Automated and Accurate Estimation of Gene Family Abundance from Shotgun Metagenomes
Publication Date
November 13, 2015
Journal
PLOS Computational Biology
Authors
Stephen Nayfach, Patrick H. Bradley, Stacia K. Wyman, Timothy J. Laurent, et al
Volume
11
Issue
11
Pages
e1004573
DOI
https://dx.plos.org/10.1371/journal.pcbi.1004573
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004573
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26565399
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4643905
Europe PMC
http://europepmc.org/abstract/MED/26565399
Web of Science
000365801600032
Scopus
84949219026
Mendeley
http://www.mendeley.com/research/automated-accurate-estimation-gene-family-abundance-shotgun-metagenomes
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CiteULike | Further Information

Mendeley | Further Information

{"title"=>"Automated and Accurate Estimation of Gene Family Abundance from Shotgun Metagenomes", "type"=>"journal", "authors"=>[{"first_name"=>"Stephen", "last_name"=>"Nayfach", "scopus_author_id"=>"56780457000"}, {"first_name"=>"Patrick H.", "last_name"=>"Bradley", "scopus_author_id"=>"57196957685"}, {"first_name"=>"Stacia K.", "last_name"=>"Wyman", "scopus_author_id"=>"6603846896"}, {"first_name"=>"Timothy J.", "last_name"=>"Laurent", "scopus_author_id"=>"56089592500"}, {"first_name"=>"Alex", "last_name"=>"Williams", "scopus_author_id"=>"8139868000"}, {"first_name"=>"Jonathan A.", "last_name"=>"Eisen", "scopus_author_id"=>"35247902700"}, {"first_name"=>"Katherine S.", "last_name"=>"Pollard", "scopus_author_id"=>"35235175500"}, {"first_name"=>"Thomas J.", "last_name"=>"Sharpton", "scopus_author_id"=>"14018726100"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84949219026", "pmid"=>"26565399", "pui"=>"607183861", "scopus"=>"2-s2.0-84949219026", "doi"=>"10.1371/journal.pcbi.1004573", "issn"=>"15537358"}, "id"=>"833d80fa-f63d-317f-9e2b-7268a2fa185e", "abstract"=>"Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohn's disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of disease.", "link"=>"http://www.mendeley.com/research/automated-accurate-estimation-gene-family-abundance-shotgun-metagenomes", "reader_count"=>123, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>3, "Librarian"=>2, "Researcher"=>27, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>37, "Student > Postgraduate"=>7, "Student > Master"=>20, "Other"=>4, "Student > Bachelor"=>8, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>3, "Librarian"=>2, "Researcher"=>27, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>37, "Student > Postgraduate"=>7, "Student > Master"=>20, "Other"=>4, "Student > Bachelor"=>8, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>9, "Environmental Science"=>7, "Biochemistry, Genetics and Molecular Biology"=>17, "Agricultural and Biological Sciences"=>65, "Medicine and Dentistry"=>4, "Physics and Astronomy"=>2, "Social Sciences"=>1, "Computer Science"=>12, "Immunology and Microbiology"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>65}, "Computer Science"=>{"Computer Science"=>12}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>17}, "Unspecified"=>{"Unspecified"=>9}, "Environmental Science"=>{"Environmental Science"=>7}}, "reader_count_by_country"=>{"Sweden"=>2, "Belgium"=>1, "United States"=>9, "Japan"=>1, "France"=>1, "Portugal"=>1, "Germany"=>3}, "group_count"=>6}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2439514"], "description"=>"<p>The Shotgun Metagenome Annotation Pipeline (ShotMAP) is an end-to-end metagenome annotation and analysis workflow. It takes as input metagenomic reads and a protein family database (grey boxes), and implements a variety of algorithms to predict genes, identify protein family homologs, quantify protein family abundances, and, optionally, statistically compare metagenomes (white boxes). ShotMAP produces a variety of outputs (blue boxes), including a mapping of sequences into protein families, an abundance profile of the protein families identified in the metagenome, estimates of protein family alpha- and beta-diversity, and a list of families that statistically stratify samples (<i>i</i>.<i>e</i>., putative biomarkers). ShotMAP can be run on a local computer and can optionally interact with an SGE-configured cluster to manage computationally expensive tasks.</p>", "links"=>[], "tags"=>["ShotMAP", "Shotgun Metagenomes Shotgun metagenomic DNA sequencing", "gut microbiota", "Gene Family Abundance", "biomarker", "Shotgun Metagenome Annotation Pipeline", "metagenome", "annotation databases impact", "Several bioinformatic tools"], "article_id"=>1602273, "categories"=>["Uncategorised"], "users"=>["Stephen Nayfach", "Patrick H. Bradley", "Stacia K. Wyman", "Timothy J. Laurent", "Alex Williams", "Jonathan A. Eisen", "Katherine S. Pollard", "Thomas J. Sharpton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004573.g006", "stats"=>{"downloads"=>1, "page_views"=>75, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ShotMAP_workflow_/1602273", "title"=>"ShotMAP workflow.