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

基本信息

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
项目类别：
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

项目摘要

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（以及遗传潜力），但其他荟萃分析（例如，元文字和元蛋白质组学）技术可以提供有关基因活动及其调节机构等社区功能特征的其他洞察力。分析这些功能性微生物组数据集会引起新的计算挑战。在此应用程序中，我们使用元素组组件的de bruijn图表示作为参考，提出了用于元文字组和元蛋白质组数据分析的新方法，从而实现了元数据集的综合分析。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（可能是冗余的），并允许在图上进行直接计算，2）它们自然捕获基因组变异； 3）他们捕获重叠群/支架之间的“模棱两可”的连接性，可以使用其他信息在随后的步骤中解决，或者使用元文字和元蛋白质组数据分别用于更好地识别和定量基因和蛋白质。我们将应用新工具来分析功能性人体微生物组数据集，包括由HMP II期项目生成的功能。