Integrated Analysis of Gene Expression, CpG Island Methylation, and Gene Copy Number in Breast Cancer Cells by Deep Sequencing
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{"title"=>"Integrated analysis of gene expression, CpG Island methylation, and gene copy number in breast cancer cells by deep sequencing", "type"=>"journal", "authors"=>[{"first_name"=>"Zhifu", "last_name"=>"Sun", "scopus_author_id"=>"7404240192"}, {"first_name"=>"Yan W.", "last_name"=>"Asmann", "scopus_author_id"=>"6505913007"}, {"first_name"=>"Krishna R.", "last_name"=>"Kalari", "scopus_author_id"=>"14828017500"}, {"first_name"=>"Brian", "last_name"=>"Bot", "scopus_author_id"=>"16021487100"}, {"first_name"=>"Jeanette E.", "last_name"=>"Eckel-Passow", "scopus_author_id"=>"9740003600"}, {"first_name"=>"Tiffany R.", "last_name"=>"Baker", "scopus_author_id"=>"41460966200"}, {"first_name"=>"Jennifer M.", "last_name"=>"Carr", "scopus_author_id"=>"37046749200"}, {"first_name"=>"Irina", "last_name"=>"Khrebtukova", "scopus_author_id"=>"35313453500"}, {"first_name"=>"Shujun", "last_name"=>"Luo", "scopus_author_id"=>"7401986127"}, {"first_name"=>"Lu", "last_name"=>"Zhang", "scopus_author_id"=>"35313608500"}, {"first_name"=>"Gary P.", "last_name"=>"Schroth", "scopus_author_id"=>"56473372200"}, {"first_name"=>"Edith A.", "last_name"=>"Perez", "scopus_author_id"=>"55416105700"}, {"first_name"=>"E. Aubrey", "last_name"=>"Thompson", "scopus_author_id"=>"56562355200"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-79952122331", "sgr"=>"79952122331", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0017490", "pmid"=>"21364760", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "pui"=>"361354807"}, "id"=>"02902533-9100-3dea-8db9-2bc770bfaa79", "abstract"=>"We used deep sequencing technology to profile the transcriptome, gene copy number, and CpG island methylation status simultaneously in eight commonly used breast cell lines to develop a model for how these genomic features are integrated in estrogen receptor positive (ER+) and negative breast cancer. Total mRNA sequence, gene copy number, and genomic CpG island methylation were carried out using the Illumina Genome Analyzer. Sequences were mapped to the human genome to obtain digitized gene expression data, DNA copy number in reference to the non-tumor cell line (MCF10A), and methylation status of 21,570 CpG islands to identify differentially expressed genes that were correlated with methylation or copy number changes. These were evaluated in a dataset from 129 primary breast tumors. Gene expression in cell lines was dominated by ER-associated genes. ER+ and ER- cell lines formed two distinct, stable clusters, and 1,873 genes were differentially expressed in the two groups. Part of chromosome 8 was deleted in all ER- cells and part of chromosome 17 amplified in all ER+ cells. These loci encoded 30 genes that were overexpressed in ER+ cells; 9 of these genes were overexpressed in ER+ tumors. We identified 149 differentially expressed genes that exhibited differential methylation of one or more CpG islands within 5 kb of the 5' end of the gene and for which mRNA abundance was inversely correlated with CpG island methylation status. In primary tumors we identified 84 genes that appear to be robust components of the methylation signature that we identified in ER+ cell lines. Our analyses reveal a global pattern of differential CpG island methylation that contributes to the transcriptome landscape of ER+ and ER- breast cancer cells and tumors. The role of gene amplification/deletion appears to more modest, although several potentially significant genes appear to be regulated by copy number aberrations.", "link"=>"http://www.mendeley.com/research/integrated-analysis-gene-expression-cpg-island-methylation-gene-copy-number-breast-cancer-cells-deep-1", "reader_count"=>169, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>9, "Researcher"=>61, "Student > Doctoral Student"=>12, "Student > Ph. D. Student"=>43, "Student > Postgraduate"=>7, "Student > Master"=>14, "Other"=>8, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>7}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>9, "Researcher"=>61, "Student > Doctoral Student"=>12, "Student > Ph. D. Student"=>43, "Student > Postgraduate"=>7, "Student > Master"=>14, "Other"=>8, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>7}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Agricultural and Biological Sciences"=>103, "Computer Science"=>9, "Decision Sciences"=>1, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>2, "Energy"=>1, "Engineering"=>2, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>24, "Mathematics"=>3, "Medicine