Comparing Apples and Oranges?: Next Generation Sequencing and Its Impact on Microbiome Analysis
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{"title"=>"Comparing apples and oranges?: Next generation sequencing and its impact on microbiome analysis", "type"=>"journal", "authors"=>[{"first_name"=>"Adam G.", "last_name"=>"Clooney", "scopus_author_id"=>"57144509200"}, {"first_name"=>"Fiona", "last_name"=>"Fouhy", "scopus_author_id"=>"37025930100"}, {"first_name"=>"Roy D.", "last_name"=>"Sleator", "scopus_author_id"=>"6602741731"}, {"first_name"=>"Aisling", "last_name"=>"O'Driscoll", "scopus_author_id"=>"55291169200"}, {"first_name"=>"Catherine", "last_name"=>"Stanton", "scopus_author_id"=>"7005945062"}, {"first_name"=>"Paul D.", "last_name"=>"Cotter", "scopus_author_id"=>"7006716039"}, {"first_name"=>"Marcus J.", "last_name"=>"Claesson", "scopus_author_id"=>"8729858900"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84959450570", "sgr"=>"84959450570", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0148028", "pmid"=>"26849217", "isbn"=>"1932-6203", "pui"=>"608547252"}, "id"=>"136e434f-37e3-3295-b331-0eeff41e4685", "abstract"=>"Rapid advancements in sequencing technologies along with falling costs present widespread opportunities for microbiome studies across a vast and diverse array of environments. These impressive technological developments have been accompanied by a considerable growth in the number of methodological variables, including sampling, storage, DNA extraction, primer pairs, sequencing technology, chemistry version, read length, insert size, and analysis pipelines, amongst others. This increase in variability threatens to compromise both the reproducibility and the comparability of studies conducted. Here we perform the first reported study comparing both amplicon and shotgun sequencing for the three leading next-generation sequencing technologies. These were applied to six human stool samples using Illumina HiSeq, MiSeq and Ion PGM shotgun sequencing, as well as amplicon sequencing across two variable 16S rRNA gene regions. Notably, we found that the factor responsible for the greatest variance in microbiota composition was the chosen methodology rather than the natural inter-individual variance, which is commonly one of the most significant drivers in microbiome studies. Amplicon sequencing suffered from this to a large extent, and this issue was particularly apparent when the 16S rRNA V1-V2 region amplicons were sequenced with MiSeq. Somewhat surprisingly, the choice of taxonomic binning software for shotgun sequences proved to be of crucial importance with even greater discriminatory power than sequencing technology and choice of amplicon. Optimal N50 assembly values for the HiSeq was obtained for 10 million reads per sample, whereas the applied MiSeq and PGM sequencing depths proved less sufficient for shotgun sequencing of stool samples. The latter technologies, on the other hand, provide a better basis for functional gene categorisation, possibly due to their longer read lengths. Hence, in addition to highlighting methodological biases, this study demonstrates the risks associated with comparing data generated using different strategies. We also recommend that laboratories with particular interests in certain microbes should optimise their protocols to accurately detect these taxa using different techniques.", "link"=>"http://www.mendeley.com/research/comparing-apples-oranges-next-generation-sequencing-impact-microbiome-analysis", "reader_count"=>262, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>10, "Researcher"=>67, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>77, "Student > Postgraduate"=>12, "Student > Master"=>45, "Other"=>15, "Student > Bachelor"=>17, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>10, "Researcher"=>67, "Student > Doctoral Student"=>7, "Student > Ph. D. 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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/4244662"], "description"=>"<p>The heat-plot also includes amplicon data long shotgun datasets from three classifiers namely: MetaPhlAn2, Kraken and GOTTCHA. Only genera in a minimum of 20% of datasets were retained. The method of correlation used was Spearman along with Ward D2 Clustering (PGM = Ion Personal Genome Machine).</p>", "links"=>[], "tags"=>["Ion PGM shotgun sequencing", "Microbiome Analysis Rapid advancements", "stool samples", "Optimal N 50 assembly values", "taxonomic binning software", "16 S rRNA gene regions", "PGM sequencing depths", "DNA", "microbiome studies", "sequencing technology", "shotgun sequencing", "amplicon"], "article_id"=>2596819, "categories"=>["Microbiology", "Genetics", "Molecular Biology", "Evolutionary Biology", "Ecology", "Biological Sciences not elsewhere classified", "Infectious Diseases"], "users"=>["Adam G. Clooney", "Fiona Fouhy", "Roy D. Sleator", "Aisling O’ Driscoll", "Catherine Stanton", "Paul D. Cotter", "Marcus J. Claesson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148028.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Heat_plot_representing_the_taxonomic_composition_of_the_samples_at_genus_level_/2596819", "title"=>"Heat-plot representing the taxonomic composition of the samples at genus level.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-05 19:49:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/4244698"], "description"=>"<p>The numbers represent the total number of predicted complete or incomplete genes for each metagenome.</p>", "links"=>[], "tags"=>["Ion PGM shotgun sequencing", "Microbiome Analysis Rapid advancements", "stool samples", "Optimal N 50 assembly values", "taxonomic binning software", "16 S rRNA gene regions", "PGM sequencing depths", "DNA", "microbiome studies", "sequencing technology", "shotgun sequencing", "amplicon"], "article_id"=>2596855, "categories"=>["Microbiology", "Genetics", "Molecular Biology", "Evolutionary Biology", "Ecology", "Biological Sciences not elsewhere classified", "Infectious Diseases"], "users"=>["Adam G. Clooney", "Fiona Fouhy", "Roy D. Sleator", "Aisling O’ Driscoll", "Catherine Stanton", "Paul D. Cotter", "Marcus J. Claesson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148028.g006", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Core_and_unique_genes_acquired_by_Metaphor_with_600_000_sequencing_randomly_selected_datasets_for_each_of_the_samples_/2596855", "title"=>"Core and unique genes acquired by Metaphor with 600,000 sequencing randomly selected datasets for each of the samples.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-05 19:49:07"}
