Transcriptional Dynamics Reveal Critical Roles for Non-coding RNAs in the Immediate-Early Response
Publication Date
April 17, 2015
Journal
PLOS Computational Biology
Authors
Stuart Aitken, Shigeyuki Magi, Ahmad M. N. Alhendi, Masayoshi Itoh, et al
Volume
11
Issue
4
Pages
e1004217
DOI
https://dx.plos.org/10.1371/journal.pcbi.1004217
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004217
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25885578
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4401570
Europe PMC
http://europepmc.org/abstract/MED/25885578
Web of Science
000354517600056
Scopus
85000786191
Mendeley
http://www.mendeley.com/research/transcriptional-dynamics-reveal-critical-roles-noncoding-rnas-immediateearly-response
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Mendeley | Further Information

{"title"=>"Transcriptional Dynamics Reveal Critical Roles for Non-coding RNAs in the Immediate-Early Response", "type"=>"journal", "authors"=>[{"first_name"=>"Stuart", "last_name"=>"Aitken", "scopus_author_id"=>"8901741400"}, {"first_name"=>"Shigeyuki", "last_name"=>"Magi", "scopus_author_id"=>"55485856500"}, {"first_name"=>"Ahmad M.N.", "last_name"=>"Alhendi", "scopus_author_id"=>"56532035600"}, {"first_name"=>"Masayoshi", "last_name"=>"Itoh", "scopus_author_id"=>"16189673200"}, {"first_name"=>"Hideya", "last_name"=>"Kawaji", "scopus_author_id"=>"57196621148"}, {"first_name"=>"Timo", "last_name"=>"Lassmann", "scopus_author_id"=>"6505696106"}, {"first_name"=>"Carsten O.", "last_name"=>"Daub", "scopus_author_id"=>"6603641082"}, {"first_name"=>"Erik", "last_name"=>"Arner", "scopus_author_id"=>"7006368112"}, {"first_name"=>"Piero", "last_name"=>"Carninci", "scopus_author_id"=>"57200710309"}, {"first_name"=>"Alistair R.R.", "last_name"=>"Forrest", "scopus_author_id"=>"26433179400"}, {"first_name"=>"Yoshihide", "last_name"=>"Hayashizaki", "scopus_author_id"=>"35355246400"}, {"first_name"=>"Levon M.", "last_name"=>"Khachigian", "scopus_author_id"=>"26643419000"}, {"first_name"=>"Mariko", "last_name"=>"Okada-Hatakeyama", "scopus_author_id"=>"57194562293"}, {"first_name"=>"Colin A.", "last_name"=>"Semple", "scopus_author_id"=>"7006977465"}, {"first_name"=>"Alistair R.R.", "last_name"=>"Forrest", "scopus_author_id"=>"7202936901"}, {"first_name"=>"Albin", "last_name"=>"Sandelin", "scopus_author_id"=>"8531296700"}, {"first_name"=>"Carsten O.", "last_name"=>"Daub", "scopus_author_id"=>"57196979375"}, {"first_name"=>"Christine", "last_name"=>"Wells", "scopus_author_id"=>"7202578262"}, {"first_name"=>"David A.", "last_name"=>"Hume", "scopus_author_id"=>"26032966400"}, {"first_name"=>"Erik", "last_name"=>"Arner", "scopus_author_id"=>"57197889492"}, {"first_name"=>"Hideya", "last_name"=>"Kawaji", "scopus_author_id"=>"57200867994"}, {"first_name"=>"Kim M.", "last_name"=>"Summers", "scopus_author_id"=>"7005583789"}, {"first_name"=>"Kristoffer", "last_name"=>"Vitting-Seerup", "scopus_author_id"=>"56126254200"}, {"first_name"=>"Piero", "last_name"=>"Carninci", "scopus_author_id"=>"7005203099"}, {"first_name"=>"Robin", "last_name"=>"Andersson", "scopus_author_id"=>"13005726000"}, {"first_name"=>"Yoshihide", "last_name"=>"Hayashizaki", "scopus_author_id"=>"57193923316"}, {"first_name"=>"Ms Noriko", "last_name"=>"Yumoto", "scopus_author_id"=>"57192190584"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"604235844", "sgr"=>"85000786191", "issn"=>"15537358", "arxiv"=>"15334406", "pmid"=>"25885578", "scopus"=>"2-s2.0-85000786191", "doi"=>"10.1371/journal.pcbi.1004217", "isbn"=>"1095-9203 (Electronic)\\r0036-8075 (Linking)"}, "id"=>"8bb95908-d76f-33db-b276-20b331fb68c7", "abstract"=>"The immediate-early response mediates cell fate in response to a variety of extracellular stimuli and is dysregulated in many cancers. However, the specificity of the response across stimuli and cell types, and the roles of non-coding RNAs are not well understood. Using a large collection of densely-sampled time series expression data we have examined the induction of the immediate-early response in unparalleled detail, across cell types and stimuli. We exploit cap analysis of gene expression (CAGE) time series datasets to directly measure promoter activities over time. Using a novel analysis method for time series data we identify transcripts with expression patterns that closely resemble the dynamics of known immediate-early genes (IEGs) and this enables a comprehensive comparative study of these genes and their chromatin state. Surprisingly, these data suggest that the earliest transcriptional responses often involve promoters generating non-coding RNAs, many of which are produced in advance of canonical protein-coding IEGs. IEGs are known to be capable of induction without de novo protein synthesis. Consistent with this, we find that the response of both protein-coding and non-coding RNA IEGs can be explained by their