and Dentistry"=>12, "Neuroscience"=>1, "Social Sciences"=>2, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>12}, "Social Sciences"=>{"Social Sciences"=>2}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>6}, "Environmental Science"=>{"Environmental Science"=>1}, "Engineering"=>{"Engineering"=>2}, "Neuroscience"=>{"Neuroscience"=>1}, "Energy"=>{"Energy"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>103}, "Computer Science"=>{"Computer Science"=>9}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>24}}, "reader_count_by_country"=>{"United States"=>8, "United Kingdom"=>1, "Spain"=>2, "Sweden"=>1, "Netherlands"=>1, "Turkey"=>1, "Belgium"=>1, "Norway"=>1, "Finland"=>1, "Denmark"=>1, "Brazil"=>1, "Italy"=>2, "Israel"=>1, "France"=>1, "Germany"=>3}, "group_count"=>15}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/796429"], "description"=>"<p><b>Panel A</b>: Unsupervised clustering of cell line data using 21,570 CpG islands, filtered for CMP methylation coverage as described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017490#s4\" target=\"_blank\">Materials and Methods</a>. The graded colors from red, orange, yellow, to white represent correlation from high to low among samples. <b>Panel B</b>: A heatmap of the top 100 differentially methylated CpG islands identified using the LIMMA model. The methylation data for each CpG island were standardized by mean among the samples where red represents hypermethylation and green hypomethylation. Genes associated with these CpG islands are indicated on the right of the figure.</p>", "links"=>[], "tags"=>["lines", "differential", "cpg"], "article_id"=>466787, "categories"=>["Molecular Biology", "Biological Sciences", "Cancer", "Genetics"], "users"=>["Zhifu Sun", "Yan W. Asmann", "Krishna R. Kalari", "Brian Bot", "Jeanette E. Eckel-Passow", "Tiffany R. Baker", "Jennifer M. Carr", "Irina Khrebtukova", "Shujun Luo", "Lu Zhang", "Gary P. Schroth", "Edith A. Perez", "E. Aubrey Thompson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017490.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ER_and_ER_8722_cell_lines_exhibit_differential_CpG_island_methylation_/466787", "title"=>"ER+ and ER− cell lines exhibit differential CpG island methylation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-02-25 01:53:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/796796"], "description"=>"<p>The heatmap was generated from GSEA analysis in which microarray data from 76 ER+ and 53 ER− tumors were interrogated with a geneset consisting of 149 genes that were differentially expressed and inversely methylated plus 30 genes that were overexpressed in ER+ cells and amplified in ER+ cells or deleted in ER− cells.</p>", "links"=>[], "tags"=>["genes", "cellular", "analyses"], "article_id"=>467150, "categories"=>["Molecular Biology", "Biological Sciences", "Cancer", "Genetics"], "users"=>["Zhifu Sun", "Yan W. Asmann", "Krishna R. Kalari", "Brian Bot", "Jeanette E. Eckel-Passow", "Tiffany R. Baker", "Jennifer M. Carr", "Irina Khrebtukova", "Shujun Luo", "Lu Zhang", "Gary P. Schroth", "Edith A. Perez", "E. Aubrey Thompson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017490.g006", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Expression_of_focus_genes_from_cellular_analyses_in_primary_human_breast_cancer_/467150", "title"=>"Expression of focus genes from cellular analyses in primary human breast cancer.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-02-25 01:59:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/796964"], "description"=>"<p><b>Panel A</b>: <i>C6orf97</i> (with <i>SYNE1</i> and <i>ESR1</i>); <b>Panel B</b>: <i>GATA3</i>; <b>Panel C</b>: <i>LYN</i>; and <b>Panel D</b>: <i>CPNE8</i>. On each figure, gene expression track represents the log2 fold change of gene expression between ER+ and ER− cell lines, red for up-regulation and blue for down-regulation in ER+ cell lines. Below that track is the methylation data for each cell line, which shows the average percent of methylated CpGs (dynamic range 0–100%) in the CpG Islands that were interrogated in this analysis.