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

描述(申请人提供)：在过去十年中，从酸性矿山排水到对流层云到人体，几乎在每个被检查的生态位中都发现了细菌和古生菌群落。了解这些群落的组成和相互作用对于了解每个生态系统如何发挥作用至关重要。元基因组学(或群落基因组学)是这样一种方法，在这种方法中，可以确定自然产生的微生物群落的集体基因组含量，而不需要单独分离和培养其成分。当组合起来时，可以获得群落的遗传框架，包括关于种群结构、系统发育多样性以及新的遗传和生化活动的关键信息。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))已经取代了传统的Sanger测序用于元基因组数据的生成。这些是具有成本效益的、无克隆的大规模并行技术，能够在一次机器运行中产生多达250 GB的数据。这种水平的测序使得重建即使是低丰度的基因组以及从群落样本中确定种内异质性也是可行的。然而，下一代技术也带来了自己的计算挑战，包括要处理的数据量巨大，以及每种技术独特的不同读取长度、错误模型和格式。这些复杂性，再加上社区本身的复杂性，使得研究人员不得不拼凑出各种软件工具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，而不是通常配备的实验室。这些困难继续对元基因组学提供的科学和技术进步产生严重的抑制作用。这项提议的长期目标是开发一种无缝的、商业级的元基因组序列组装和分析流水线，该流水线完全可以扩展到任何规模的项目。拟议的软件将易于使用，并在一台成本不到5000美元的台式计算机上运行，这样任何合理资助的实验室或诊所都可以利用元基因组技术。为了实现这一目标，这份第一阶段提案的重点是解决在台式计算机上处理大量下一代元基因组数据集这一中心任务。我们将评估我们的新的非内存绑定组装引擎XNG是否能够在三个关键步骤中应对挑战：1)移除来自污染宿主DNA的读取，2)根据与本地参考基因组数据库的匹配将潜在的数亿剩余读取“招募”到适当的系统发育库中，以及3)将给定物种的多个菌株的基因组序列转换为单个注释条目(“泛基因组”)，以增强读取招募和下游注释。 与公共健康相关：人类健康和医学很大程度上受到微生物群落的影响，微生物群落是我们身体的组成部分，例如产生有用抗生素的微生物群落。为了更好地开发这些群落的潜力，元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.