transcriptionally poised, permissive chromatin state prior to stimulation. We also explore the function of non-coding RNAs in the attenuation of the immediate early response in a small RNA sequencing dataset matched to the CAGE data: We identify a novel set of microRNAs responsible for the attenuation of the IEG response in an estrogen receptor positive cancer cell line. Our computational statistical method is well suited to meta-analyses as there is no requirement for transcripts to pass thresholds for significant differential expression between time points, and it is agnostic to the number of time points per dataset.", "link"=>"http://www.mendeley.com/research/transcriptional-dynamics-reveal-critical-roles-noncoding-rnas-immediateearly-response", "reader_count"=>52, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Student > Doctoral Student"=>3, "Researcher"=>11, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>3, "Student > Master"=>4, "Other"=>1, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Student > Doctoral Student"=>3, "Researcher"=>11, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>3, "Student > Master"=>4, "Other"=>1, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>1, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>13, "Agricultural and Biological Sciences"=>27, "Medicine and Dentistry"=>5, "Neuroscience"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Psychology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Neuroscience"=>{"Neuroscience"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>27}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>13}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "United States"=>1, "Japan"=>2, "United Kingdom"=>2}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2026409"], "description"=>"<p>(A) Kinetic signatures are defined as piece-wise exponential (peak and dip), simple exponential (decay) or linear functions.(B) CAGE clusters associated with known IEGs show significant expression at time 0 (left; median 14.7 TPM). The maximum log2 fold change at any point in the time course over expression at time 0 is typically less than 2 (right; median 1.64). Histograms show data from all four data sets for 194l known IEGs. (C) Kinetic signatures fitted to the CAGE time course of EGR1 in EGF treated MCF7 cells yield values for the fit (log Z) and estimates for parameter moments. Plots show the kinetic signature function using computed parameter means (blue) and confidence intervals (red) for peak (left) and linear (right) kinetic signatures. In this case, log Z for the peak signature (-27.2) is greater than that for the linear model (-35), indicating a significantly better explanation of the data. Data values are plotted as circles (median value is filled). (D) CAGE time course data and best-fitting kinetic signature for IEGs JUN, FOS, EGR1 and DUSP1 (colours as in (C)). The vertical green lines indicate the mean switch time <i>t</i><sub><i>S</i></sub> and one standard deviation above and below.</p>", "links"=>[], "tags"=>["RNA sequencing dataset", "time series data", "cage", "time points", "gene", "expression", "measure promoter activities", "cancer cell line", "IEG", "novel analysis method", "time series datasets", "Transcriptional Dynamics Reveal Critical Roles", "stimuli", "cell types", "chromatin state", "response"], "article_id"=>1383789, "categories"=>["Biological Sciences"], "users"=>["Stuart Aitken", "Shigeyuki Magi", "Ahmad M. N. Alhendi", "Masayoshi Itoh", "Hideya Kawaji", "Timo Lassmann", "Carsten O. Daub", "Erik Arner", "Piero Carninci", "Alistair R. R. Forrest", "Yoshihide Hayashizaki", "Levon M. Khachigian", "Mariko Okada-Hatakeyama", "Colin A. Semple"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004217.g001", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Kinetic_signatures_for_IEGs_/1383789", "title"=>"Kinetic signatures for IEGs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 04:31:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026417"], "description"=>"<p>(A) Histograms of <i>t</i><sub><i>s</i></sub> for lncRNA, snoRNA, snRNA and miRNA precursors show these genes are activated rapidly. (B) Density plot of early peak gene length against <i>t</i><sub><i>s</i></sub> for all RNA biotypes (grey symbols), lncRNA (red symbols) and miRNA precursors (blue symbols). LncRNA and miRNA form distinct clusters of RNAs activated with a wide range of kinetics.