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: 8149989
• 负责人: Daniel N Frank
• 金额: $ 22.88万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 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 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8149989
• 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; 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; 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; web services

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序列的高效验证和分类。 环境DNA序列的非文化独立研究的创新（即，宏基因组学），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的量。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。这些包括人类和嵌合序列的检测和剔除;测序错误的去除/校正;生物多样性的准确评估;序列的准确分类;以及宏基因组标本中微生物真核生物的分析。我们的总体目标是建立一个评估和确保一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宏基因组序列分析的重要手段，我们在这个拟议的项目中专注于与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序列比对是宏基因组样品中微生物精确的基于序列的系统发育分类的严格要求。由Sean Eddy教授（共同研究者）及其同事开发的开源INFERNAL比对软件允许远超出其他广泛使用的自动序列比对器的分析水平。开发这种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，它提供了开发和整合功能的机会，可以显着减少当前宏基因组分析的障碍。INFERNAL使用共有RNA一级和二级结构（协方差模型; CM）来指导比对。比对不确定性的位置特异性测量的计算允许检测比对不良的序列和比对位置，其可以在下游应用（例如系统发育推断）之前被去除。因此，基于INFERNAL的CM比对可以用作检测和消除异常序列（例如，嵌合体，非rRNA序列）和来自数据集的测序错误。 在这个为期两年的项目中，我们提出了一个杠杆计划，其中INFERNAL技术的实用性通过Pace和Eddy小组的联合开发来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，特别强调促进人类微生物组项目中进行的研究。1 公共卫生相关性：非培养微生物学的创新（即，宏基因组学（metagenomics）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。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，随着研究规模的扩大，必须克服分析这种DNA序列数据的几个瓶颈。这些问题包括与主要DNA序列数据的质量保证有关的几个问题，以及对从这些数据中得出的结果的解释，例如，仅根据DNA序列鉴定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（INFERNAL）的软件基础设施，解决宏基因组学领域研究人员目前面临的许多关键障碍。 在这个为期两年的项目中，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社区的需求。该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，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进行的研究。1

项目成果

Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing.

• DOI: 10.1371/journal.pone.0036009
• 发表时间: 2012
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Xiong X;Frank DN;Robertson CE;Hung SS;Markle J;Canty AJ;McCoy KD;Macpherson AJ;Poussier P;Danska JS;Parkinson J
• 通讯作者: Parkinson J

The microbiome of the middle meatus in healthy adults.

• DOI: 10.1371/journal.pone.0085507
• 发表时间: 2013
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Ramakrishnan VR;Feazel LM;Gitomer SA;Ir D;Robertson CE;Frank DN
• 通讯作者: Frank DN