Investigating how non-homologous recombination structures genes, proteins, operons, clusters, genomes and ecosystems

自达尔文时代以来，我们就把地球上的生命进化看作是一棵伟大的统一的生命之树。达尔文在给托马斯·亨利·赫胥黎的信中说：“总有一天(虽然我不会有生之年看到它)，那时我们将对每一个伟大的生命王国有相当真实的谱系。”在其间的大部分时间里，人们的重点一直是试图构建这棵伟大的生命之树。然而，尽管许多生命是树状的和多样化的，但许多生命也参与了融合的过程。从简单的共生，可能成为永久的，也可能不是永久的(例如，我们看到的为地球上的植物生命提供动力的叶绿体是曾经自由生活的细菌的后代)，到植物或动物的杂交，再到基因的融合，我们看到了许多许多合并的例子。不幸的是，我们对这些合并的了解远远落后于我们对多样化进化的了解。在这项建议中，我们将扩大我们对合并的理解，使这类分析更容易和更全面。我们的首要目标是开发软件来帮助分析基因数据。在这一努力中，我们得到了谷歌、Facebook和Twitter等实体的极大帮助--或许相当令人惊讶。这些公司的技术基于我们用于分子序列的图形类型。当一个人加入Facebook时，他们被软件表示为图表上的一个“节点”。当它们“加”某人为好友时，就会在这两个节点之间画出一条“边”。当他们“喜欢”一个帖子或页面时，就会在Person节点和页面节点之间画出一条不同类型的边。人物和页面构成了一个二分图。页面的特征是，比如说政治页面，或者对运动或家具感兴趣的页面等。因此，页面有另一个层次。总体而言，在人、页面、组、兴趣等之间。Facebook将他们的整个业务表示为一个多层次的图表。我们现在正在对进化实体做同样的事情。在进化对象的多层次分析的情况下，我们可以将最小的进化对象(比方说，蛋白质结构域)表示为节点。如果两个域是同源的(它们共享一个共同的祖先并且是相关的)，那么我们可以在它们之间画一条边。如果出现在相同的蛋白质/基因上，那么我们就可以在结构域和该基因之间画出边界(就像两个人在Facebook上有相同的钓鱼兴趣一样)。然后，我们可以将该基因定性为一种特定的“类型”，比如新陈代谢，或膜嵌入。我们还可以指出我们网络中位于同一染色体上的基因(类似于说它们有相同的“兴趣”)。我们也可以有一个网络层面，在那里我们可以指出有机体是否自由生活、致病、厌氧、参与代谢联盟等。就像我们在社区形成的社会网络上看到的一样，我们在序列网络上看到社区形成。有许多相似之处，我们可以对进化是如何真正构建地球上的生命结构获得重要的见解。例如，初步研究表明，一些序列是混杂的，而另一些则不是。某些结构域在基因中广泛存在，而有些只在一种序列中发现，而在另一种序列中没有发现。我们看到了一些质粒，比如在莱姆病致病细菌疏螺旋体中发现的那些具有独特类型基因的质粒，但这些基因在疏螺旋体的各种质粒中都能找到。换句话说，基因是受物种限制的，而不是受质粒限制的。这项计划的结果将是拥有灵活的软件，以及对进化如何具有结构、基因和基因组的几个新见解。

Since Darwin's time we have thought of the evolution of life on the planet in terms of a great unifying tree of life. Darwin, writing to Thomas Henry Huxley said "The time will come (though I will not live to see it), when we shall have fairly true genealogies of each great kingdom of life". For much of the intervening years, the focus has been on trying to construct this great tree of life. However, while much of life is tree-like and diversifying, much of life is also involved in the process of merging. From simple symbioses, which might or might not become permanent (e.g. the chloroplast that we see powering plant life on the planet is the descendent of a once free-living bacterium), to the hybridization of plants or animals, to the fusion of genes, we see many, many instances of mergings. Unfortunately, our knowledge of these mergings lags well behind our knowledge of diversifying evolution. In this proposal, we will broaden our understanding of mergings and make such analyses easier and more comprehensive.Our first objective is to develop software to help analyse genetic data. In this effort we have been helped enormously - and perhaps quite surprisingly - by entities such as Google, Facebook and Twitter. These companies have based their technology around the kinds of graphs we are using for molecular sequences. When a person joins Facebook, they are represented by the software as a "node" on a graph. When they "friend" somebody, then an "edge" is drawn between these two nodes. When they "like" a post or a page, a different kind of edge is drawn between the person node and the page node. People and pages form a bipartite graph. Pages are characterised, say as being political pages, or pages with an interest in sport or furniture, etc. Therefore, there is another level for pages. Overall, between people, pages, groups, interests, etc. Facebook represents their entire business as a multi-level graph. We are now doing the same kind of thing for evolving entities.In the case of multilevel analysis of evolving objects, we can represent the smallest of evolving objects (say, a protein domain) as a node. If two domains are homologous (they share a common ancestor and are related), then we can draw an edge between them. If the appear on the same protein/gene, then we can draw edges between the domains and that gene (like as if two people have the same interest in fishing, on facebook). We can then characterise the gene as being of a particular "kind", say metabolic, or membrane-embedded. We can also indicate genes on our network, that are sitting on the same chromosome (analogous to saying they have the same "interest"). We can also have a network level where we indicate whether the organism is free-living, pathogenic, anaerobic, involved in a metabolic consortium, etc.In the same way that we see on social networks that communities form, we see on sequence networks that communities form. There are many parallels and we can gain significant insights into how evolution is really structuring life on the planet. For instance, preliminary studies have shown that some sequences are promiscuous and some are not. Certain domains are widespread in genes, while some are only found in one kind of sequence and no other. We see plasmids, such as those found in the Lyme-disease-causing bacterium Borrelia that have unique kinds of genes, but these genes are found across the diversity of Borrelia plasmids. In other words, the genes are species-restricted, but not plasmid restricted.The outcome of this programme will be to have flexible software and several new insights into how evolution has structures genes and genomes.