High Performance Validation and Classification of Metagenomic Ribosomal-RNA Seque

描述（由申请人提供）：宏基因组核糖体rna序列的高效验证和分类。环境DNA序列的培养独立研究（即宏基因组学）的创新，加上快速发展的DNA测序能力，已经深刻地改变了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研究结果的推广，必须克服宏基因组数据分析的几个瓶颈。这些措施包括检测和剔除人类和嵌合序列；去除/纠正测序错误；准确评估生物多样性；序列的精确分类；以及宏基因组标本中真核微生物的分析。我们的总体目标是建立一个框架来评估和确保初级序列数据和相关系统发育元数据的质量。由于基于rRNA的系统发育分析仍然是组织和解释其他宏基因组序列分析的重要手段，因此我们在本项目中重点关注与rRNA序列数据相关的质量保证问题。具体来说，我们建议建立一个基于高精度比对工具（INFERNAL）的软件基础设施，以解决宏基因组研究计划面临的许多关键障碍。严格的rRNA序列比对是对宏基因组样品中微生物进行精确的基于序列的系统发育分类的严格要求。由Sean Eddy教授（合作研究者）及其同事开发的开源INFERNAL比对软件允许的分析水平远远超出其他广泛使用的自动序列比对器。该基础技术与Rfam数据库一起开发，用于识别和注释基因组中的RNA基因，为开发和整合可以显著减少当前宏基因组分析障碍的功能提供了机会。INFERNAL使用共识RNA一级和二级结构（协方差模型；CM）来指导对齐。通过计算特定位置的比对不确定性，可以检测到比对不佳的序列和比对位置，这些可以在下游应用（例如系统发育推断）之前删除。因此，基于内部的CM比对可以用作检测和消除数据集中异常序列（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INFERNAL技术的效用适应宏基因组学社区的需求。在该提案中，Eddy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（由该拨款资助）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扩展INFERNAL基本功能集的软件工具，特别强调促进在人类微生物组项目中进行的研究

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