Multi-locus Analysis of Genomic Time Series Data from Experimental Evolution
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{"title"=>"Inferring parameters of mutation, selection and demography from patterns of synonymous site evolution in Drosophila", "type"=>"journal", "authors"=>[{"first_name"=>"Gilean A T", "last_name"=>"McVean", "scopus_author_id"=>"55751007300"}, {"first_name"=>"Jorge", "last_name"=>"Vieira", "scopus_author_id"=>"7202312456"}], "year"=>2001, "source"=>"Genetics", "identifiers"=>{"issn"=>"00166731", "arxiv"=>"arXiv:1302.0452v1", "scopus"=>"2-s2.0-0035157374", "sgr"=>"0035157374", "pui"=>"32066311", "isbn"=>"0016-6731", "pmid"=>"11139506", "doi"=>"10.1371/journal.pgen.1005069"}, "id"=>"e7f736d7-d1e6-3a93-b0e6-a400629edcaf", "abstract"=>"Selection acting on codon usage can cause patterns of synonymous evolution to deviate considerably from those expected under neutrality. To investigate the quantitative relationship between parameters of mutation, selection, and demography, and patterns of synonymous site divergence, we have developed a novel combination of population genetic models and likelihood methods of phylogenetic sequence analysis. Comparing 50 orthologous gene pairs from Drosophila melanogaster and D. virilis and 27 from D. melanogaster and D. simulans, we show considerable variation between amino acids and genes in the strength of selection acting on codon usage and find evidence for both long-term and short-term changes in the strength of selection between species. Remarkably, D. melanogaster shows no evidence of current selection on codon usage, while its sister species D. simulans experiences only half the selection pressure for codon usage of their common ancestor. We also find evidence for considerable base asymmetries in the rate of mutation, such that the average synonymous mutation rate is 20-30% higher than in noncoding regions. A Bayesian approach is adopted to investigate how accounting for selection on codon usage influences estimates of the parameters of mutation.", "link"=>"http://www.mendeley.com/research/inferring-parameters-mutation-selection-demography-patterns-synonymous-site-evolution-drosophila", "reader_count"=>61, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>9, "Researcher"=>11, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>2, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>7}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>9, "Researcher"=>11, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>2, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>7}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Biochemistry, Genetics and Molecular Biology"=>11, "Mathematics"=>1, "Agricultural and Biological Sciences"=>40, "Medicine and Dentistry"=>2, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>40}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>11}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>5}}, "reader_count_by_country"=>{"Canada"=>1, "Netherlands"=>1, "Austria"=>1, "United States"=>1, "United Kingdom"=>3, "France"=>1, "Germany"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2009384"], "description"=>"<p>Density estimates of the product <math><mrow><mi>h</mi><mo>̂</mo><mo>⋅</mo><mi>s</mi><mo>̂</mo></mrow></math> when the parameters are estimated jointly. The selection coefficient was fixed at <i>s</i> = 0.01 while the dominance parameter <i>h</i> was varied. In each simulation, the initial allelic frequency was restricted to lie outside the interval [0.4, 0.6] (see discussion in text). The mean of <math><mrow><mi>h</mi><mo>̂</mo><mo>⋅</mo><mi>s</mi><mo>̂</mo></mrow></math> is quite close to the true value <i>hs</i> and the distribution is tightly concentrated around that value.</p>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370797, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005069.g004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overdominance_estimation_/1370797", "title"=>"Overdominance estimation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-07 05:48:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2009383"], "description"=>"<p>Data were generated using the standard parameters and sampled to a depth of 10 reads per site. Density estimates for the residual <math><mrow><mi>s</mi><mo>−</mo><mi>s</mi><mo>̂</mo></mrow></math> for <i>s</i> = 0.02, 0.05, 0.10 (top to bottom) are plotted. The red and blue lines denote the density estimates obtained using one- and five-locus models, respectively. The five-locus model, which takes additional data from linked sites into account, produces estimates which are more concentrated around the true parameter value.</p>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370796, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005069.g003", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimated_error_density_of_with_sampling_/1370796", "title"=>"Estimated error density of with sampling.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-07 05:48:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2009397", "https://ndownloader.figshare.com/files/2009398", "https://ndownloader.figshare.com/files/2009399", "https://ndownloader.figshare.com/files/2009400", "https://ndownloader.figshare.com/files/2009401", "https://ndownloader.figshare.com/files/2009402", "https://ndownloader.figshare.com/files/2009403", "https://ndownloader.figshare.com/files/2009404"], "description"=>"<div><p>Genomic time series data generated by evolve-and-resequence (E&R) experiments offer a powerful window into the mechanisms that drive evolution. However, standard population genetic inference procedures do not account for sampling serially over time, and new methods are needed to make full use of modern experimental evolution data. To address this problem, we develop a Gaussian process approximation to the multi-locus Wright-Fisher process with selection over a time course of tens of generations. The mean and covariance structure of the Gaussian process are obtained by computing the corresponding moments in discrete-time Wright-Fisher models conditioned on the presence of a linked selected site. This enables our method to account for the effects of linkage and selection, both along the genome and across sampled time points, in an approximate but principled