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

自达尔文时代以来，我们一直认为地球上生命的进化是一个伟大的统一生命树。达尔文在给托马斯亨利赫胥黎的信中说："总有一天（尽管我活不到那一天），我们会有相当真实的每一个生命王国的家谱。"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，虽然生命的大部分是树状的和多样化的，但生命的大部分也参与了合并的过程。从简单的共生，可能会也可能不会成为永久性的（例如，我们看到的为地球上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，使这种分析更容易和更全面。我们的第一个目标是开发软件，以帮助分析基因数据。在这一努力中，我们得到了谷歌、Facebook和Twitter等实体的巨大帮助--也许相当令人惊讶。这些公司的技术是基于我们用于分子序列的各种图表。当一个人加入Facebook时，他们会被软件表示为图表上的“节点”。当他们把某人加为好友时，这两个节点之间就形成了一条“边”。当用户"喜欢"一篇文章或一个页面时，会在person节点和page节点之间绘制一条不同类型的边。人和页面构成一个二分图。页面的特征是，比如政治页面，或者对体育或家具感兴趣的页面等。总的来说，在人、页面、群组、兴趣等之间，Facebook将他们的整个业务表现为一个多层次的图表。我们现在对进化实体做同样的事情，在进化对象的多级分析中，我们可以将最小的进化对象（比如蛋白质结构域）表示为一个节点。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。如果出现在同一个蛋白质/基因上，那么我们可以在域和该基因之间画出边缘（就像两个人对钓鱼有相同的兴趣，在Facebook上）。然后，我们可以将该基因归类为一种特殊的“种类”，比如说代谢性的或膜嵌入性的。我们还可以在我们的网络上指出位于同一染色体上的基因（类似于说它们具有相同的“兴趣”）。我们也可以有一个网络层次，在那里我们表明生物体是否是自由生活的，致病的，厌氧的，参与代谢财团的，等等。就像我们在社交网络上看到的社区形成一样，我们在序列网络上看到社区形成。有许多相似之处，我们可以获得重要的见解，了解进化是如何真正构建地球上的生命的。例如，初步研究表明，有些序列是混杂的，有些则不是。某些结构域广泛存在于基因中，而有些结构域只存在于一种序列中，而没有其他序列。我们看到了质粒，比如在莱姆病致病细菌疏螺旋体中发现的那些质粒，它们具有独特的基因，但这些基因在疏螺旋体质粒的多样性中发现。换句话说，基因是物种限制的，但不是质粒限制的。这个计划的结果将是有灵活的软件和几个新的见解如何进化的结构基因和基因组。

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.