</p>", "links"=>[], "tags"=>["RNA sequencing dataset", "time series data", "cage", "time points", "gene", "expression", "measure promoter activities", "cancer cell line", "IEG", "novel analysis method", "time series datasets", "Transcriptional Dynamics Reveal Critical Roles", "stimuli", "cell types", "chromatin state", "response"], "article_id"=>1383797, "categories"=>["Biological Sciences"], "users"=>["Stuart Aitken", "Shigeyuki Magi", "Ahmad M. N. Alhendi", "Masayoshi Itoh", "Hideya Kawaji", "Timo Lassmann", "Carsten O. Daub", "Erik Arner", "Piero Carninci", "Alistair R. R. Forrest", "Yoshihide Hayashizaki", "Levon M. Khachigian", "Mariko Okada-Hatakeyama", "Colin A. Semple"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004217.g004", "stats"=>{"downloads"=>3, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Non_coding_RNA_gene_activation_/1383797", "title"=>"Non-coding RNA gene activation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 04:31:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026425"], "description"=>"<p>P values for the over-representation of CAGE clusters in 73 Panther gene sets containing at least 20 genes. P values are calculated by hypergeometric test on the counts of clusters from all four data sets combined. Only pathways with p values ≤ 0.05 are listed (those with a FDR significant at 0.1 are indicated by *).</p><p>Pathway analysis.</p>", "links"=>[], "tags"=>["RNA sequencing dataset", "time series data", "cage", "time points", "gene", "expression", "measure promoter activities", "cancer cell line", "IEG", "novel analysis method", "time series datasets", "Transcriptional Dynamics Reveal Critical Roles", "stimuli", "cell types", "chromatin state", "response"], "article_id"=>1383805, "categories"=>["Biological Sciences"], "users"=>["Stuart Aitken", "Shigeyuki Magi", "Ahmad M. N. Alhendi", "Masayoshi Itoh", "Hideya Kawaji", "Timo Lassmann", "Carsten O. Daub", "Erik Arner", "Piero Carninci", "Alistair R. R. Forrest", "Yoshihide Hayashizaki", "Levon M. Khachigian", "Mariko Okada-Hatakeyama", "Colin A. Semple"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004217.t002", "stats"=>{"downloads"=>1, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pathway_analysis_/1383805", "title"=>"Pathway analysis.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-17 04:31:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026424"], "description"=>"<p>Enrichment (odds ratio) and p values by data set, and for all data sets (significant enrichments are in bold: p ≤ 0.05 by hypergeometric test).</p><p>Enrichment of IEGs in kinetic signatures.</p>", "links"=>[], "tags"=>["RNA sequencing dataset", "time series data", "cage", "time points", "gene", "expression", "measure promoter activities", "cancer cell line", "IEG", "novel analysis method", "time series datasets", "Transcriptional Dynamics Reveal Critical Roles", "stimuli", "cell types", "chromatin state", "response"], "article_id"=>1383804, "categories"=>["Biological Sciences"], "users"=>["Stuart Aitken", "Shigeyuki Magi", "Ahmad M. N. Alhendi", "Masayoshi Itoh", "Hideya Kawaji", "Timo Lassmann", "Carsten O. Daub", "Erik Arner", "Piero Carninci", "Alistair R. R. Forrest", "Yoshihide Hayashizaki", "Levon M. Khachigian", "Mariko Okada-Hatakeyama", "Colin A. Semple"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004217.t001", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enrichment_of_IEGs_in_kinetic_signatures_/1383804", "title"=>"Enrichment of IEGs in kinetic signatures.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-17 04:31:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026421"], "description"=>"<p>(A) Expression of mature hsa-mir-6163 and transcriptional activation of two of its target IEGs FOSB and EGR3 in MCF7 cells in response to HRG. Data values are plotted as circles (median value is filled). (B) Median CAGE expression (black circles) of precursor miRNA and median mature miRNA expression (red triangles) for hsa-mir-320a, host lncRNA MIR155HG and mature hsa-mir-155, and for hsa-mir-21 in MCF7-HRG (three replicates, lines are a spline fitted to the data). For hsa-mir-320a the increase in CAGE expression is significant when comparing 0min and 210min and the decrease in mature transcript levels is significant when comparing 0min and 240min (p ≤ 0.05 by t test). For hsa-mir-155 the increase in CAGE expression of MIR155HG is significant when comparing 0min and 180min, and the increase in mature transcript levels is significant when comparing 0min and 240min (p ≤ 0.05 by t test). For hsa-mir-21 the increases in CAGE expression and in mature transcript levels are significant when comparing 0min and 80min (p ≤ 0.05 by t test).