manner. We first use simulated data to demonstrate the power of our method to correctly detect, locate and estimate the fitness of a selected allele from among several linked sites. We study how this power changes for different values of selection strength, initial haplotypic diversity, population size, sampling frequency, experimental duration, number of replicates, and sequencing coverage depth. In addition to providing quantitative estimates of selection parameters from experimental evolution data, our model can be used by practitioners to design E&R experiments with requisite power. We also explore how our likelihood-based approach can be used to infer other model parameters, including effective population size and recombination rate. Then, we apply our method to analyze genome-wide data from a real E&R experiment designed to study the adaptation of <i>D. melanogaster</i> to a new laboratory environment with alternating cold and hot temperatures.</p></div>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370810, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1005069.s001", "https://dx.doi.org/10.1371/journal.pgen.1005069.s002", "https://dx.doi.org/10.1371/journal.pgen.1005069.s003", "https://dx.doi.org/10.1371/journal.pgen.1005069.s004", "https://dx.doi.org/10.1371/journal.pgen.1005069.s005", "https://dx.doi.org/10.1371/journal.pgen.1005069.s006", "https://dx.doi.org/10.1371/journal.pgen.1005069.s007", "https://dx.doi.org/10.1371/journal.pgen.1005069.s008"], "stats"=>{"downloads"=>23, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Multi_locus_Analysis_of_Genomic_Time_Series_Data_from_Experimental_Evolution_/1370810", "title"=>"Multi-locus Analysis of Genomic Time Series Data from Experimental Evolution", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-04-07 05:48:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2009392"], "description"=>"<p>Results of localization procedure with finite coverage.</p>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370805, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005069.t002", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_localization_procedure_with_finite_coverage_/1370805", "title"=>"Results of localization procedure with finite coverage.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-07 05:48:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2009388"], "description"=>"<p>Shown here are the results for a 200 kb region of chromosome arm 3R. Note that the one-locus model resulted in several distant SNPs with comparably high LR values, while all multi-locus models produced cleaner pictures, isolating two nearby SNPs (illustrated in red) in the 14.615–14.619 Mb region with pronounced peaks.</p>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370801, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005069.g007", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_likelihood_ratio_results_for_the_one_locus_and_multi_locus_models_applied_to_a_real_E_R_experiment_of_D_melanogaster_/1370801", "title"=>"Comparison of likelihood-ratio results for the one-locus and multi-locus models applied to a real E&R experiment of <i>D. melanogaster</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-07 05:48:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2009386"], "description"=>"<p>The census population size (<i>N</i>) versus the estimated effective population size <math><mrow><mo stretchy=\"false\">(</mo><msub><mi>N</mi><mo>̂</mo><mi>e</mi></msub><mo stretchy=\"false\">)</mo></mrow></math> for 1,000 simulated E&R experiments. For each simulation, population size was chosen uniformly at random from the interval [10, 10<sup>4</sup>]. The estimator is quite accurate for small <i>N</i>, but becomes more variable as <i>N</i> grows. See text for discussion.</p>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370799, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005069.g006", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effective_population_size_estimation_/1370799", "title"=>"Effective population size estimation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-07 05:48:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2009385"], "description"=>"<p>A recombination hotspot was simulated by evolving a 100 kb region in which the recombination rate <i>r</i><sub><i>H</i></sub> = <i>α</i> ⋅ <i>r</i> for the middle 2 kb (positions 49–51 kb) was increased by a multiplicative factor <i>α</i> ∊ {10, 100, 1000} above the baseline recombination rate <i>r</i>. The hotspot intensity <math><mrow><msub><mi>r</mi><mo>̂</mo><mi>H</mi></msub></mrow></math> was then estimated from E&R experimental data. The figure shows density estimates of the residual <math><mrow><msub><mrow><mi>log</mi></mrow><mrow><mn>10</mn></mrow></msub><mo stretchy=\"false\">(</mo><msub><mi>r</mi><mo>̂</mo><mi>H</mi></msub><mo stretchy=\"false\">)</mo><mo>−</mo><msub><mrow><mi>log</mi></mrow><mrow><mn>10</mn></mrow></msub><mo stretchy=\"false\">(</mo><msub><mi>r</mi><mi>H</mi></msub><mo stretchy=\"false\">)</mo></mrow></math> for each value of <i>α</i>. Note that the mode of the density is close to zero in all cases. Furthermore, a 3-order increase in <i>r</i> is easily detected, while a 2-order increase can also be estimated to well within an order of magnitude of accuracy.</p>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370798, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005069.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hotspot_estimation_/1370798", "title"=>"Hotspot estimation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-07 05:48:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2009382"], "description"=>"<p>Parameters for each simulation were the same as in <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005069#pgen.1005069.g001\" target=\"_blank\">Fig. 1</a>, except that the population size was increased to <i>N</i> = 2000 (middle panel) and <i>N</i> = 5000 (bottom panel). Comparing these ROC curves with those in <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005069#pgen.1005069.g001\" target=\"_blank\">Fig. 1</a>, we see that increasing the population size by only a few folds significantly improves the performance of the test for selection.