Advancing understanding of the evolution of key bacterial and fungal genes in microbial communities through metagenomic assembly optimisation and context-aware graph algorithms

微生物支撑着地球上每个生态系统的功能。宏基因组测序，即我们一次确定样本中所有微生物的DNA，是了解微生物作用、它们如何相互作用以及这如何影响我们的重要工具。通过观察海洋、农场和医院等环境中微生物相互作用的生态系统，我们可以尝试回答与人类相关的问题：微生物是如何对抗生素产生耐药性的？微生物如何应对气候变化？是什么导致了新的传染病的出现？比较来自不同微生物的DNA告诉我们，DNA有时可以在不相关的微生物之间转移，这一过程称为横向基因转移。我们还了解到，围绕特定基因的DNA在基因的作用、进化方式以及以这种方式转移的可能性方面起着重要作用。因此，为了了解抗生素耐药性等功能如何在微生物群落中进化和传播，重要的是能够识别DNA片段来自哪个微生物，该DNA中的哪个基因导致该功能，以及该基因旁边的DNA是什么。最近，宏基因组分析的两个进展可能有助于解决这些问题：将来自同一微生物的DNA序列分组在一起的方法（宏基因组组装的基因组）和直接观察当我们将DNA片段组装成更长序列时形成的网络的方法（序列图）。不幸的是，大多数做这些事情的工具都有明显的缺点。尽管其他微生物如真菌和变形虫在微生物群落的功能和进化中起着重要作用，但微生物分类方法往往只关注细菌。如果我们想研究横向基因转移，它们对一些最重要的DNA类型也表现不佳：移动的遗传元件。另一方面，用于分析序列图的方法在运行时对计算要求很高，并且容易给出不正确的结果。因此，本提案包括两个互补项目，力求解决这些不足之处。首先，我们将开发自动识别工具和设置的最佳组合的方法，以正确地对来自移动的遗传元素和微生物（如真菌和变形虫）的DNA进行分组。然后，我们将利用这一点来试图更好地了解哪些DNA片段控制微生物群落可以做的事情，例如，抵抗抗生素或消化塑料。第二，我们将开发使用序列图识别横向基因转移的方法。通过绘制这些模式，我们可能能够预测基因何时以及为什么在微生物之间转移。总之，这些结果将教会我们如何优化我们现有的工具，以及如何从我们已经收集的数据中学习更多。这将使我们能够更好地了解微生物如何相互作用以及微生物功能如何进化和传播，从而对医学，农业，工程和生态学产生影响。

Microbes underpin the functioning of every ecosystem on the planet. Metagenomic sequencing, in which we determine the DNA of all the microbes in a sample at once, has been a vital tool in understanding what microbes do, how they interact with one another, and how this impacts us. By looking at the interacting ecosystem of microbes in environments such as the oceans, farms, and hospitals we can try to answer questions relevant to humanity: How do microbes become resistant to antibiotics? How do microbes respond to climate change? What leads to the emergence of new infectious diseases? Comparing DNA from different microbes has taught us that DNA can sometimes transfer between unrelated microbes in a process known as lateral gene transfer. We've also learnt that the DNA that surrounds a particular gene plays an important role in what the gene does, how it evolves, and how likely it is to be transferred in this way. Therefore, to understand how functions like antibiotic resistance evolve and spread in microbial communities it is important to be able to identify which microbe a piece of DNA came from, which gene in that DNA leads to that function, and what DNA is next to that gene. Recently, two advances in metagenomic analysis may help solve these problems: methods to group DNA sequences from the same microbe together (metagenome-assembled genomes) and methods looking directly at the network formed when we assemble DNA fragments into longer sequences (sequence graphs). Unfortunately, most tools to do these things have significant shortcomings. Grouping methods tend to focus only on bacteria despite other microbes such as fungi and amoeba playing important roles in the function and evolution of microbial communities. They also perform poorly for some of the most important types of DNA if we want to study lateral gene transfer: mobile genetic elements. On the other hand, the methods for analysing sequence graphs are very computationally demanding to run and prone to giving out incorrect results. Therefore, this proposal encompasses two complementary projects seeking to address these shortcomings. Firstly, we will develop ways to automatically identify the best possible combination of tools and settings to correctly group DNA from mobile genetic elements and microbes like fungi and amoeba. We will then use this to try to better understand which bits of DNA control things a microbial community can do e.g., resist antibiotics or digest plastics. Secondly, we will develop ways of identifying cases of lateral gene transfer using sequence graphs. By mapping these patterns we may be able to predict when and why a gene is transferred between microbes. Together, these results will teach us how to optimise our current tools and how to learn more from the data we've already collected. This will enable us to better understand how microbes interact and how microbial functions evolve and spread with implications for medicine, agriculture, engineering, and ecology.