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-13 03:10:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2439515"], "description"=>"<p>Crohn’s disease (CD) associated gut microbiomes are functionally differentiated from their healthy (H) and ulcerative colitis (UC) associated counterparts in the MGS cohort. (A) Boxplots illustrate that CD gut microbiomes exhibit lower protein family richness than UC and H microbiomes (p < 0.05). (B) The average genome size of the organisms in CD microbiomes is larger than the corresponding average in UC patients or healthy controls (p = 0.1). (C) Principal components analysis (PCA) of protein family abundance profiles identifies differentiation in beta-diversity between CD (red) and non-CD populations (Ulcerative colitis, green; Healthy, blue) for the first two (upper panel) and first and third (lower panel) principal components. Here, PCA was conducted by scaling all KO abundances to have zero mean and unit variance, though similar structure is identified using different PCA parameters. Ellipses represent 95% confidence intervals as quantified by the ordiellipse function in the R package vegan [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004573#pcbi.1004573.ref045\" target=\"_blank\">45</a>]. A permutational multivariate analysis of variance (adonis) quantified the reported p-values for the null hypothesis that the PCA axes are not different by group based on 1e4 permutations. These trends are also observed in an independent analysis of the MetaHIT cohort (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004573#pcbi.1004573.s015\" target=\"_blank\">S11 Fig</a>).</p>", "links"=>[], "tags"=>["ShotMAP", "Shotgun Metagenomes Shotgun metagenomic DNA sequencing", "gut microbiota", "Gene Family Abundance", "biomarker", "Shotgun Metagenome Annotation Pipeline", "metagenome", "annotation databases impact", "Several bioinformatic tools"], "article_id"=>1602274, "categories"=>["Uncategorised"], "users"=>["Stephen Nayfach", "Patrick H. Bradley", "Stacia K. Wyman", "Timothy J. Laurent", "Alex Williams", "Jonathan A. Eisen", "Katherine S. Pollard", "Thomas J. Sharpton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004573.g007", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Crohn_8217_s_disease_associated_gut_microbiomes_are_functionally_distinct_from_microbiome_associated_with_ulcerative_colitis_or_healthy_controls_/1602274", "title"=>"Crohn’s disease associated gut microbiomes are functionally distinct from microbiome associated with ulcerative colitis or healthy controls.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-13 03:10:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2439523", "https://ndownloader.figshare.com/files/2439524", "https://ndownloader.figshare.com/files/2439525", "https://ndownloader.figshare.com/files/2439526", "https://ndownloader.figshare.com/files/2439527", "https://ndownloader.figshare.com/files/2439528", "https://ndownloader.figshare.com/files/2439529", "https://ndownloader.figshare.com/files/2439530", "https://ndownloader.figshare.com/files/2439531", "https://ndownloader.figshare.com/files/2439532", "https://ndownloader.figshare.com/files/2439533", "https://ndownloader.figshare.com/files/2439534", "https://ndownloader.figshare.com/files/2439535", "https://ndownloader.figshare.com/files/2439536", "https://ndownloader.figshare.com/files/2439537", "https://ndownloader.figshare.com/files/2439538", "https://ndownloader.figshare.com/files/2439539", "https://ndownloader.figshare.com/files/2439540"], "description"=>"<div><p>Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohn’s disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of disease.</p></div>", "links"=>[], "tags"=>["ShotMAP", "Shotgun Metagenomes Shotgun metagenomic DNA sequencing", "gut microbiota", "Gene Family Abundance", "biomarker", "Shotgun Metagenome Annotation Pipeline", "metagenome", "annotation databases impact", "Several bioinformatic tools"], "article_id"=>1602281, "categories"=>["Uncategorised"], "users"=>["Stephen Nayfach", "Patrick H. Bradley", "Stacia K. Wyman", "Timothy J. Laurent", "Alex Williams", "Jonathan A. Eisen", "Katherine S. Pollard", "Thomas J. Sharpton"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004573.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s005", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s006", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s007", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s008", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s009", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s010", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s011", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s012", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s013", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s014", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s015", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s016", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s017", "https://dx.doi.org/10.1371/journal.pcbi.1004573.s018"], "stats"=>{"downloads"=>13, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Automated_and_Accurate_Estimation_of_Gene_Family_Abundance_from_Shotgun_Metagenomes_/1602281", "title"=>"Automated and Accurate Estimation of Gene Family Abundance from Shotgun Metagenomes", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-13 03:10:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2439492"], "description"=>"<p>1) Taxonomic profiles from real metagenomes are used to construct mock microbial communities. 