ABI Innovation: Tools for reconstructing mobile genetic elements from shotgun metagenomic data

ABI Innovation：从鸟枪法宏基因组数据重建移动遗传元件的工具

基本信息

批准号：
1661338
负责人：
Ilana Brito
金额：
$ 102.96万
依托单位：
Cornell University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2021-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661338&HistoricalAwards=false
关键词：
ABI Innovation Tools reconstructing mobile

项目摘要

Bacteria may acquire novel DNA from other bacteria in their environment through the process of horizontal gene transfer (HGT). This transferred DNA often encodes new functions that expand an organism's niche, change its relationships with its host or provide a competitive edge against other organisms within its environment. For example, antibiotic resistance genes are frequently observed to be transferred between organisms. New high-throughput DNA sequencing technologies now allow researchers to simultaneously sequence data from millions of different bacterial species in a single sample, called metagenome sequencing. Thousands of publicly available metagenomic sequence datasets exist for samples from very diverse ecologies, including: gut microbiomes from humans, animals and insects, soil and root communities, surfaces in built environments, hydrothermal mats and vents, wastewater treatment plants, freshwater sources and marine ecosystems. Using these datasets, the research team will develop a suite of computational tools for examining the mobile gene content of these datasets, to identify those genes that may shape the response of microbial communities to stress. Graduate students will carry out the research under the direction of the PI, while undergraduates will be trained to use the resulting methods through a series of hackathons aimed at building the computational science community focused on microbial community research. Recruiting efforts will be aimed at measurably increasing the diversity in computational biology throughout the greater Finger Lakes region. Functional characterization of microbial communities is often performed using metagenomic shotgun data, obtained by sequencing microbial samples in their entirety. Yet, despite its importance, few methods exist to distinguish genes that are inherited versus those genes which have been mobilized and transferred between organisms in this type of short-read sequence data. By providing new computational methods for reconstructing mobile genetic elements in short-read data, researchers can tap into the huge resource of existing metagenomic data to examine the role of HGT in shaping the functions of natural microbial communities. This research will be transformative for all fields of microbiology, including clinical microbiology, microbiome research, environmental microbiology, and microbial engineering.

通过水平基因转移过程（HGT），细菌可能从其环境中的其他细菌中获取新的DNA。转移的DNA通常编码扩大生物体利基市场，改变其与宿主的关系或与其环境中其他生物体的竞争优势的新功能。例如，经常观察到抗生素抗性基因在生物之间转移。现在，新的高通量DNA测序技术使研究人员可以同时从单个样品中的数百万种不同细菌物种（称为元基因组测序）中对数据进行测序。对于来自非常多样化的生态的样品，存在成千上万的公开可用的宏基因组序列数据集，包括：来自人类，动物和昆虫的肠道微生物，土壤和根部群落，建筑环境中的表面，水热垫子和通风口，废水处理工厂，淡水来源，淡水来源和海洋生态系统。使用这些数据集，研究团队将开发一套计算工具来检查这些数据集的移动基因含量，以识别可能影响微生物群落对压力的反应的基因。研究生将在PI的指导下进行研究，而本科生将通过一系列旨在建立针对微生物社区研究的计算科学界的黑客马拉松培训，以使用由此产生的方法进行培训。招聘工作将旨在可衡量地增加整个更大的手指湖区计算生物学的多样性。通常使用元基因组shot弹枪数据进行微生物群落的功能表征，该数据通过对微生物样品进行整体测序获得。然而，尽管它很重要，但很少有方法可以区分遗传的基因与在这种短阅读序列数据中动员和转移的基因。通过提供新的计算方法来重建短阅读数据中的移动遗传因素，研究人员可以利用现有的元基因组数据的巨大资源来研究HGT在塑造天然微生物群落功能中的作用。这项研究将对所有微生物学领域进行变革，包括临床微生物学，微生物组研究，环境微生物学和微生物工程。