A Desktop Assembly and Analysis Pipeline for Next-gen Metagenomic Sequencing

描述（由申请人提供）：在过去十年中，细菌和古菌群落已被确定在几乎每一个生态位检查，从酸性矿井排水对流层云到人体。了解这些群落的组成和相互作用对于了解每个生态系统的功能至关重要。宏基因组学（或社区基因组学）是一种方法，通过该方法可以确定天然存在的微生物群落的集体基因组内容，而无需独立分离和培养其组分。当组装，遗传框架的社区，包括人口结构，系统发育多样性，以及新的遗传和生物化学活动的关键信息可以获得。这些发现对生物技术、医学和生态学的潜在影响是巨大的。 最近，下一代测序技术（例如Roche/454、Illumina和Life Technologies（SOLiD））已经取代传统的桑格测序用于宏基因组数据生成。这些都是具有成本效益的，无克隆的，大规模并行的技术，能够在一台机器运行中产生多达250千兆字节的数据。这种水平的测序使得重建甚至低丰度的基因组以及从群落样本确定种内异质性成为可能。然而，下一代技术也提出了自己的计算挑战，包括要处理的大量数据以及每种技术特有的不同读取长度、错误模型和格式。这些复杂性加上社区本身的复杂性，使得研究人员不得不拼凑各种软件工具的组合来处理和分析他们的数据。大多数能够处理这些大型复杂数据集的软件还需要大量的计算资源和计算机专业知识，超出了通常配备的实验室。这些困难继续严重阻碍宏基因组学所带来的科学和技术进步。 该提案的长期目标是开发一个无缝的商业级宏基因组序列组装和分析管道，该管道完全可扩展到任何规模的项目。拟议中的软件将易于使用，并在成本低于5000美元的台式计算机上运行，以便任何资金合理的实验室或诊所都可以利用宏基因组技术。为了实现这一目标，这个第一阶段的建议集中在一个解决方案的中心任务，处理大量的下一代宏基因组数据集在台式计算机上。我们将评估我们新的非内存绑定汇编引擎XNG是否可以通过三个关键步骤来应对挑战：2）基于与本地参考基因组数据库的匹配，将潜在的数亿剩余读段“募集”到适当的系统发育箱中，和3）将来自给定物种的多个菌株的基因组序列转化为单个注释条目（“泛基因组”），以增强读段募集和下游注释。 公共卫生相关性：人类健康和医学受到微生物群落的极大影响，这些微生物群落是我们身体的组成部分，也是产生有用抗生素的微生物群落。为了更好地利用这些社区的潜力，宏基因组学研究正在产生大量的下一代DNA序列数据，以破译它们的集体遗传内容。该项目的重点是开发能够重建微生物群落基因组并分析其内容的计算机软件。

DESCRIPTION (provided by applicant): Over the last decade, bacterial and archaeal communities have been identified in virtually every ecological niche examined, from acid mine drainage to tropospheric clouds to the human body. Understanding the make-up and interactions within these communities is crucial in understanding how each ecosystem functions. Metagenomics (or community genomics) is the approach whereby the collective genomic content of a naturally occurring microbial community can be determined without the need to isolate and culture its constituents independently. When assembled, the genetic framework of the community, including critical information on population structure, phylogenetic diversity, as well as novel genetic and biochemical activities can be obtained. The potential impact of such findings on biotechnology, medicine and ecology are enormous. Recently, next-gen sequencing technologies (e.g. Roche/454, Illumina, and Life Technologies (SOLiD)) have replaced traditional Sanger sequencing for metagenomic data generation. These are cost effective, clone-free, massively parallel technologies capable of producing as many as 250 gigabases of data in a single machine run. That level of sequencing makes it feasible to reconstruct even low abundance genomes as well as determine intraspecies heterogeneity from a community sample. However, the next-gen technologies also present their own computational challenges including the sheer volume of data to be processed together with the different read lengths, error models and formats unique to each technology. These complexities together with those posed the communities themselves together has left researchers to cobble together various combinations of software tools to process and analyze their data. Most software that can handle these large, complex data sets also require substantial computing resources and computer expertise beyond that of a normally equipped lab. These difficulties continue to have a serious stifling effect on the advances in science and technology that metagenomics offers. The long term goal of this proposal is to develop a seamless, commercial-grade metagenomic sequence assembly and analysis pipeline that is fully scalable to any size project. The proposed software will be easy to use and run on a desktop computer costing less than $5000 so that any reasonably funded laboratory or clinic can exploit metagenomic technology. Toward that goal, this Phase I proposal focuses on a solution to the central task of processing massive next-gen metagenomic data sets on a desktop computer. We will evaluate whether our new non-memory bound assembly engine, XNG, can meet the challenges in three crucial steps: 1) removing reads derived from contaminating host DNA, 2) "recruiting" the potentially hundreds of millions remaining reads into appropriate phylogenetic bins based on matches to a local reference genome database, and 3) converting genome sequences from multiple strains of a given species into a single annotated entry (the "pan-genome") for enhanced read recruitment and downstream annotation. PUBLIC HEALTH RELEVANCE: Human health and medicine are greatly influenced by the microbial communities that are integral parts of our bodies as well as those that produce useful antibiotics for example. To better exploit the potential of these communities, metagenomic studies are producing vast amounts of Next-gen DNA sequence data to decipher their collective genetic content. This project focuses on developing computer software capable of reconstructing microbial community genomes and analyzing their content.