Unveiling Distribution Patterns of Freshwater Phytoplankton by a Next Generation Sequencing Based Approach
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{"title"=>"Unveiling Distribution Patterns of Freshwater Phytoplankton by a Next Generation Sequencing Based Approach", "type"=>"journal", "authors"=>[{"first_name"=>"Alexander", "last_name"=>"Eiler", "scopus_author_id"=>"6506469534"}, {"first_name"=>"Stina", "last_name"=>"Drakare", "scopus_author_id"=>"6602645393"}, {"first_name"=>"Stefan", "last_name"=>"Bertilsson", "scopus_author_id"=>"6603932886"}, {"first_name"=>"Jakob", "last_name"=>"Pernthaler", "scopus_author_id"=>"7003766879"}, {"first_name"=>"Sari", "last_name"=>"Peura", "scopus_author_id"=>"54401504000"}, {"first_name"=>"Carina", "last_name"=>"Rofner", "scopus_author_id"=>"55568293800"}, {"first_name"=>"Karel", "last_name"=>"Simek", "scopus_author_id"=>"7005449468"}, {"first_name"=>"Yang", "last_name"=>"Yang", "scopus_author_id"=>"57192549400"}, {"first_name"=>"Petr", "last_name"=>"Znachor", "scopus_author_id"=>"8564692800"}, {"first_name"=>"Eva S.", "last_name"=>"Lindström", "scopus_author_id"=>"35321384800"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84872777339", "pui"=>"368176340", "doi"=>"10.1371/journal.pone.0053516", "isbn"=>"1932-6203", "sgr"=>"84872777339", "pmid"=>"23349714"}, "id"=>"f8ccb35e-46ee-3a70-85a8-b8a5639c01fe", "abstract"=>"The recognition and discrimination of phytoplankton species is one of the foundations of freshwater biodiversity research and environmental monitoring. This step is frequently a bottleneck in the analytical chain from sampling to data analysis and subsequent environmental status evaluation. Here we present phytoplankton diversity data from 49 lakes including three seasonal surveys assessed by next generation sequencing (NGS) of 16S ribosomal RNA chloroplast and cyanobacterial gene amplicons and also compare part of these datasets with identification based on morphology. Direct comparison of NGS to microscopic data from three time-series showed that NGS was able to capture the seasonality in phytoplankton succession as observed by microscopy. Still, the PCR-based approach was only semi-quantitative, and detailed NGS and microscopy taxa lists had only low taxonomic correspondence. This is probably due to, both, methodological constraints and current discrepancies in taxonomic frameworks. Discrepancies included Euglenophyta and Heterokonta that were scarce in the NGS but frequently detected by microscopy and Cyanobacteria that were in general more abundant and classified with high resolution by NGS. A deep-branching taxonomically unclassified cluster was frequently detected by NGS but could not be linked to any group identified by microscopy. NGS derived phytoplankton composition differed significantly among lakes with different trophic status, showing that our approach can resolve phytoplankton communities at a level relevant for ecosystem management. The high reproducibility and potential for standardization and parallelization makes our NGS approach an excellent candidate for simultaneous monitoring of prokaryotic and eukaryotic phytoplankton in inland waters.", "link"=>"http://www.mendeley.com/research/unveiling-distribution-patterns-freshwater-phytoplankton-next-generation-sequencing-based-approach", "reader_count"=>166, "reader_count_by_academic_status"=>{"Unspecified"=>6, "Professor > Associate Professor"=>10, "Librarian"=>1, "Researcher"=>36, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>54, "Student > Postgraduate"=>5, "Student > Master"=>22, "Other"=>5, "Student > Bachelor"=>11, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>6, "Professor > Associate Professor"=>10, "Librarian"=>1, "Researcher"=>36, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>54, "Student > Postgraduate"=>5, "Student > Master"=>22, "Other"=>5, "Student > Bachelor"=>11, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>11, "Engineering"=>2, "Environmental Science"=>44, "Biochemistry, Genetics and Molecular Biology"=>13, "Agricultural and Biological Sciences"=>83, "Medicine and Dentistry"=>2, "Physics and Astronomy"=>1, "Chemical Engineering"=>1, "Chemistry"=>1, "Earth and Planetary Sciences"=>7, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>7}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>83}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>13}, "Unspecified"=>{"Unspecified"=>11}, "Environmental Science"=>{"Environmental Science"=>44}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "reader_count_by_country"=>{"United States"=>5, "Japan"=>1, "United Kingdom"=>2, "Portugal"=>1, "Switzerland"=>3, "Spain"=>1, "Canada"=>1, "Sweden"=>2, "Netherlands"=>1, "South Korea"=>1, "Norway"=>1, "Brazil"=>1, "Mexico"=>1, "Australia"=>1, "Chile"=>2, "France"=>1, "Germany"=>1, "Estonia"=>1}, "group_count"=>4}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/503168"], "description"=>"<p>Stress value was 0.20. Permutational ANOVA confirmed visual inspection of significant differences in community composition between lakes of different status (p<0.001; R<sup>2</sup> = 0.254).