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

合作研究：ABI 开发：“超越核糖体 RNA 基因：用于分析全基因组和宏基因组的社区工具”

• 批准号: 1356288
• 负责人: Konstantinos Konstantinidis
• 金额: $ 82.34万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1356288&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Development; Beyond

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.

细菌和古细菌（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、水、人类肠道还是大气中，原核生物即使不能控制，也会影响地球上所有生命维持过程，但这些微生物如何与环境相互作用并改变其环境尚不完全清楚。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，即它们代表了不可培养的大多数，因此无法有效地研究。在过去的几年中，独立于培养物的基因组技术（又名宏基因组学）出现了爆炸式增长，该技术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其自然栖息地中的微生物及其群落，从而绕过了实验室培养的需要。然而，用于分析宏基因组数据的计算工具和算法的发展落后于测序技术的发展。为了增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）开发满足关键研究需求的新计算工具，从而帮助科学家了解微生物群落的组成、功能和价值，2）对包括社区学院在内的本科院校的教师进行新的宏基因组学技术培训，这些技术定位于微生物学、基因组学、生物信息学和计算生物学的交叉领域，这是当代研究的关键领域 以及传统课程中未充分涵盖的教育。因此，这些活动预计将为培训未来劳动力和促进当代研究提供重要的基础设施。在过去的二十年里，小亚基核糖体 RNA 基因 (SSU rRNA) 已成功用于编目和研究微生物的多样性。这项工作得到了核糖体数据库项目（RDP；http://rdp.cme.msu.edu）等专用资源（数据库和工具存储库）的开发的促进。然而，基于 rRNA 基因的研究具有重要的局限性，而基于基因组序列的技术却没有。例如，基因组技术可以在 SSU rRNA 基因分辨率不足的水平（即物种和更精细的水平）更好地解析微生物群落，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耐药性的出现相关。该项目旨在开发工具来克服基于 rRNA 基因的方法的一些局限性，并允许有效分析微生物组。将提供两种广为接受的现有方法的稳健实施，例如用于描述密切相关的物种和菌株的基因组聚合平均核苷酸同一性（gANI），以及更新的方法，包括最近开发的用于估计通过宏基因组数据集获得的微生物群落覆盖度的Nonpareil方法，以及用于检查微生物谱系之间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水平上进行分类和多样性研究。