</p>", "links"=>[], "tags"=>["examples", "methylation", "mrna", "abundance", "genes", "differentially", "methylated", "lines"], "article_id"=>467311, "categories"=>["Molecular Biology", "Biological Sciences", "Cancer", "Genetics"], "users"=>["Zhifu Sun", "Yan W. Asmann", "Krishna R. Kalari", "Brian Bot", "Jeanette E. Eckel-Passow", "Tiffany R. Baker", "Jennifer M. Carr", "Irina Khrebtukova", "Shujun Luo", "Lu Zhang", "Gary P. Schroth", "Edith A. Perez", "E. Aubrey Thompson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017490.g007", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Representative_examples_of_methylation_status_and_mRNA_abundance_for_genes_that_are_differentially_methylated_in_cell_lines_and_differentially_expressed_in_both_cell_lines_and_tumors_/467311", "title"=>"Representative examples of methylation status and mRNA abundance for genes that are differentially methylated in cell lines and differentially expressed in both cell lines and tumors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-02-25 02:01:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/397752", "https://ndownloader.figshare.com/files/397768", "https://ndownloader.figshare.com/files/397830", "https://ndownloader.figshare.com/files/397850", "https://ndownloader.figshare.com/files/397871", "https://ndownloader.figshare.com/files/397890"], "description"=>"<div><p>We used deep sequencing technology to profile the transcriptome, gene copy number, and CpG island methylation status simultaneously in eight commonly used breast cell lines to develop a model for how these genomic features are integrated in estrogen receptor positive (ER+) and negative breast cancer. Total mRNA sequence, gene copy number, and genomic CpG island methylation were carried out using the Illumina Genome Analyzer. Sequences were mapped to the human genome to obtain digitized gene expression data, DNA copy number in reference to the non-tumor cell line (MCF10A), and methylation status of 21,570 CpG islands to identify differentially expressed genes that were correlated with methylation or copy number changes. These were evaluated in a dataset from 129 primary breast tumors. Gene expression in cell lines was dominated by ER-associated genes. ER+ and ER− cell lines formed two distinct, stable clusters, and 1,873 genes were differentially expressed in the two groups. Part of chromosome 8 was deleted in all ER− cells and part of chromosome 17 amplified in all ER+ cells. These loci encoded 30 genes that were overexpressed in ER+ cells; 9 of these genes were overexpressed in ER+ tumors. We identified 149 differentially expressed genes that exhibited differential methylation of one or more CpG islands within 5 kb of the 5′ end of the gene and for which mRNA abundance was inversely correlated with CpG island methylation status. In primary tumors we identified 84 genes that appear to be robust components of the methylation signature that we identified in ER+ cell lines. Our analyses reveal a global pattern of differential CpG island methylation that contributes to the transcriptome landscape of ER+ and ER− breast cancer cells and tumors. The role of gene amplification/deletion appears to more modest, although several potentially significant genes appear to be regulated by copy number aberrations.</p> </div>", "links"=>[], "tags"=>["cpg", "cancer", "cells", "sequencing"], "article_id"=>138577, "categories"=>["Molecular Biology", "Biological Sciences", "Cancer", "Genetics"], "users"=>["Zhifu Sun", "Yan W. Asmann", "Krishna R. Kalari", "Brian Bot", "Jeanette E. Eckel-Passow", "Tiffany R. Baker", "Jennifer M. Carr", "Irina Khrebtukova", "Shujun Luo", "Lu Zhang", "Gary P. Schroth", "Edith A. Perez", "E. Aubrey Thompson"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017490.s001", "https://dx.doi.org/10.1371/journal.pone.0017490.s002", "https://dx.doi.org/10.1371/journal.pone.0017490.s003", "https://dx.doi.org/10.1371/journal.pone.0017490.s004", "https://dx.doi.org/10.1371/journal.pone.0017490.s005", "https://dx.doi.org/10.1371/journal.pone.0017490.s006"], "stats"=>{"downloads"=>19, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Integrated_Analysis_of_Gene_Expression_CpG_Island_Methylation_and_Gene_Copy_Number_in_Breast_Cancer_Cells_by_Deep_Sequencing/138577", "title"=>"Integrated Analysis of Gene Expression, CpG Island Methylation, and Gene Copy Number in Breast Cancer Cells by Deep Sequencing", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-02-25 02:22:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/797050"], "description"=>"<p>Genes in bold face were also differentially expressed between 76 ER+ and 53 ER− breast tumors and were consistent with cell line expression and methylation data. Among these, 67 were overexpressed in ER+ tumors and 17 were overexpressed in ER− tumors. Detailed gene expression and methylation data for the 149 genes (153 CpG islands) in the cell lines can be found in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017490#pone.0017490.s004\" target=\"_blank\">Table S4</a>.