2) Protein family annotations for SFams [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004573#pcbi.1004573.ref023\" target=\"_blank\">23</a>] are transferred to genes present in community members’ genomes. 3) The expected relative abundance of protein families is computed for the mock community. 4) Metagenomic reads are simulated from reference genomes that correspond to community members. 5) Simulated metagenomic reads are translated into predicted peptides. 6) Peptides are searched against the database of protein families used in (2) using the alignment tool RAPsearch2 [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004573#pcbi.1004573.ref024\" target=\"_blank\">24</a>] 7) Metagenomic peptides are classified into protein families according to their top-hit. Hits that do not satisfy the classification threshold are removed. 8) Classified metagenomic reads are used to estimate the relative abundance of protein families in the mock community. 9) Estimation accuracy is computed using an L1 distance between the expected and estimated relative abundance profiles.</p>", "links"=>[], "tags"=>["ShotMAP", "Shotgun Metagenomes Shotgun metagenomic DNA sequencing", "gut microbiota", "Gene Family Abundance", "biomarker", "Shotgun Metagenome Annotation Pipeline", "metagenome", "annotation databases impact", "Several bioinformatic tools"], "article_id"=>1602256, "categories"=>["Uncategorised"], "users"=>["Stephen Nayfach", "Patrick H. Bradley", "Stacia K. Wyman", "Timothy J. Laurent", "Alex Williams", "Jonathan A. Eisen", "Katherine S. Pollard", "Thomas J. Sharpton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004573.g001", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Metagenomic_simulation_framework_/1602256", "title"=>"Metagenomic simulation framework.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-13 03:10:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2439499"], "description"=>"<p>Reads of various length (70–500 bp) were simulated with 1% error rate from mock community 160319967-stool1. Reads were translated naïvely in 6 frames (6FT) or were translated using a metagenomic gene prediction tool (Prodigal, FragGeneScan, or MetaGeneMark). Predicted ORFs were searched and classified into SFams using RAPsearch2. All other parameters were tuned to minimize relative abundance error. (A) Amino acid sequence length (relative to naïve 6FT) resulting from read translation. (B) Minimum relative abundance estimation error for mock community 160319967-stool1 corresponding to different translation methods.</p>", "links"=>[], "tags"=>["ShotMAP", "Shotgun Metagenomes Shotgun metagenomic DNA sequencing", "gut microbiota", "Gene Family Abundance", "biomarker", "Shotgun Metagenome Annotation Pipeline", "metagenome", "annotation databases impact", "Several bioinformatic tools"], "article_id"=>1602258, "categories"=>["Uncategorised"], "users"=>["Stephen Nayfach", "Patrick H. Bradley", "Stacia K. Wyman", "Timothy J. Laurent", "Alex Williams", "Jonathan A. Eisen", "Katherine S. Pollard", "Thomas J. Sharpton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004573.g002", "stats"=>{"downloads"=>2, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ab_initio_gene_prediction_reduces_data_volume_at_a_small_cost_to_accuracy_/1602258", "title"=>"<i>Ab initio</i> gene prediction reduces data volume at a small cost to accuracy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-13 03:10:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2439501"], "description"=>"<p>Reads of various length (70–3,000 bp) were simulated with 1% error rate from mock community 160319967-stool1. (A) Predicted open reading frames (ORFs) were derived either via naïve 6-frame translation (6FT) or via the metagenomic gene finder Prodigal. Per-read annotation indicates that each read was classified according to the top-scoring hit across all of its ORFs. Per-ORF annotation indicates that each ORF was classified independently. Short reads benefit from 6FT and per-read annotation while long reads benefit from the gene finder Prodigal and per-ORF annotation. (B) Protein family abundances were estimated either by counting the number of hits to a family (count-based abundance) or by taking the sum of alignment lengths from hits (coverage-based abundance). In both cases, protein family abundance estimates were normalized by the gene length of reference sequences and scaled to sum to 1.0. The coverage-based abundance metric improves performance for long reads.</p>", "links"=>[], "tags"=>["ShotMAP", "Shotgun Metagenomes Shotgun metagenomic DNA sequencing", "gut microbiota", "Gene Family Abundance", "biomarker", "Shotgun Metagenome Annotation Pipeline", "metagenome", "annotation databases impact", "Several bioinformatic tools"], "article_id"=>1602260, "categories"=>["Uncategorised"], "users"=>["Stephen Nayfach", "Patrick H. Bradley", "Stacia K. Wyman", "Timothy J. Laurent", "Alex Williams", "Jonathan A. Eisen", "Katherine S. Pollard", "Thomas J. Sharpton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004573.g003", "stats"=>{"downloads"=>3, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Short_reads_and_long_reads_require_different_annotation_strategies_/1602260", "title"=>"Short-reads and long-reads require different annotation