High Performance Validation and Classification of Metagenomic Ribosomal-RNA Seque

宏基因组核糖体-RNA 序列的高性能验证和分类

• 批准号: 8021062
• 负责人: Daniel N Frank
• 金额: $ 22.73万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-27 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8021062
• 关键词: Address; Archaea; Bacteria; Base Sequence; Biodiversity; Chimera organism; Classification; Communities; Complex; Computational Biology; Computer software; Consensus; Coupled; DNA Sequence; DNA Sequence Analysis; Data; Data Analyses; Data Set; Databases; Defect; Detection; Development; Discrimination; Eukaryota; Excision; Funding; Genes; Genome; Grant; Health; Human; Human Microbiome; Internet; Joints; Laboratories; Measures; Metadata; Metagenomics; Microbe; Microbiology; Modeling; Molecular Evolution; Performance; Personal Satisfaction; Philosophy; Phylogenetic Analysis; Population; Positioning Attribute; Process; Production; RNA; RNA Sequences; Reliance; Research; Research Infrastructure; Research Personnel; Ribosomal RNA; Sampling; Scheme; Sequence Alignment; Sequence Analysis; Services; Software Tools; Source Code; Specimen; Statistical Models; Structure; Taxonomy; Technology; Trees; Uncertainty; United States National Institutes of Health; Validation; Work; base; improved; innovation; metagenomic sequencing; microbial; microorganism; microorganism classification; next generation; open source; programs; public health relevance; quality assurance; scale up; software development; structural biology; tool

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

描述（由申请人提供）：宏基因组核糖体-RNA 序列的高性能验证和分类。 环境 DNA 序列（即宏基因组学）的独立于培养物的研究的创新，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。其中包括人类和嵌合序列的检测和剔除；去除/纠正测序错误；准确评估生物多样性；序列的准确分类；以及宏基因组样本中微生物真核生物的分析。我们的总体目标是建立一个框架来评估和确保一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，因此我们在这个拟议项目中重点关注与 rRNA 序列数据相关的质量保证问题。具体来说，我们建议构建一个基于高精度比对工具（INFRNAL）的软件基础设施，以解决宏基因组研究项目面临的许多关键障碍。 严格的 rRNA 序列比对是对宏基因组样本中微生物进行准确的基于序列的系统发育分类的严格要求。 Sean Eddy 教授（联合研究员）及其同事开发的开源 INFRNAL 比对软件可实现远远超出其他广泛使用的自动序列比对仪的分析水平。这项基础技术旨在与 Rfam 数据库结合识别和注释基因组中的 RNA 基因，为开发和整合可显着减少当前宏基因组分析障碍的功能提供了机会。 INFRNAL 使用共有的 RNA 一级和二级结构（协方差模型；CM）来指导比对。计算比对不确定性的位置特异性测量可以检测比对不良的序列和比对位置，可以在下游应用（例如系统发育推断）之前将其删除。因此，基于 INFRNAL 的 CM 比对可用作检测和消除数据集中的异常序列（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方案，通过 Pace 和 Eddy 小组的联合开发，INFRNAL 技术的实用性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（由本次拨款资助）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 INFRNAL 基本功能集的软件工具，特别强调促进人类微生物组项目中开展的研究。 1 公共卫生相关性：独立于培养物的微生物学（即宏基因组学）的创新现在允许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。 DNA 测序能力的快速发展极大地改变了研究中可处理的序列数据量。然而，随着研究规模的扩大，必须克服 DNA 序列数据分析的几个瓶颈。其中包括与原始 DNA 序列数据的质量保证以及对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 序列识别样本中微生物的准确性。在这个项目中，我们建议构建一个基于高精度 DNA 序列分析工具 (INFRNAL) 的软件基础设施，以解决宏基因组学领域研究人员目前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（联合研究员）及其同事开发的用于识别和注释基因组中 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宏基因组学界的需求。该研究团队将利用其在 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扩展 INFERNAL 基本功能集的软件工具，特别强调促进 NIH 人类微生物组项目中开展的研究。 1