Graph-centric approaches to metatranscriptomic and metaproteomic data analysis

以图为中心的宏转录组和宏蛋白质组数据分析方法

• 批准号: 8760378
• 负责人: Yuzhen Ye
• 金额: $ 34.04万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8760378
• 关键词: Adopted; Algorithms; Antisense RNA; Bacteria; Bioinformatics; Characteristics; Clustered Regularly Interspaced Short Palindromic Repeats; Collaborations; Communities; Complex; Computing Methodologies; Data; Data Analyses; Data Set; Elements; European; Gene Proteins; Genes; Genetic; Genetic Materials; Genome; Genomics; Graph; Health; Human; Human Microbiome; Immune system; Individual; Integrons; International; Maps; Mass Spectrum Analysis; Memory; Meta-Analysis; Metabolic Pathway; Metagenomics; Methods; Mining; Nature; Operon; Peptides; Phase; Property; Proteins; Proteomics; Reading; Regulation; Research Personnel; Sampling; Simulate; Software Tools; Structure; System; Techniques; United States National Institutes of Health; Variant; Virus; Work; computerized tools; design; environmental change; human subject; insight; member; metagenome; metagenomic sequencing; microbial community; microbiome; novel; novel strategies; protein aminoacid sequence; public health relevance; response; scaffold; tool; transcriptomics; working group

DESCRIPTION (provided by applicant): While metagenomics can reveal genetic composition (and therefore the genetic potential) of microbial communities, other meta-omic (e.g., metatranscriptomic and metaproteomic) techniques can provide additional insights on functional characteristics of the communities, such as gene activities and their regulation mechanisms. Analyzing these functional microbiome data sets raises new computational challenges. In this application, we propose novel approaches to metatranscriptomic and metaproteomic data analyses, using de Bruijn graph representations of metagenome assemblies as the reference, enabling an integrated analysis of meta-omic data sets. The advantages of using de Bruijn graphs include: 1) they provide a compact representation of metagenomes (which are likely to be redundant) and allow direct computation on the graph, 2) they naturally capture genomic variations; and 3) they capture the "ambiguous" connectivity between contigs/scaffolds, which can be resolved in subsequent steps using additional information, or utilized to achieve better identification and quantification of genes and proteins using metatranscriptomic and metaproteomic data, respectively. We will apply our new tools to analyzing functional human microbiome data sets, including those to be generated from HMP phase II projects.

描述（由申请人提供）：虽然宏基因组学可以揭示微生物群落的遗传组成（并因此揭示遗传潜力），但其他元基因组学（例如，元转录组学和元蛋白质组学）技术可以提供关于群落的功能特征的额外见解，例如基因活性及其调节机制。分析这些功能性微生物组数据集提出了新的计算挑战。在这个应用中，我们提出了新的方法来元转录组和元蛋白质组数据分析，使用de Bruijn图表示的宏基因组组件作为参考，使元组数据集的综合分析。使用de Bruijn图的优点包括：1）它们提供了宏基因组的紧凑表示（这可能是冗余的）并允许在图上直接计算，2）它们自然地捕获基因组变异;和3）它们捕获叠连群/支架之间的“模糊”连接性，这可以在随后的步骤中使用附加信息来解析，或用于分别使用元转录组学和元蛋白质组学数据实现基因和蛋白质的更好鉴定和定量。我们将应用我们的新工具来分析功能性人类微生物组数据集，包括HMP II期项目生成的数据集。