</p>", "links"=>[], "tags"=>["phytoplankton", "lakes", "trophic", "eutrophic"], "article_id"=>173676, "categories"=>["Microbiology", "Inorganic Chemistry", "Genetics", "Plant Biology", "Evolutionary Biology"], "users"=>["Alexander Eiler", "Stina Drakare", "Stefan Bertilsson", "Jakob Pernthaler", "Sari Peura", "Carina Rofner", "Karel Šimek", "Yang Yang", "Petr Znachor", "Eva S. Lindström"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053516.g003", "stats"=>{"downloads"=>2, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ordination_plot_showing_phytoplankton_community_composition_among_lakes_of_different_trophic_status_oligotrophic_mesotrophic_eutrophic_and_dystrophic_/173676", "title"=>"Ordination plot showing phytoplankton community composition among lakes of different trophic status (oligotrophic, mesotrophic, eutrophic and dystrophic).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-22 01:01:16"}
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  • {"files"=>["https://ndownloader.figshare.com/files/503551"], "description"=>"<p>Summary statistics of sequencing data including coordinates and classification of systems.</p>", "links"=>[], "tags"=>["sequencing", "coordinates", "classification"], "article_id"=>174060, "categories"=>["Microbiology", "Inorganic Chemistry", "Genetics", "Plant Biology", "Evolutionary Biology"], "users"=>["Alexander Eiler", "Stina Drakare", "Stefan Bertilsson", "Jakob Pernthaler", "Sari Peura", "Carina Rofner", "Karel Šimek", "Yang Yang", "Petr Znachor", "Eva S. Lindström"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053516.t001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_statistics_of_sequencing_data_including_coordinates_and_classification_of_systems_/174060", "title"=>"Summary statistics of sequencing data including coordinates and classification of systems.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-01-22 01:07:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/503444"], "description"=>"<p>Colors indicate the abundance of each taxon at each time point in relation to its maximum abundance in the respective time-series.</p>", "links"=>[], "tags"=>["temporal", "phytoplankton", "taxa", "dystrophic", "alinen", "mesotrophic", "erken", "reservoir", "rimov", "revealed"], "article_id"=>173935, "categories"=>["Microbiology", "Inorganic Chemistry", "Genetics", "Plant Biology", "Evolutionary Biology"], "users"=>["Alexander Eiler", "Stina Drakare", "Stefan Bertilsson", "Jakob Pernthaler", "Sari Peura", "Carina Rofner", "Karel Šimek", "Yang Yang", "Petr Znachor", "Eva S. Lindström"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053516.g005", "stats"=>{"downloads"=>4, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heatmap_showing_temporal_dynamics_of_phytoplankton_taxa_in_dystrophic_Lake_Alinen_Mustaj_228_rvi_A_mesotrophic_Lake_Erken_B_and_mesotrophic_reservoir_Rimov_C_as_revealed_by_next_generation_sequencing_/173935", "title"=>"Heatmap showing temporal dynamics of phytoplankton taxa in dystrophic Lake Alinen Mustajärvi (A), mesotrophic Lake Erken (B), and mesotrophic reservoir Rimov (C) as revealed by next generation sequencing.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-22 01:05:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/502872"], "description"=>"<p>The lines depict different ratios (phytoplankton reads∶total number of reads) and the points represent the samples.</p>", "links"=>[], "tags"=>["reads", "phytoplankton", "rarefaction", "curves", "next-generation", "sequencing", "259"], "article_id"=>173373, "categories"=>["Microbiology", "Inorganic Chemistry", "Genetics", "Plant Biology", "Evolutionary Biology"], "users"=>["Alexander Eiler", "Stina Drakare", "Stefan Bertilsson", "Jakob Pernthaler", "Sari Peura", "Carina Rofner", "Karel Šimek", "Yang Yang", "Petr Znachor", "Eva S. Lindström"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053516.g001", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_ratio_between_the_total_number_of_reads_and_the_number_of_phytoplankton_reads_A_and_rarefaction_curves_of_the_next_generation_sequencing_data_from_all_259_samples_B_/173373", "title"=>"The ratio between the total number of reads and the number of phytoplankton reads (A) and rarefaction curves of the next-generation sequencing data (from all 259 samples) (B).