Abstract：
Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome\'s functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute\'s Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data.
OBJECTIVE: Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome\'s metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.
OBJECTIVE: Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome\'s metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.
摘要：
宏基因组测序通过为不同生态系统的微生物组成提供前所未有的观点，提高了我们对生物地球化学过程的理解。虽然宏基因组数据的数量快速增长，简单易用的分析和比较跨研究的方法已经落后了。因此，需要表达社区代谢特征的工具来扩大现有数据的效用。基因丰度谱是宏基因组功能潜力的相对低维嵌入，因此，易于在许多样品中进行比较。这里,我们比较了来自联合基因组研究所的综合微生物基因组和宏基因组（JGIIMG/M）数据库的6,539个宏基因组的KEGG直系同源组（KO）的丰度。我们发现样本聚集在陆地上，水生,和厌氧生态系统，其标记KO反映了对这些环境的适应。例如,功能簇通过抗生素的代谢来区分，光合作用,产甲烷，令人惊讶的GC含量。使用这种功能基因方法，我们揭示了塑造微生物群落的广泛模式，并证明了直系同源丰度谱在代表快速扩展的宏基因组数据中的实用性。
目标：宏基因组学，或复杂微生物组的DNA测序，提供了对不同环境的微生物组成的看法。创建了宏基因组数据库以编译研究中的测序数据，但是，从这些大型数据集中进行比较和获得洞察力仍然具有挑战性。因此,需要开发可访问的方法来提取宏基因组中的知识。不同直系同源物的丰度(即，跨物种执行类似功能的基因）提供了宏基因组代谢潜力的简化表示，可以很容易地与其他人进行比较。在这项研究中,我们将来自不同环境的数千种宏基因组的直系同源丰度谱进行聚类，并发现区分它们的特征。这项工作为功能比较提供了一个简单易用的框架，并增进了我们对环境如何塑造微生物群落的理解。
目标：宏基因组学，或复杂微生物组的DNA测序，提供了对不同环境的微生物组成的看法。创建了宏基因组数据库以编译研究中的测序数据，但是，从这些大型数据集中进行比较和获得洞察力仍然具有挑战性。因此,需要开发可访问的方法来提取宏基因组中的知识。不同直系同源物的丰度(即，跨物种执行类似功能的基因）提供了宏基因组代谢潜力的简化表示，可以很容易地与其他人进行比较。在这项研究中,我们将来自不同环境的数千种宏基因组的直系同源丰度谱进行聚类，并发现区分它们的特征。这项工作为功能比较提供了一个简单易用的框架，并增进了我们对环境如何塑造微生物群落的理解。