</p>", "links"=>[], "tags"=>["genes", "differentially", "inversely", "correlated", "cpg"], "article_id"=>467410, "categories"=>["Molecular Biology", "Biological Sciences", "Cancer", "Genetics"], "users"=>["Zhifu Sun", "Yan W. Asmann", "Krishna R. Kalari", "Brian Bot", "Jeanette E. Eckel-Passow", "Tiffany R. Baker", "Jennifer M. Carr", "Irina Khrebtukova", "Shujun Luo", "Lu Zhang", "Gary P. Schroth", "Edith A. Perez", "E. Aubrey Thompson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017490.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_149_genes_differentially_expressed_and_inversely_correlated_with_CpG_island_methylation_/467410", "title"=>"149 genes differentially expressed and inversely correlated with CpG island methylation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-02-25 02:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/796616"], "description"=>"<p><b>Panel A</b>: A histogram of the number of statistically significant CNAs identified in each cell line. <b>Panel B</b>: IGV (Integrative Genomics Viewer) view of copy number aberrations for the region of chromosome 8 that is deleted in all three ER− cell lines. <b>Panel C</b>: Genomic view of copy number aberrations for the regions of chromosome 17 with amplification in all four ER+ cell lines. The symbols and abbreviations in Panels B and C are as follows: CNA - the copy number aberration track for each individual cell line; red represents amplification, blue deletion, and gray no change. Gene expression – the differential gene expression track; red represents overexpression in ER+ cells, blue represents overexpression in ER− cells, shown as log2 fold change.</p>", "links"=>[], "tags"=>["aberrations", "differential"], "article_id"=>466979, "categories"=>["Molecular Biology", "Biological Sciences", "Cancer", "Genetics"], "users"=>["Zhifu Sun", "Yan W. Asmann", "Krishna R. Kalari", "Brian Bot", "Jeanette E. Eckel-Passow", "Tiffany R. Baker", "Jennifer M. Carr", "Irina Khrebtukova", "Shujun Luo", "Lu Zhang", "Gary P. Schroth", "Edith A. Perez", "E. Aubrey Thompson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017490.g005", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_copy_number_aberrations_are_associated_with_differential_gene_expression_/466979", "title"=>"Gene copy number aberrations are associated with differential gene expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-02-25 01:56:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/796172"], "description"=>"<p><b>Panel A</b>: A box plot of total read normalized (RPM) log2 transformed data for 7 breast cancer cell lines. <b>Panel B</b>: RPM mean versus standard deviation (SD) of 7 cell lines showing variation is much higher in low abundance transcripts. Log2 = 0 corresponds to ∼1 RPM or about 50 raw counts. <b>Panel C</b>: An unsupervised clustering using all genes for 7 cell lines. The graded colors from red, orange, to yellow represent correlation from high to low among samples. ER+ and ER− cell lines are in two different clusters. <b>Panel D</b>: A volcano plot for differentially expressed genes identified using LIMMA statistical model. The red circles indicate genes significant at p<0.05 and fold change >1.5. <b>Panel E</b>: p-value distribution of all genes in the analysis indicates that p-values are not uniformly distributed, as would be predicted if the distribution of p-values were random. Random frequency distribution was approximated by assuming that if the distribution were random, the p-values for individual genes would be uniformly (equally) distributed across the different bins of p-values. From this assumption, we estimated the number of genes that would distribute to each bin by dividing the total number of genes by 20 bins. This calculation estimates a random frequency of ∼624 genes in each p-value bin simply by chance, as indicated by the dashed line. <b>Panel F</b>: A heatmap showing the strict assortment of ER+ and ER− tumors based on the top 100 differentially expressed genes identified using the LIMMA model. Gene expression was standardized by the mean among the samples, red indicating higher expression and green for lower expression.</p>", "links"=>[], "tags"=>["mrna", "identifies", "cohort", "genes", "differentially"], "article_id"=>466517, "categories"=>["Molecular Biology", "Biological Sciences", "Cancer", "Genetics"], "users"=>["Zhifu Sun", "Yan W. Asmann", "Krishna R. Kalari", "Brian Bot", "Jeanette E. Eckel-Passow", "Tiffany R. Baker", "Jennifer M. Carr", "Irina Khrebtukova", "Shujun Luo", "Lu Zhang", "Gary P. Schroth", "Edith A. Perez", "E. Aubrey Thompson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017490.