</p>", "links"=>[], "tags"=>["RNA sequencing dataset", "time series data", "cage", "time points", "gene", "expression", "measure promoter activities", "cancer cell line", "IEG", "novel analysis method", "time series datasets", "Transcriptional Dynamics Reveal Critical Roles", "stimuli", "cell types", "chromatin state", "response"], "article_id"=>1383801, "categories"=>["Biological Sciences"], "users"=>["Stuart Aitken", "Shigeyuki Magi", "Ahmad M. N. Alhendi", "Masayoshi Itoh", "Hideya Kawaji", "Timo Lassmann", "Carsten O. Daub", "Erik Arner", "Piero Carninci", "Alistair R. R. Forrest", "Yoshihide Hayashizaki", "Levon M. Khachigian", "Mariko Okada-Hatakeyama", "Colin A. Semple"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004217.g005", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mature_microRNA_regulation_and_host_gene_activation_/1383801", "title"=>"Mature microRNA regulation and host gene activation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 04:31:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026415"], "description"=>"<p>Grey contour lines indicate the projected <i>t</i><sub><i>s</i></sub> for the completion of transcription accounting for gene length (a transcription rate of 60 bases/s is assumed [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004217#pcbi.1004217.ref032\" target=\"_blank\">32</a>]). (A) Early peak known IEGs (red symbols represent the underlying IEG CAGE cluster data). (B) Early peak known nucleotide binding genes and underlying data (blue symbols). (C) Travelling ratios for known IEGs and for early peak genes in MCF7 cells demonstrate promoter proximal pausing as the travelling ratio is shifts towards higher values. The intersection of IEGs and early peak genes (right-most plot) shows that the strong pausing effect seen for IEGs holds for those assigned the early peak signature.</p>", "links"=>[], "tags"=>["RNA sequencing dataset", "time series data", "cage", "time points", "gene", "expression", "measure promoter activities", "cancer cell line", "IEG", "novel analysis method", "time series datasets", "Transcriptional Dynamics Reveal Critical Roles", "stimuli", "cell types", "chromatin state", "response"], "article_id"=>1383795, "categories"=>["Biological Sciences"], "users"=>["Stuart Aitken", "Shigeyuki Magi", "Ahmad M. N. Alhendi", "Masayoshi Itoh", "Hideya Kawaji", "Timo Lassmann", "Carsten O. Daub", "Erik Arner", "Piero Carninci", "Alistair R. R. Forrest", "Yoshihide Hayashizaki", "Levon M. Khachigian", "Mariko Okada-Hatakeyama", "Colin A. Semple"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004217.g003", "stats"=>{"downloads"=>0, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Density_plots_of_gene_length_against_t_s_for_early_peak_clusters_/1383795", "title"=>"Density plots of gene length against <i>t</i><sub><i>s</i></sub> for early peak clusters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 04:31:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026429", "https://ndownloader.figshare.com/files/2026430", "https://ndownloader.figshare.com/files/2026431", "https://ndownloader.figshare.com/files/2026432", "https://ndownloader.figshare.com/files/2026433", "https://ndownloader.figshare.com/files/2026434", "https://ndownloader.figshare.com/files/2026435", "https://ndownloader.figshare.com/files/2026436", "https://ndownloader.figshare.com/files/2026437", "https://ndownloader.figshare.com/files/2026438", "https://ndownloader.figshare.com/files/2026439", "https://ndownloader.figshare.com/files/2026440", "https://ndownloader.figshare.com/files/2026441", "https://ndownloader.figshare.com/files/2026442", "https://ndownloader.figshare.com/files/2026443", "https://ndownloader.figshare.com/files/2026444", "https://ndownloader.figshare.com/files/2026445", "https://ndownloader.figshare.com/files/2026446"], "description"=>"<div><p>The immediate-early response mediates cell fate in response to a variety of extracellular stimuli and is dysregulated in many cancers. However, the specificity of the response across stimuli and cell types, and the roles of non-coding RNAs are not well understood. Using a large collection of densely-sampled time series expression data we have examined the induction of the immediate-early response in unparalleled detail, across cell types and stimuli. We exploit cap analysis of gene expression (CAGE) time series datasets to directly measure promoter activities over time. Using a novel analysis method for time series data we identify transcripts with expression patterns that closely resemble the dynamics of known immediate-early genes (IEGs) and this enables a comprehensive comparative study of these genes and their chromatin state. Surprisingly, these data suggest that the earliest transcriptional responses often involve promoters generating non-coding RNAs, many of which are produced in advance of canonical protein-coding IEGs. IEGs are known to be capable of induction without de novo protein synthesis. Consistent with this, we find that the response of both protein-coding and non-coding RNA IEGs can be explained by their transcriptionally poised, permissive chromatin state prior to stimulation. We also explore the function of non-coding RNAs in the attenuation of the immediate early response in a small RNA sequencing dataset matched to the CAGE data: We identify a novel set of microRNAs responsible for the attenuation of the IEG response in an estrogen receptor positive cancer cell line. Our computational statistical method is well suited to meta-analyses as there is no requirement for transcripts to pass thresholds for significant differential expression between time points, and it is agnostic to the number of time points per dataset.