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-22 00:56:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/477173", "https://ndownloader.figshare.com/files/477175"], "description"=>"<div><p>The recognition and discrimination of phytoplankton species is one of the foundations of freshwater biodiversity research and environmental monitoring. This step is frequently a bottleneck in the analytical chain from sampling to data analysis and subsequent environmental status evaluation. Here we present phytoplankton diversity data from 49 lakes including three seasonal surveys assessed by next generation sequencing (NGS) of 16S ribosomal RNA chloroplast and cyanobacterial gene amplicons and also compare part of these datasets with identification based on morphology. Direct comparison of NGS to microscopic data from three time-series showed that NGS was able to capture the seasonality in phytoplankton succession as observed by microscopy. Still, the PCR-based approach was only semi-quantitative, and detailed NGS and microscopy taxa lists had only low taxonomic correspondence. This is probably due to, both, methodological constraints and current discrepancies in taxonomic frameworks. Discrepancies included Euglenophyta and Heterokonta that were scarce in the NGS but frequently detected by microscopy and Cyanobacteria that were in general more abundant and classified with high resolution by NGS. A deep-branching taxonomically unclassified cluster was frequently detected by NGS but could not be linked to any group identified by microscopy. NGS derived phytoplankton composition differed significantly among lakes with different trophic status, showing that our approach can resolve phytoplankton communities at a level relevant for ecosystem management. The high reproducibility and potential for standardization and parallelization makes our NGS approach an excellent candidate for simultaneous monitoring of prokaryotic and eukaryotic phytoplankton in inland waters.</p> </div>", "links"=>[], "tags"=>["unveiling", "patterns", "freshwater", "phytoplankton", "sequencing", "based", "approach"], "article_id"=>153980, "categories"=>["Microbiology", "Inorganic Chemistry", "Genetics", "Cell Biology", "Evolutionary Biology"], "users"=>["Alexander Eiler", "Stina Drakare", "Stefan Bertilsson", "Jakob Pernthaler", "Sari Peura", "Carina Rofner", "Karel Šimek", "Yang Yang", "Petr Znachor", "Eva S. Lindström"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0053516.s001", "https://dx.doi.org/10.1371/journal.pone.0053516.s002"], "stats"=>{"downloads"=>18, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Unveiling_Distribution_Patterns_of_Freshwater_Phytoplankton_by_a_Next_Generation_Sequencing_Based_Approach__/153980", "title"=>"Unveiling Distribution Patterns of Freshwater Phytoplankton by a Next Generation Sequencing Based Approach", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-01-22 01:06:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/503040"], "description"=>"<p>Inner ring indicates the similarity of sequences to the nt/nr database (NCBI) as determined by BLAST search. Outer ring (bars) indicates the number of reads assigned to each node when using the resampled dataset (100 reads); note that nodes where all reads were removed by resampling are still given. Colored branches indicate group assignments from Bayesian classifier against a phytoplankton database.</p>", "links"=>[], "tags"=>["silva106", "sequences", "phytoplankton-related"], "article_id"=>173548, "categories"=>["Microbiology", "Inorganic Chemistry", "Genetics", "Plant Biology", "Evolutionary Biology"], "users"=>["Alexander Eiler", "Stina Drakare", "Stefan Bertilsson", "Jakob Pernthaler", "Sari Peura", "Carina Rofner", "Karel Šimek", "Yang Yang", "Petr Znachor", "Eva S. Lindström"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053516.g002", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phylogenetic_tree_based_on_SILVA106_reference_tree_showing_representative_sequences_from_all_phytoplankton_related_OTUs_/173548", "title"=>"Phylogenetic tree (based on SILVA106 reference tree) showing representative sequences from all phytoplankton-related OTUs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-22 00:59:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/503293"], "description"=>"<p>Plots show the ratio between relative reads numbers and biovolumes (as determined by microscopy) for each phylum. (AM) Alinen Mustajarvi, (ER) Lake Erken, and (RI) Rimov Reservoir. A ratio above zero indicates that a specific phylum is preferentially detected by NGS whereas a ratio below zero indicates an over representation in the biovolume data relative to NGS. The part of the plot indicated in grey represents the area where the ratio is the result of that a phylum was only detected by either method.</p>", "links"=>[], "tags"=>["ratios", "taxonomic", "phylum", "revealed", "sequencing"], "article_id"=>173796, "categories"=>["Microbiology", "Inorganic Chemistry", "Genetics", "Plant Biology", "Evolutionary Biology"], "users"=>["Alexander Eiler", "Stina Drakare", "Stefan Bertilsson", "Jakob Pernthaler", "Sari Peura", "Carina Rofner", "Karel Šimek", "Yang Yang", "Petr Znachor", "Eva S. Lindström"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053516.g004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Boxplot_showing_ratios_in_taxonomic_composition_at_phylum_level_as_revealed_by_next_generation_sequencing_NGS_vs_microscopy_/173796", "title"=>"Boxplot showing ratios in taxonomic composition (at phylum level) as revealed by next generation sequencing (NGS) vs. microscopy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-22 01:03:16"}

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Relative Metric

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