Fragment assembly and metabolic/species diversity analysis for Human microbiome p

描述（由申请人提供）：人类微生物组为宿主人类提供必要和互补的遗传和代谢成分。直到最近，微生物学家主要研究单个可培养的微生物物种，尽管绝大多数（约95%-98%）微生物不能在纯培养中生存。由于DNA测序技术的快速发展，宏基因组学试图直接确定环境样本中的整个基因集合。为了在全球水平上研究人类微生物组，宏基因组学成为人类微生物组计划（HMP）的首选方法。我们建议开发计算方法来解决HMP宏基因组分析的几个挑战，即从焦磷酸测序中获得短读段的组装，通过数据库搜索对蛋白质编码基因的功能注释，以及样品中生物多样性的表征。我们从一种新的方法开始组装来自宏基因组的短reads，称为ORFome Assembly，通过将来自同一家族的同源蛋白质的假定orf组装成蛋白质家族图（欧拉路径方法）。然后，我们提出了一种使用蛋白质家族图作为查询的相似性搜索的网络匹配方法。我们预计，使用蛋白质家族图将导致数据库搜索具有更高的灵敏度和特异性，而不是简单地使用未组装的测序reads。最后，我们建议开发计算工具来同时评估样本中的生物多样性和生物功能，通过基于相似性搜索结果识别涵盖宏基因组数据中注释基因功能的最可能的连贯通路变异集。这些软件工具将使研究人员能够高效和有效地分析来自HMP的数据，这将增强对人类微生物群（即生活在人体表面和体内的微生物）与人类疾病之间关系的理解，并加速开发更好或新的治疗方法。公共卫生相关性：我们建议开发计算方法来解决人类微生物组计划（HMP）数据宏基因组分析的几个挑战。这些软件工具将使研究人员能够高效和有效地分析来自HMP的数据，这将增强对人类微生物群与人类疾病之间关系的理解，并加速开发更好或新的治疗方法。

DESCRIPTION (provided by applicant): The human microbiome contributes essential and complementary genetic and metabolic components to the host human. Until recently, microbiologists mainly studied individual culturable species of microbes, even though a vast majority (approximately 95%-98%) of microorganisms cannot live in pure culture. Facilitated by the rapid advancement of the DNA sequencing techniques, metagenomics attempts to directly determine the whole collection of genes within an environmental sample. To study the human microbiome at a global level, metagenomics becomes the methodology of choice for the Human Microbiome Project (HMP). We propose to develop computational methods addressing several challenges to the metagenomic analysis in HMP, namely, the assembly of short reads from pyrosequencing, the functional annotation of protein coding genes through database searching, and the characterization of the biodiversity in samples. We start with a novel approach to assembling short reads from metagenomics, called ORFome Assembly, by assembling putative ORFs from homologous proteins in the same family into a protein family graph (an Eulerian path approach). We then propose a network matching approach for the similarity search using the protein family graphs as queries. We anticipate that using protein family graphs will result in database searching with higher sensitivity and specificity than simply using unassembled sequencing reads. Finally, we propose to develop computational tools to simultaneously assess the biodiversity and biological functions in samples, by identifying the most likely set of coherent pathway variants covering the annotated gene functions within the metagenomic data based on the similarity search results. These software tools will enable researchers to efficiently and effectively analyze the data from HMP, which will enhance the understanding of the relationship between the human microbiota (i.e., the microbes living on the surface and inside human body) and human diseases, and hasten the development of better or new therapies. PUBLIC HEALTH RELEVANCE: We propose to develop computational methods addressing several challenges to the metagenomic analysis of human microbiome project (HMP) data. These software tools will enable researchers to efficiently and effectively analyze the data from HMP, which will enhance the understanding of the relationship between the human microbiota and human diseases, and hasten the development of better or new therapies.