Generation and Analysis of a Mouse Intestinal Metatranscriptome through Illumina Based RNA-Sequencing
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{"title"=>"Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing", "type"=>"journal", "authors"=>[{"first_name"=>"Xuejian", "last_name"=>"Xiong", "scopus_author_id"=>"35239405300"}, {"first_name"=>"Daniel N.", "last_name"=>"Frank", "scopus_author_id"=>"7201798042"}, {"first_name"=>"Charles E.", "last_name"=>"Robertson", "scopus_author_id"=>"15731757800"}, {"first_name"=>"Stacy S.", "last_name"=>"Hung", "scopus_author_id"=>"36166744700"}, {"first_name"=>"Janet", "last_name"=>"Markle", "scopus_author_id"=>"55203196000"}, {"first_name"=>"Angelo J.", "last_name"=>"Canty", "scopus_author_id"=>"8643311700"}, {"first_name"=>"Kathy D.", "last_name"=>"McCoy", "scopus_author_id"=>"55203183600"}, {"first_name"=>"Andrew J.", "last_name"=>"Macpherson", "scopus_author_id"=>"7101638494"}, {"first_name"=>"Philippe", "last_name"=>"Poussier", "scopus_author_id"=>"7003421490"}, {"first_name"=>"Jayne S.", "last_name"=>"Danska", "scopus_author_id"=>"6603814356"}, {"first_name"=>"John", "last_name"=>"Parkinson", "scopus_author_id"=>"18335998200"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"364720872", "sgr"=>"84860477454", "issn"=>"19326203", "pmid"=>"22558305", "scopus"=>"2-s2.0-84860477454", "doi"=>"10.1371/journal.pone.0036009", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)"}, "id"=>"c6125cb5-911b-3369-968f-bf57d8231346", "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.", "link"=>"http://www.mendeley.com/research/generation-analysis-mouse-intestinal-metatranscriptome-through-illumina-based-rnasequencing", "reader_count"=>204, "reader_count_by_academic_status"=>{"Unspecified"=>6, "Professor > Associate Professor"=>11, "Librarian"=>1, "Researcher"=>63, "Student > Doctoral Student"=>10, "Student > Ph. D. 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Student"=>50, "Student > Postgraduate"=>8, "Other"=>7, "Student > Master"=>25, "Student > Bachelor"=>15, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>6}, "reader_count_by_subject_area"=>{"Unspecified"=>11, "Environmental Science"=>7, "Biochemistry, Genetics and Molecular Biology"=>16, "Agricultural and Biological Sciences"=>150, "Medicine and Dentistry"=>3, "Arts and Humanities"=>1, "Physics and Astronomy"=>1, "Computer Science"=>8, "Immunology and Microbiology"=>6, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>6}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>150}, "Computer Science"=>{"Computer Science"=>8}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>16}, "Unspecified"=>{"Unspecified"=>11}, "Environmental Science"=>{"Environmental Science"=>7}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"United States"=>10, "Japan"=>3, "Malaysia"=>1, "Sweden"=>1, "Belgium"=>1, "Norway"=>1, "Taiwan"=>1, "Finland"=>1, "Brazil"=>4, "Mexico"=>2, "Italy"=>1, "France"=>3, "Australia"=>1, "Germany"=>2}, "group_count"=>4}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/646375"], "description"=>"<p>(A) Spearman correlation coefficients of abundance of reads mapping to <i>E. coli</i> homologs across samples. (B)–(D) Comparison between conservation, as defined by number of bacterial genomes in which a putative ortholog has been identified and relative expression of <i>E. coli</i> homologs for three selected subsystems derived from the transcriptome data for a single sample (NOD503CecMN). Subsystems are defined from a previously generated high quality protein-protein interaction network <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036009#pone.0036009-PeregrinAlvarez3\" target=\"_blank\">[53]</a>. Colours of nodes indicate genes involved in common functional modules. Size of nodes indicate either number of genomes or relative expression in terms of RPKM (largest = >900 genomes/transcripts). (B) Proteins involved in cell division and cell wall biogenesis. (C) Proteins involved in iron transport and tryptophan metabolism. (D) Proteins involved in select transport pathways - note of the ones shown, despite many being highly conserved, only components involved in spermidine transport (PotA-D) are abundant within our sample.</p>", "links"=>[], "tags"=>["mapped"], "article_id"=>316858, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Microbiology"], "users"=>["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"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036009.g005", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Metatranscriptome_data_mapped_in_the_context_of_a_global_E_coli_protein_interaction_network_/316858", "title"=>"Metatranscriptome data mapped in the context of a global <i>E. coli</i> protein interaction network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-27 01:54:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/646260"], "description"=>"<p>For each enzyme in the network, betweenness centrality was calculated and mapped to node colour. Between the two samples we identify differences in the expression of nodes of high betweenness, suggesting an altered reliance on pathway flux within the network.</p>", "links"=>[], "tags"=>["differences", "enzymes", "betweenness"], "article_id"=>316744, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Microbiology"], "users"=>["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"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036009.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Samples_display_subtle_differences_in_expression_of_enzymes_of_high_betweenness_centrality_/316744", "title"=>"Samples display subtle differences in expression of enzymes of high betweenness centrality.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-27 01:52:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/646523"], "description"=>"<p>The 236,769 bacterial transcripts found to match reads were clustered into 13,278 gene families. The table indicates the relative abundance of each gene family as measured by summing RPKM values for each transcript in that family. Shown are the top 20 most abundant families in the NOD503CecMN sample together with the rank of abundance across all 12 samples. ‘Descriptions’ represent annotations associated with the transcripts according to definitions provided by Genbank. ‘Predominant taxon’ indicate the most frequent taxonomic group associated with the transcripts in the gene family.</p>", "links"=>[], "tags"=>["abundance"], "article_id"=>317006, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Microbiology"], "users"=>["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"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036009.t003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relative_abundance_of_gene_families_/317006", "title"=>"Relative abundance of gene families.