EBI Metagenomics - enabling the reconstruction of microbial populations

微生物几乎生活在地球上的所有环境中。例如，海洋中的微生物比已知宇宙中的恒星还多，从热带到极地水域，从光线充足的表层水域到深渊，复杂的群落生活在截然不同的生态位中。它们采集和转换太阳能，据估计，它们对全球初级生产的贡献率为50%-90%，通过光合作用将光转化为生物质，使它们对世界食物链至关重要。微生物产生和消耗大多数温室气体(二氧化碳、一氧化二氮和甲烷)，这对人为气候变化特别重要。它们还负责地球上一半以上的氧气生产。在生态系统中，微生物催化维持有机生产力的营养物质和微量元素的关键生物地球化学转化。了解这些过程将带来许多潜在的好处。例如，研究微生物将有机磷解锁为可被植物吸收的可溶性形式的机制，可以减少化肥的使用，提高农业产量。在每种环境中，微生物种群都包含一个巨大的、动态的遗传变异性储存库，其中大部分还有待研究。目前的生物数据库并不代表绝大多数环境生物，因为传统的基因组测序方法需要分离和培养。元基因组学，即对环境样本中发现的整个DNA集合进行测序的方法，绕过了这一需求。因此，我们已经开始回答一些关键问题，即在哪些环境中发现了哪些有机体。在广泛的学科范围内，这种方法已经得到了巨大的接受。然而，过去十年产生的大多数元基因组学项目只给出了潜在微生物基因组的片断图景，因为需要更大数量的测序来提高基因组细节水平。在数据驱动的科学时代，随着测序技术的普及和成本的不断下降，海量的序列数据为在更详细的水平上了解微生物世界提供了一个惊人的机会。然而，元基因组学领域面临以下问题：1)由于数据量巨大，需要专家建立高效、高通量的分析管道；2)结果的生物信息学分析成本高昂，需要专家知识；3)为了从实验中提取最大限度的知识，需要系统地捕获相关的实验数据和序列数据；4)不同分析方法之间缺乏一致性，影响了可比性。EBI元基因组学(EMG)资源通过为所有元基因组数据的分析和存档提供免费服务来解决这些问题。随着算法和方法的进步，现在可以拼凑组成单个有机体基因组的片段。在这个项目中，我们不仅将继续提供肌电图，而且还将开发从元基因组生成基因组所需的分析、存档、工具和数据呈现框架。由于EMG的独特地位，我们还将能够将包含相似微生物群落的不同项目的数据结合在一起。这种重要的数据再利用将使我们能够产生最高质量的基因组，使我们能够检测不同的细菌菌株，并确保我们利用之前的投资。我们的基因组将丰富当前的生命之树，我们将扩展肌电接口，以适应我们将产生的新数据。这将推动环境、生物产业、农业和医学(人和牲畜)方面的研究和创新。我们将与生物技术行业密切合作，使它们能够利用发现的巨大潜力。

Microorganisms inhabit practically all environments on Earth. For example, there are more microbes in the ocean than stars in the known universe, with complex communities living in vastly different niches, from the tropics to the polar waters and from well-lit surface waters to the deep abyss. They harvest and transduce solar energy and is estimated that they contribute 50-90% to global primary production, turning light into biomass through photosynthesis, making them vital to the world's food chain. Microbes produce and consume most greenhouse gases (carbon dioxide, nitrous oxide and methane), which is of particular importance in relation to man-made climate change. They are also responsible for over half of all oxygen production on Earth. Within ecosystems, microbes catalyse the key bio-geochemical transformations of nutrients and trace elements that sustain organic productivity. Understanding these processes would bring many potential benefits. For example, working out the mechanisms by which microbes unlock organic phosphate to a soluble form that can be absorbed by plants could reduce the use of fertilizers and increase agricultural yields. Within each environment, the microbial population contains a vast and dynamic reservoir of genetic variability, much of which is yet to be studied. Current biological databases do not represent the vast majority of environmental organisms, as traditional genome sequencing approaches require isolation and culturing. Metagenomics, the sequencing of the entire collection of DNA found within an environmental sample, circumvents this need. As a result, we have begun to answer some of the key questions about which organisms are found in which environments. There has been a huge uptake of the approach across a broad range of disciplines. Nevertheless, the majority of metagenomics projects produced over the past decade have given only a fragmentary picture of underlying micro-organisms genomes, as larger volumes of sequencing are required to improve the level of genomic detail.In the era of data driven science, and with widespread access to sequencing technology and ever diminishing costs, huge volumes of sequence data present an amazing opportunity to understand the microbial world at a more detailed level. However, the field of metagenomics faces the following issues: 1) given the vast data volumes, specialist expert-built pipelines are required for efficient, high-throughput analysis; 2) bioinformatics analysis of results is costly to produce and requires expert knowledge; 3) to extract maximum knowledge from experiments, there is a need to systematically capture the associated experimental data along with the sequence data; 4) there is a lack of consistency between different analysis approaches, affecting comparability. The EBI Metagenomics (EMG) resource solves these issues by offering a free service for the analysis and archiving of all metagenomic data. With advances in algorithms and methods, it is now possible to piece together the fragments that make up an individual organism's genome. In this project, we will not only continue the provision of the EMG, but also develop the analysis, archiving, tools and data presentation frameworks required to generate genomes from metagenomes. Due to the unique position of EMG, we will be also able to combine data across different projects that contain similar microbial communities. This important data reuse will enable us to generate the highest quality genomes, allow us to detect different strains of bacteria and ensure that we capitalize on previous investments. Our genomes will enrich the current tree of life, and we will extend the EMG interfaces to accommodate the new data that we will produce. This will empower research and innovation in the environment, bioindustries, agriculture and medicine (human and livestock). We will work closely with biotechnological industries, to enable them to harness the huge potential for discovery.