Assembly and analysis software for exploring the human microbiome

描述（由申请人提供）：细菌是地球上最丰富的生物，但对这一生命领域的大多数成员知之甚少。只有大约1%的细菌种类可以很容易地在培养基中生长，而且已经测序的细菌种类要少得多。测序技术的进步使得直接从环境中对细菌进行测序成为可能，为我们世界上细菌的多样性提供了一个引人注目的新前景。最初的研究已经探索了存在于矿山、海水和土壤中的细菌，以及栖息在人体中的共生微生物群落。后者提供了细菌和人类宿主之间复杂的共生关系的一瞥。尽管人们对环境测序（宏基因组学）越来越感兴趣，但很少有专门的计算算法用于分析这些数据。例如，环境数据的组装正在使用最初用于同质DNA来源的软件进行，例如克隆细菌种群或近亲繁殖的真核生物。这些程序不适合异质微生物群落的组装，许多“黑客”必须产生迄今为止发表的组装。这个建议的目的是填补专门的软件组装和发现基因在宏基因组数据集的需求。一个特别的重点将是开发工具，以揭示微生物群落内的基因组变异。拟议的软件将专门解决在宏基因组项目中使用新测序技术所产生的问题。这些技术的低成本和高通量将使我们能够比以前更深入地探索微生物生物圈。然而，它们的广泛应用取决于适应其特定特性的软件系统的可用性。此外，将开发新的算法，使宏基因组分析管道的各个组成部分紧密集成，以提高组装和注释的整体质量，并促进从大型宏基因组数据集中提取其他类型的信息。该提案进一步旨在通过开发模拟细菌种群和模拟各种测序策略的软件，研究实验设计和测序技术选择对组装和分析宏基因组数据能力的影响。更好的实验设计可以降低目前与环境测序相关的高成本，并增强后续分析。作为本提案一部分开发的所有软件，以及任何模拟数据和重新分析公共数据集的结果将通过公共数据库和开源软件库免费发布。对居住在我们体内的细菌群落的初步探索已经为微生物与人类宿主之间的复杂关系以及细菌对肥胖和炎症性肠病等疾病的贡献提供了见解。需要更多的研究来帮助我们充分理解复杂的人类与微生物的相互作用，并将这些发现转化为新的治疗方法。目前的提案为科学家提供了软件基础设施的组件，这些组件将对人类微生物组的基因组研究至关重要。

DESCRIPTION (provided by applicant): Bacteria are the most abundant organisms on Earth, yet little is known about most members of this domain of life. Only about 1% of bacterial species can be easily grown in culture, and considerably fewer have been sequenced. Advances in sequencing technologies have made it possible to sequence bacteria directly from the environment, providing a dramatic new outlook on the diversity of bacteria populating our world. Initial studies have explored the bacteria present in mines, ocean water, and soil, as well as communities of commensal microbes that inhabit the human body. The latter have provided a glimpse at the complex symbiotic relationships between bacteria and their human hosts. Despite an increased interest in environmental sequencing (metagenomics), few specialized computational algorithms exist for the analysis of such data. For example, the assembly of environmental data is being performed with software originally intended for homogeneous DNA sources, such as clonal bacterial populations or inbred eukaryotes. These programs are ill-suited to the assembly of heterogeneous microbial communities and numerous "hacks" have been necessary to produce the assemblies published to date. This proposal aims to fill the need for specialized software for assembling and finding genes in metagenomic datasets. A particular focus will be on developing tools for uncovering genomic variation within the assemblies of microbial communities. The proposed software will specifically address issues arising from the use of new sequencing technologies in metagenomic projects. The low cost and high throughput of these technologies will allow a far deeper exploration of the microbial biosphere than was previously possible. Their broad application, however, depends on the availability of software systems adapted to their specific characteristics. In addition, new algorithms will be developed to allow the individual components of a metagenomic analysis pipeline to be tightly integrated, with the goal of improving the overall quality of both assembly and annotation, and to facilitate the extraction of other types of information from large sets of metagenomic data. The proposal further aims to investigate the impact of experimental design and choice of sequencing technology on the ability to assemble and analyze metagenomic data, through the development of software for simulating bacterial populations and emulating a variety sequencing strategies. Better experimental design can reduce the high costs currently associated with environmental sequencing and enhance subsequent analyses. All software developed as part of this proposal, as well as any simulated data and results of reanalyzing public datasets will be released freely through public databases and open-source software repositories. PUBLIC HEALTH RELEVANCE: Project Narrative Initial explorations of the communities of bacteria that inhabit our bodies have already provided insights into the complex relationships between microbes and the human host, as well as the contribution of bacteria to diseases such as obesity, and inflammatory bowel disease. Many more studies will be needed to help us fully understand the complex human-microbe interactions and to translate these discoveries into new therapies. The current proposal provides scientists with components of the software infrastructure that will be essential for genomic studies of the human microbiome.