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-04-27 01:56:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/646134"], "description"=>"<p>(A) Network map highlighting metabolic enzyme expression for reads obtained from the NOD503CecMN sample. Size of node indicates relative expression. Colour of node indicates functional category of enzyme as defined by KEGG superclasses (see key for details). Several example pathways are indicated. (B) Spearman rank correlation coefficients of relative enzyme expression across the 12 samples. In general there is a high degree of consistency in enzyme expression across samples. (C) Top 100 expressed enzymes in the NOD503CecMN sample. For three subnetworks, links are apparent that extend beyond the boundaries of KEGG defined pathways.</p>", "links"=>[], "tags"=>["mapped", "metabolic"], "article_id"=>316623, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Microbiology"], "users"=>["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"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036009.g003", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Metatranscriptome_data_mapped_in_the_context_of_a_global_metabolic_network_/316623", "title"=>"Metatranscriptome data mapped in the context of a global metabolic network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-27 01:50:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/333850", "https://ndownloader.figshare.com/files/333895"], "description"=>"<div><p>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 <em>Escherichia coli</em> 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.</p> </div>", "links"=>[], "tags"=>["intestinal", "metatranscriptome", "illumina", "based", "rna-sequencing"], "article_id"=>125934, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Microbiology"], "users"=>["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"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0036009.s001", "https://dx.doi.org/10.1371/journal.pone.0036009.s002"], "stats"=>{"downloads"=>1, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Generation_and_Analysis_of_a_Mouse_Intestinal_Metatranscriptome_through_Illumina_Based_RNA_Sequencing/125934", "title"=>"Generation and Analysis of a Mouse Intestinal Metatranscriptome through Illumina Based RNA-Sequencing", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-04-27 01:38:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/645881"], "description"=>"<p>(A) Distribution of mRNA transcripts. (B) Distribution of rRNA transcripts. Results are shown for the 12 independent samples described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036009#pone-0036009-t001\" target=\"_blank\">Table 1</a>. Consistent with 16S rRNA studies, the vast majority of identified mRNA transcripts derive from Parabacteroides, Bacteroides or Clostridial species.</p>", "links"=>[], "tags"=>["transcripts", "mapped", "bacterial"], "article_id"=>316362, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Microbiology"], "users"=>["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"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036009.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phylogenetic_distribution_of_transcripts_mapped_to_known_bacterial_genes_/316362", "title"=>"Phylogenetic distribution of transcripts mapped to known bacterial genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-27 01:46:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/646439"], "description"=>"<p>12 cecal and colon derived samples were prepared from four different NOD mice using a variety of RNA extraction protocols. Samples were multiplexed on a single Illumina sequencing run to generate 21.7 million sequences. Of these ∼1.5 million (∼10%) could be mapped to a known bacterial transcript either via BWA against bacterial genomes (nt) or via BLASTX against the protein non-redundant database (peptide). Use of the Invitrogen extraction kit resulted in the most consistent generation of a high proportion of bacterial transcripts.</p>‘*’<p>indicates the additional use of the RiboMinus kit.</p>", "links"=>[], "tags"=>["yields", "12"], "article_id"=>316920, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Microbiology"], "users"=>["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"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036009.t001", "stats"=>{"downloads"=>3, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sequence_yields_for_12_different_sample_preparations_/316920", "title"=>"Sequence yields for 12 different sample preparations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-04-27 01:55:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/646479"], "description"=>"1<p>Values are percents of total reads for sample.</p>2<p>Anatomic location of sample. Cec = Cecum contents. Col = Colon contents.</p>3<p>RNA extraction kit. Qia = Qiagen RNEasy. Inv = Invitrogen.</p>4<p>RiboMinus removal of bacterial rRNA from sample.</p>5<p>Phylum and family classifications of rRNA reads.</p>6<p>Number of Altered Schaedler Flora species belonging to bacterial family.</p>", "links"=>[], "tags"=>["small-subunit", "rna"], "article_id"=>316964, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Microbiology"], "users"=>["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"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036009.t002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Family_level_distribution_of_small_subunit_RNA_sequences_1_/316964", "title"=>"Family-level distribution of small-subunit RNA sequences<sup>1</sup>.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-04-27 01:56:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/645992"], "description"=>"<p>(A) Distribution of COG functional annotations of reads mapping to known bacterial transcripts for the 12 samples analysed in this study. Also shown is the distribution of COG assignments for all proteins in the COG database (background). Asterisk's indicate COG categories with significant (Z-score>2) differences in relative frequency between samples and the background (Z-score is indicated). (B) Pearson correlation coefficients of frequency of reads assigned to each COG category across the 12 samples.</p>", "links"=>[], "tags"=>["cog"], "article_id"=>316479, "categories"=>["Molecular Biology", "Biological Sciences", "Genetics", "Microbiology"], "users"=>["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"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036009.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_COG_functional_annotations_/316479", "title"=>"Distribution of COG functional annotations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-27 01:47:59"}

PMC Usage Stats | Further Information

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