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Total_mRNA_sequence_analysis_identifies_a_cohort_of_genes_that_are_differentially_expressed_in_ER_and_ER_8722_cell_lines_/466517", "title"=>"Total mRNA sequence analysis identifies a cohort of genes that are differentially expressed in ER+ and ER− cell lines.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-02-25 01:48:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/796327"], "description"=>"<p><b>Panel A</b>: A comparison of the abundance of three transcripts (ESR1, PGR, and ERBB2) measured by mRNA-seq (blue symbols) or qPCR (red symbols). <b>Panel B</b>: A correlation plot between mRNA-seq and NanoString for 236 cancer reference genes. Log2 RPM data for mRNA-seq and log2 NanoString data were used. R was used to calculate the Pearson correlation coefficient. <b>Panel C</b>: log2 fold change correlation between mRNA-seq and NanoString for 25 differentially expressed genes detected by NanoString.</p>", "links"=>[], "tags"=>["mrna-seq", "validate", "qpcr", "nanostring"], "article_id"=>466680, "categories"=>["Molecular Biology", "Biological Sciences", "Cancer", "Genetics"], "users"=>["Zhifu Sun", "Yan W. Asmann", "Krishna R. Kalari", "Brian Bot", "Jeanette E. Eckel-Passow", "Tiffany R. Baker", "Jennifer M. Carr", "Irina Khrebtukova", "Shujun Luo", "Lu Zhang", "Gary P. Schroth", "Edith A. Perez", "E. Aubrey Thompson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017490.g002", "stats"=>{"downloads"=>2, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_mRNA_seq_data_validate_in_comparison_to_qPCR_and_NanoString_data_/466680", "title"=>"The mRNA-seq data validate in comparison to qPCR and NanoString data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-02-25 01:51:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/797094"], "description"=>"<p>*mRNA-seq log2 fold change between 4 ER+ and 3 ER− cell lines.</p><p>**Genes in bold face are significantly up-regulated (p≤0.05) in ER+ tumors in the set of 129 breast tumor samples.</p>", "links"=>[], "tags"=>["differentially", "genes"], "article_id"=>467453, "categories"=>["Molecular Biology", "Biological Sciences", "Cancer", "Genetics"], "users"=>["Zhifu Sun", "Yan W. Asmann", "Krishna R. Kalari", "Brian Bot", "Jeanette E. Eckel-Passow", "Tiffany R. Baker", "Jennifer M. Carr", "Irina Khrebtukova", "Shujun Luo", "Lu Zhang", "Gary P. Schroth", "Edith A. Perez", "E. Aubrey Thompson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017490.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_30_differentially_expressed_genes_in_the_consistent_CAN_/467453", "title"=>"30 differentially expressed genes in the consistent CAN.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-02-25 02:04:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/796522"], "description"=>"<p><b>Panel A</b>: A scatter plot and trend line between fold change of gene expression and mean difference of methylation between ER+ and ER− cell lines. The Pearson correlation coefficient R is −0.75 [95%CI: −0.81, −0.68] with p-value<2.2e-16. <b>Panel B</b>: The distance from the start of each of the CpG islands that exhibited inverse correlation with mRNA abundance, illustrated in Figure A, to the start of the corresponding gene plotted against log2 gene expression fold change between ER+ and ER− cell lines. <b>Panel C</b>: A histogram representing the distribution of differentially methylated CpG islands in 149 differentially expressed genes.</p>", "links"=>[], "tags"=>["inverse", "methylation", "promoter-proximal", "cpg", "islands", "mrna"], "article_id"=>466879, "categories"=>["Molecular Biology", "Biological Sciences", "Cancer", "Genetics"], "users"=>["Zhifu Sun", "Yan W. Asmann", "Krishna R. Kalari", "Brian Bot", "Jeanette E. Eckel-Passow", "Tiffany R. Baker", "Jennifer M. Carr", "Irina Khrebtukova", "Shujun Luo", "Lu Zhang", "Gary P. Schroth", "Edith A. Perez", "E. Aubrey Thompson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017490.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_There_is_an_inverse_correlation_between_methylation_status_of_promoter_proximal_CpG_islands_and_mRNA_abundance_/466879", "title"=>"There is an inverse correlation between methylation status of promoter-proximal CpG islands and mRNA abundance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-02-25 01:54:39"}

PMC Usage Stats | Further Information

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Relative Metric

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