. This process, known as horizontal gene transfer (HGT) and which is believed to be ubiquitous among various groups of bacteria, plays a major role in genome diversification as well as other bacteria-related issues, such as antibiotic resistance acquirement. In this project, we propose a set of methodologies to enable efficient and accurate phylogeny-based reconstruction of HGT in bacterial genome. In the phylogeny-based approach, a tree is built for each gene in the genome of a group of bacteria, and the gene tree disagreements are quantified and analyzed to detect HGT. Two major challenges face this approach: 1. Gene trees may disagree not only due to HGT, but also due to other processes, such as gene duplication and loss, lineage sorting, etc. Hence, it is imperative to develop a framework that determines the cause of gene tree disagreements before any estimates of HGT events are made.2. Comparing and reconciling gene trees to obtain estimates of HGT events and rates are computationally very hard tasks.To ameliorate these two challenges, we propose to develop a stochastic framework that incorporates population genetics theories with evolutionary events that act among species to classify gene tree disagreements. Further, we will develop mathematical criteria and algorithmic techniques for comparing gene trees, quantifying their disagreements, and inferring the numbers and locations of HGT events. All the models and algorithms will be implemented in a software package, Sequoia, which will be made publicly available through open source mechanisms.