High Performance Validation and Classification of Metagenomic Ribosomal-RNA Seque

描述(由申请人提供)：元基因组核糖体-RNA序列的高效验证和分类。独立于培养的环境DNA序列研究的创新(即元基因组学)，加上快速发展的DNA测序能力，已经深刻地改变了一项研究中可以处理的序列数据量。然而，随着生产规模的扩大和结果的推广，元基因组数据分析的几个瓶颈必须克服。这些措施包括检测和剔除人类和嵌合序列；消除/纠正测序错误；准确评估生物多样性；对序列进行准确的分类；以及分析元基因组标本中的微生物真核生物。我们的总体目标是建立一个评估和确保初级序列数据和相关系统发育元数据质量的框架。由于基于rRNA的系统发育分析仍然是组织和解释其他元基因组序列分析的基本手段，在这个拟议的项目中，我们将重点放在与rRNA序列数据相关的质量保证问题上。具体地说，我们建议建立一个基于高精度比对工具(INFNAL)的软件基础设施，该工具可以解决元基因组研究计划面临的许多关键障碍。严格的rRNA序列比对是对元基因组样本中的微生物进行准确的基于序列的系统发育分类的严格要求。由Sean Eddy教授(Co-Investigator)和他的同事开发的开源地狱比对软件允许进行远远超出其他广泛使用的自动序列比对的分析水平。这项基础技术是为了结合Rfam数据库识别和注释基因组中的RNA基因而开发的，它提供了开发和纳入可显著减少当前元基因组分析障碍的特征的机会。Inneal使用一致的RNA一级和二级结构(协方差模型；CM)来指导比对。对比对不确定性的特定位置测量的计算允许检测比对较差的序列和比对位置，这些序列和比对位置可以在下游应用之前被移除，例如系统发育推断。因此，基于地狱的CM比对可以用作从数据集中检测和消除异常序列(例如，嵌合体、非rRNA序列)和测序错误的敏感机制。在这个为期两年的项目中，我们提出了一个杠杆计划，通过PACE和Eddy小组的联合开发，使地狱技术的效用适应元基因组学社区的需求。在这项提议中，涡流实验室(由HHMI全额资助)将继续开发INFERNAL的核心技术和功能增强，而PACE实验室(由该赠款资助)将利用其在rRNA系统发育分析方面的广泛背景来构建和验证软件工具，以扩展INFERNAL的基本特征集，并特别强调促进在人类微生物组项目中进行的研究。1 公共卫生相关性：独立于培养的微生物学(即元基因组学)的创新现在可以对复杂的微生物种群进行详细分析，例如那些有助于人类健康和福祉的微生物种群。快速发展的DNA测序能力已经深刻地改变了一项研究中可以处理的序列数据量。然而，随着研究规模的扩大，这些DNA序列数据的分析必须克服几个瓶颈。这些问题包括与初级DNA序列数据的质量保证以及对从这些数据得出的结果的解释有关的几个问题，例如，仅根据DNA序列识别标本中微生物的准确性。在这个项目中，我们建议建立一个基于高精度DNA序列分析工具(INFNAL)的软件基础设施，该工具解决了目前元基因组学领域的研究人员面临的许多关键进展障碍。在这个为期两年的项目中，由Eddy教授(Co-Investigator)及其同事开发的用于识别和注释基因组中的RNA基因的基础软件技术将通过PACE和Eddy小组的联合开发来适应元基因组学社区的需求。这个研究团队将利用他们在RNA结构生物学、分子进化和计算生物学方面的广泛背景来构建和验证软件工具，以扩展INFERNAL的基本功能集，并特别强调促进在NIH人类微生物组项目中进行的研究。1

DESCRIPTION (provided by applicant): High-Performance Validation and Classification of Metagenomic Ribosomal-RNA Sequences. Innovations in culture-independent studies of environmental DNA sequences (i.e., metagenomics), coupled with rapidly advancing DNA sequencing capabilities, have altered profoundly the volume of sequence data that can be processed in a study. However several bottlenecks to metagenomic data analysis must be overcome as production is scaled up and findings are generalized. These include detection and culling of human and chimeric sequences; removal/correction of sequencing errors; accurate assessment of biodiversity; accurate taxonomic classification of sequences; and analysis of microbial eukaryotes in metagenomic specimens. Our overall objective is to build a framework for evaluating and insuring the quality of primary sequence data and associated phylogenetic metadata. Because rRNA-based phylogenetic analysis remains an essential means of organizing and interpreting the analyses of other metagenomic sequences, we focus in this proposed project on quality assurance issues related to rRNA sequence data. Specifically, we propose to build a software infrastructure based on a high-precision alignment tool (INFERNAL) that addresses many of the critical barriers to progress facing metagenomic research programs. Rigorous rRNA sequence alignment is a strict requirement for accurate sequence-based phylogenetic classification of microorganisms in metagenomic samples. The open-source INFERNAL alignment software developed by Prof. Sean Eddy (Co-Investigator) and colleagues permits a level of analysis that extends far beyond other widely-used automated sequence aligners. This base technology, developed to identify and annotate RNA genes in genomes in conjunction with the Rfam database, offers opportunity to develop and incorporate features that could significantly reduce current barriers to metagenomic analysis. INFERNAL uses consensus RNA primary and secondary structure (a covariance model; CM) to guide alignment. Calculation of position-specific measures of alignment uncertainty allows detection of poorly aligned sequences and alignment positions, which can be removed prior to downstream applications, for example phylogenetic inference. INFERNAL-based CM alignment can be used, therefore, as a sensitive mechanism for detecting and eliminating anomalous sequences (e.g., chimeras, non-rRNA sequences) and sequencing errors from datasets. In this two-year project, we propose a leveraged scheme in which the utility of the INFERNAL technology is adapted to the needs of the metagenomics community through joint development by the Pace and Eddy groups. In this proposal the Eddy lab (fully funded by HHMI) will continue to develop the core technology and functionality enhancements of INFERNAL, while the Pace lab (as funded by this grant) will use their extensive background in rRNA phylogenetic analyses to build and validate software tools that extend the basic feature set of INFERNAL, with special emphasis on facilitating research carried out in the Human Microbiome Project. 1 PUBLIC HEALTH RELEVANCE: Innovations in culture-independent microbiology (i.e., metagenomics) now permit detailed analyses of complex microbial populations, such as those that contribute to the health and well-being of humans. Rapidly advancing DNA sequencing capabilities have altered profoundly the volume of sequence data that can be processed in a study. However several bottlenecks to the analysis of this DNA sequence data must be overcome as the scale of studies expands. These include several issues concerned with the quality assurance of primary DNA sequence data, as well as interpretation of results drawn from these data, for instance the accuracy of identifying microorganisms in a specimen based solely on DNA sequence. In this project, we propose to build a software infrastructure based on a high-precision DNA sequence analysis tool (INFERNAL), that addresses many of the critical barriers to progress currently facing researchers in the metagenomics field. In this two-year project, the base software technology, developed by Prof. Eddy (Co-Investigator) and colleagues to identify and annotate RNA genes in genomes, will be adapted to the needs of the metagenomics community through joint development by the Pace and Eddy groups. This research team will use their extensive backgrounds in RNA structural biology, molecular-evolution, and computational biology to build and validate software tools that extend the basic feature set of INFERNAL, with special emphasis on facilitating research carried out in the NIH Human Microbiome Project. 1