strategies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-13 03:10:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2439503"], "description"=>"<p>(A) Simulated metagenomes (50–500 bp; 1% error rate; mock community 160319967-stool1) were searched and classified into SFams at different bit-score thresholds. At each threshold, L1 relative abundance error was calculated. (B) Simulated 101-bp Illumina metagenomes from ten communities were searched and classified into SFams at different bit-score thresholds. Plotted is the optimal bit-score threshold for each community. Error bars indicate the range of bit-scores that result in L1 error within 1% of the optimal level. (C) Relative abundance error for simulated metagenomes of varying phylogenetic distance to reference genomes (50–500 bp; species to phylum taxonomic exclusion; 1% error rate; mock community 160319967-stool1). (D) Optimal bit-score thresholds error for metagenomes in (C). (E) Relative abundance error for simulated metagenomes of varying length and sequencing error (50–500 bp; 0–10% error rate; mock community 160319967-stool1). (F) Optimal bit-score thresholds for metagenomes in (E).</p>", "links"=>[], "tags"=>["ShotMAP", "Shotgun Metagenomes Shotgun metagenomic DNA sequencing", "gut microbiota", "Gene Family Abundance", "biomarker", "Shotgun Metagenome Annotation Pipeline", "metagenome", "annotation databases impact", "Several bioinformatic tools"], "article_id"=>1602262, "categories"=>["Uncategorised"], "users"=>["Stephen Nayfach", "Patrick H. Bradley", "Stacia K. Wyman", "Timothy J. Laurent", "Alex Williams", "Jonathan A. Eisen", "Katherine S. Pollard", "Thomas J. Sharpton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004573.g004", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_read_length_bit_score_threshold_and_prediction_accuracy_/1602262", "title"=>"Relationship between read length, bit-score threshold, and prediction accuracy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-13 03:10:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/2439507"], "description"=>"<p>(A) Relative abundance error for 101-bp Illumina metagenomes from 10 mock communities using between 10,000 and 1 million reads. (B) Relative abundance error for a 101-bp Illumina metagenomes from mock community 160319967-stool1 using between 10,000 and 100 million reads. (C) Expected versus observed functional distances for 10 mock communities using between 10,000 and 1.5 million 101-bp Illumina reads. (D) Distributions of Bray-Curtis dissimilarity error at each sequencing depth from (C).</p>", "links"=>[], "tags"=>["ShotMAP", "Shotgun Metagenomes Shotgun metagenomic DNA sequencing", "gut microbiota", "Gene Family Abundance", "biomarker", "Shotgun Metagenome Annotation Pipeline", "metagenome", "annotation databases impact", "Several bioinformatic tools"], "article_id"=>1602266, "categories"=>["Uncategorised"], "users"=>["Stephen Nayfach", "Patrick H. Bradley", "Stacia K. Wyman", "Timothy J. Laurent", "Alex Williams", "Jonathan A. Eisen", "Katherine S. Pollard", "Thomas J. Sharpton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004573.g005", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Shallow_sequencing_enables_accurate_estimates_of_alpha_and_beta_functional_diversity_/1602266", "title"=>"Shallow sequencing enables accurate estimates of alpha and beta functional diversity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-13 03:10:40"}

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  • {"unique-ip"=>"21", "full-text"=>"24", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"11", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"13", "full-text"=>"17", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"16", "full-text"=>"12", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"14", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"16", "full-text"=>"20", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"10", "full-text"=>"11", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"6", "full-text"=>"7", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"11", "full-text"=>"10", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"9", "full-text"=>"9", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"18", "full-text"=>"16", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"40", "full-text"=>"56", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"12", "full-text"=>"11", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"13", "full-text"=>"14", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"2", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"22", "full-text"=>"23", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"8", "full-text"=>"11", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"17", "full-text"=>"19", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"19", "full-text"=>"14", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"2", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"21", "full-text"=>"12", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"21", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"17", "full-text"=>"15", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"19", "cited-by"=>"2", "year"=>"2020", "month"=>"8"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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