</p></div>", "links"=>[], "tags"=>["RNA sequencing dataset", "time series data", "cage", "time points", "gene", "expression", "measure promoter activities", "cancer cell line", "IEG", "novel analysis method", "time series datasets", "Transcriptional Dynamics Reveal Critical Roles", "stimuli", "cell types", "chromatin state", "response"], "article_id"=>1383809, "categories"=>["Biological Sciences"], "users"=>["Stuart Aitken", "Shigeyuki Magi", "Ahmad M. N. Alhendi", "Masayoshi Itoh", "Hideya Kawaji", "Timo Lassmann", "Carsten O. Daub", "Erik Arner", "Piero Carninci", "Alistair R. R. Forrest", "Yoshihide Hayashizaki", "Levon M. Khachigian", "Mariko Okada-Hatakeyama", "Colin A. Semple"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004217.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s005", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s006", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s007", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s008", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s009", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s010", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s011", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s012", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s013", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s014", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s015", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s016", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s017", "https://dx.doi.org/10.1371/journal.pcbi.1004217.s018"], "stats"=>{"downloads"=>18, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transcriptional_Dynamics_Reveal_Critical_Roles_for_Non_coding_RNAs_in_the_Immediate_Early_Response_/1383809", "title"=>"Transcriptional Dynamics Reveal Critical Roles for Non-coding RNAs in the Immediate-Early Response", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-04-17 04:31:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026414"], "description"=>"<p>(A) Bar charts showing the percentage of early peak clusters associated with IEGs (<i>t</i><sub><i>s</i></sub> binned in 30 min intervals), and (B) and those associated with nucleotide binding genes. The horizontal line indicates the average percentage. (C) The timing of known IEGs and transcription factors is shown for IEGs (red) and TFs (purple) assigned to the early peak signature in each MCF7 experiment. Symbols indicate the <i>t</i><sub><i>s</i></sub> (plotted on the x axis) and are labelled with the gene name associated with the CAGE cluster (symbols are positioned on the y axis for legibility only).</p>", "links"=>[], "tags"=>["RNA sequencing dataset", "time series data", "cage", "time points", "gene", "expression", "measure promoter activities", "cancer cell line", "IEG", "novel analysis method", "time series datasets", "Transcriptional Dynamics Reveal Critical Roles", "stimuli", "cell types", "chromatin state", "response"], "article_id"=>1383794, "categories"=>["Biological Sciences"], "users"=>["Stuart Aitken", "Shigeyuki Magi", "Ahmad M. N. Alhendi", "Masayoshi Itoh", "Hideya Kawaji", "Timo Lassmann", "Carsten O. Daub", "Erik Arner", "Piero Carninci", "Alistair R. R. Forrest", "Yoshihide Hayashizaki", "Levon M. Khachigian", "Mariko Okada-Hatakeyama", "Colin A. Semple"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004217.g002", "stats"=>{"downloads"=>3, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Timing_of_early_peak_CAGE_clusters_/1383794", "title"=>"Timing of early peak CAGE clusters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 04:31:21"}

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  • {"unique-ip"=>"30", "full-text"=>"24", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"7", "cited-by"=>"1", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"9", "full-text"=>"12", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"11", "full-text"=>"11", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"10", "full-text"=>"9", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"17", "full-text"=>"20", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"17", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"15", "full-text"=>"8", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"9", "cited-by"=>"1", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"5", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"8", "full-text"=>"7", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"12", "full-text"=>"12", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"1", "year"=>"2019", "month"=>"5"}

Relative Metric

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