</p>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370795, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005069.g002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Receiver_operating_characteristics_when_testing_for_selection_in_E_amp_R_experiments_with_larger_population_sizes_/1370795", "title"=>"Receiver operating characteristics when testing for selection in E&R experiments with larger population sizes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-07 05:48:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2009396"], "description"=>"<p>Genes identified by our analysis as potentially being under selection in the E&R experiment [<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005069#pgen.1005069.ref025\" target=\"_blank\">25</a>, <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005069#pgen.1005069.ref036\" target=\"_blank\">36</a>] of <i>D. melanogaster</i>.</p>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370809, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005069.t006", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genes_identified_by_our_analysis_as_potentially_being_under_selection_in_the_E_R_experiment_25_36_of_D_melanogaster_/1370809", "title"=>"Genes identified by our analysis as potentially being under selection in the E&R experiment [25, 36] of <i>D. melanogaster</i>.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-07 05:48:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2009381"], "description"=>"<p>Each ROC curve was estimated using 200 simulations. For each selection regime, the curve was calculated by comparing the distribution of the maximum likelihood-ratio over all segregating sites in a region of length 100 kb with the distribution of the same statistic under neutrality. As the plots show, stronger selection is easier to detect than weaker selection, and increased haplotypic diversity makes it more difficult to confidently reject the null hypothesis of neutrality.</p>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370794, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005069.g001", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Receiver_operating_characteristics_ROC_when_testing_for_selection_in_a_region_under_the_default_parameter_setting_/1370794", "title"=>"Receiver operating characteristics (ROC) when testing for selection in a region under the default parameter setting.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-07 05:48:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2009394"], "description"=>"<p>Effect of sampling frequency on selection coefficient estimation.</p>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370807, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005069.t004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_sampling_frequency_on_selection_coefficient_estimation_/1370807", "title"=>"Effect of sampling frequency on selection coefficient estimation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-07 05:48:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2009395"], "description"=>"<p>Overdominance estimation.</p>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370808, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005069.t005", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overdominance_estimation_/1370808", "title"=>"Overdominance estimation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-07 05:48:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2009393"], "description"=>"<p>Estimation of selection coefficient.</p>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370806, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005069.t003", "stats"=>{"downloads"=>4, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimation_of_selection_coefficient_/1370806", "title"=>"Estimation of selection coefficient.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-07 05:48:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2009390"], "description"=>"<p>Results of localization procedure.</p>", "links"=>[], "tags"=>["evolution data", "Population size", "Experimental Evolution Genomic time series data", "experiment", "method", "sequencing coverage depth", "Gaussian process approximation", "sampling", "site", "Genomic Time Series Data", "parameter", "model"], "article_id"=>1370803, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Jonathan Terhorst", "Christian Schlötterer", "Yun S. Song"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1005069.t001", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_localization_procedure_/1370803", "title"=>"Results of localization procedure.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-07 05:48:58"}

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  • {"unique-ip"=>"15", "full-text"=>"13", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"7", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"11", "full-text"=>"14", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"10", "full-text"=>"11", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"8", "full-text"=>"9", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"10", "full-text"=>"10", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"11", "full-text"=>"13", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"12", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"15", "full-text"=>"16", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"9", "full-text"=>"11", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"15", "full-text"=>"14", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"10", "full-text"=>"7", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"15", "full-text"=>"15", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"16", "full-text"=>"18", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"15", "full-text"=>"27", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"15", "full-text"=>"19", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"13", "full-text"=>"13", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"13", "full-text"=>"15", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"10", "full-text"=>"9", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"7", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"8", "full-text"=>"6", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}

Relative Metric

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