Collaborative Research: ABI Development: "Beyond Ribosomal RNA genes: Community Tools for Analysis of Whole-Genomes and Metagenomes"

细菌和古生菌（原核生物）的遗传多样性是迄今为止所有生物体中最大的。无论是在土壤、沃茨、人类肠道还是大气中，原核生物都会影响（如果不是控制的话）地球上所有维持生命的过程，但这些微生物如何与环境相互作用并改变环境还没有完全了解。目前的不完全理解至少部分是由于绝大多数微生物抵抗实验室培养的事实，即，他们代表了不可培养的大多数，因此不能有效地进行研究。在过去的几年里，有一个爆炸的文化无关的基因组技术（又名。宏基因组学），其允许通过对微生物的整个基因组或转录组进行测序来分析微生物及其在其自然栖息地中的群落，从而绕过了对实验室培养的需要。然而，用于分析宏基因组数据的计算工具和算法的开发落后于测序技术的发展。为了促进对大多数不可培养微生物的了解，并充分利用社会对基因组技术的投资，需要新的定量方法。该项目的目标是：1）开发新的计算工具，以满足关键的研究需求，从而帮助科学家了解微生物群落的组成，功能和价值，以及2）从本科院校，包括社区学院，在新的宏基因组学技术，这是定位在微生物学，基因组学，生物信息学和计算生物学的接口教师，这是当代研究和教育的一个关键领域，在传统课程中没有得到充分的覆盖。因此，预计这些活动将为培训未来的劳动力提供重要的基础设施，并促进当代研究。小亚基核糖体RNA基因（SSU rRNA）在过去的二十年中已成功地用于微生物多样性的分类和研究。这项工作已经通过开发专用资源（数据库和工具库）如核糖体数据库项目（RDP; http://rdp.cme.msu.edu）得到了促进。然而，基于rRNA基因的研究具有基于基因组序列的技术所不具有的重要局限性。例如，基因组技术可以在SSU rRNA基因提供不充分分辨率的水平上更好地解析微生物群落，即物种和更精细的水平，并对全基因组多样性和流动性进行编目，这与营养循环，生物修复工作和微生物抗生素抗性的出现有关。该项目旨在开发工具，克服基于rRNA基因的方法的几个局限性，并允许微生物组的有效分析。将提供两种广为接受的现有方法的稳健实施，例如用于描绘密切相关的物种和菌株的基因组聚集平均核苷酸同一性（gANI），以及沿着更新的方法，包括最近开发的用于估计由宏基因组数据集获得的微生物群落的覆盖率的Nonpareil方法，以及用于检查微生物谱系之间的水平基因转移事件的MyTaxa方法。总体目标是开发相当于RDP的基因组，使科学界能够在基因组一级进行分类和多样性研究。

The genetic diversity of bacteria and archaea (the prokaryotes) is by far the largest among all living organisms. Whether in soils, waters, human guts, or the atmosphere, prokaryotes affect, if not control, all life-sustaining processes on Earth, but how these microbes interact with and change their environment is not fully understood. Current incomplete understanding is, at least in part, due to the fact that the great majority of microorganisms resist cultivation in the laboratory, i.e., they represent the uncultivable majority, and thus, cannot be studied efficiently. In the past few years, there has been an explosion of culture-independent genomic techniques (a.k.a. metagenomics), which allow the analysis of microorganisms and their communities in their natural habitat by sequencing their entire genomes or transcriptomes, bypassing the need for lab cultivation. However, the development of computational tools and algorithms to analyze metagenomic data is lagging behind developments in sequencing technologies. To advance the understanding of the uncultivable majority of microorganisms, and take full advantage of the investment of society in genomic technologies, new quantitative approaches are needed. The goals of this project are: 1) to develop new computational tools that fulfill critical research needs and thus, help scientists understand the composition, functions and values of the microbial communities, and 2) to train faculty from undergraduate colleges, including community colleges, in new metagenomics techniques, which are positioned at the interface of microbiology, genomics, bioinformatics, and computational biology, a pivotal area of contemporary research and education that is inadequately covered in traditional curricula. Therefore, these activities are expected to provide important infrastructure for training the future workforce and to facilitate contemporary research. The small subunit ribosomal RNA gene (SSU rRNA) has been successfully used to catalogue and study the diversity of microorganisms for the last two decades. This work has been facilitated by the development of dedicated resources (databases and tool repositories) such as the Ribosomal Database Project (RDP; http://rdp.cme.msu.edu). However, rRNA gene-based studies have important limitations that techniques based on genome sequences do not. For instance, the genomic techniques can better resolve microbial communities at the levels where the SSU rRNA gene provides inadequate resolution, namely the species and finer levels, and catalogue whole-genome diversity and fluidity, which are relevant for nutrient cycling, bioremediation efforts, and emergence of microbial antibiotic resistance. This project seeks to develop tools that overcome several of the limitations of the rRNA gene-based approaches and allow the efficient analysis of microbiomes. Robust implementations of both well-accepted existing methods, such as genome-aggregate average nucleotide identity (gANI) for delineating closely-related species and strains, along with newer methods, including the recently developed Nonpareil method for estimating the coverage of a microbial community obtained by a metagenomic dataset, and MyTaxa method for examining horizontal gene transfer events between microbial lineages will be provided. The overarching objective is to develop the genome equivalent of the RDP that will enable the scientific community to perform classification and diversity studies at the genome level.