Title |
Generation and Analysis of a Mouse Intestinal Metatranscriptome through Illumina Based RNA-Sequencing
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Published in |
PLOS ONE, April 2012
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DOI | 10.1371/journal.pone.0036009 |
Pubmed ID | |
Authors |
Xuejian Xiong, Daniel N. Frank, Charles E. Robertson, Stacy S. Hung, Janet Markle, Angelo J. Canty, Kathy D. McCoy, Andrew J. Macpherson, Philippe Poussier, Jayne S. Danska, John Parkinson |
Abstract |
With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics. |
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Professor > Associate Professor | 17 | 7% |
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