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  • {"files"=>["https://ndownloader.figshare.com/files/4244671"], "description"=>"<p>The families are first organised by phylum abundance (highest to lowest) followed by family abundance (highest to lowest) in each of the phyla. The numbers of observed species are located at the top of each bar.</p>", "links"=>[], "tags"=>["Ion PGM shotgun sequencing", "Microbiome Analysis Rapid advancements", "stool samples", "Optimal N 50 assembly values", "taxonomic binning software", "16 S rRNA gene regions", "PGM sequencing depths", "DNA", "microbiome studies", "sequencing technology", "shotgun sequencing", "amplicon"], "article_id"=>2596825, "categories"=>["Microbiology", "Genetics", "Molecular Biology", "Evolutionary Biology", "Ecology", "Biological Sciences not elsewhere classified", "Infectious Diseases"], "users"=>["Adam G. Clooney", "Fiona Fouhy", "Roy D. Sleator", "Aisling O’ Driscoll", "Catherine Stanton", "Paul D. Cotter", "Marcus J. Claesson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148028.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Bar_charts_of_taxonomic_composition_at_family_level_/2596825", "title"=>"Bar-charts of taxonomic composition at family level.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-05 19:49:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/4244635", "https://ndownloader.figshare.com/files/4244638", "https://ndownloader.figshare.com/files/4244641", "https://ndownloader.figshare.com/files/4244647", "https://ndownloader.figshare.com/files/4244653"], "description"=>"<div><p>Rapid advancements in sequencing technologies along with falling costs present widespread opportunities for microbiome studies across a vast and diverse array of environments. These impressive technological developments have been accompanied by a considerable growth in the number of methodological variables, including sampling, storage, DNA extraction, primer pairs, sequencing technology, chemistry version, read length, insert size, and analysis pipelines, amongst others. This increase in variability threatens to compromise both the reproducibility and the comparability of studies conducted. Here we perform the first reported study comparing both amplicon and shotgun sequencing for the three leading next-generation sequencing technologies. These were applied to six human stool samples using Illumina HiSeq, MiSeq and Ion PGM shotgun sequencing, as well as amplicon sequencing across two variable 16S rRNA gene regions. Notably, we found that the factor responsible for the greatest variance in microbiota composition was the chosen methodology rather than the natural inter-individual variance, which is commonly one of the most significant drivers in microbiome studies. Amplicon sequencing suffered from this to a large extent, and this issue was particularly apparent when the 16S rRNA V1-V2 region amplicons were sequenced with MiSeq. Somewhat surprisingly, the choice of taxonomic binning software for shotgun sequences proved to be of crucial importance with even greater discriminatory power than sequencing technology and choice of amplicon. Optimal N50 assembly values for the HiSeq was obtained for 10 million reads per sample, whereas the applied MiSeq and PGM sequencing depths proved less sufficient for shotgun sequencing of stool samples. The latter technologies, on the other hand, provide a better basis for functional gene categorisation, possibly due to their longer read lengths. Hence, in addition to highlighting methodological biases, this study demonstrates the risks associated with comparing data generated using different strategies. We also recommend that laboratories with particular interests in certain microbes should optimise their protocols to accurately detect these taxa using different techniques.</p></div>", "links"=>[], "tags"=>["Ion PGM shotgun sequencing", "Microbiome Analysis Rapid advancements", "stool samples", "Optimal N 50 assembly values", "taxonomic binning software", "16 S rRNA gene regions", "PGM sequencing depths", "DNA", "microbiome studies", "sequencing technology", "shotgun sequencing", "amplicon"], "article_id"=>2596813, "categories"=>["Microbiology", "Genetics", "Molecular Biology", "Evolutionary Biology", "Ecology", "Biological Sciences not elsewhere classified", "Infectious Diseases"], "users"=>["Adam G. Clooney", "Fiona Fouhy", "Roy D. Sleator", "Aisling O’ Driscoll", "Catherine Stanton", "Paul D. Cotter", "Marcus J. Claesson"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0148028.s001", "https://dx.doi.org/10.1371/journal.pone.0148028.s002", "https://dx.doi.org/10.1371/journal.pone.0148028.s003", "https://dx.doi.org/10.1371/journal.pone.0148028.s004", "https://dx.doi.org/10.1371/journal.pone.0148028.s005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Comparing_Apples_and_Oranges_Next_Generation_Sequencing_and_Its_Impact_on_Microbiome_Analysis/2596813", "title"=>"Comparing Apples and Oranges?: Next Generation Sequencing and Its Impact on Microbiome Analysis", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2016-02-05 19:49:07"}

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Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}

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