The CO-Regulation Database (CORD): A Tool to Identify Coordinately Expressed Genes
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{"title"=>"The CO-Regulation Database (CORD): A tool to identify coordinately expressed genes", "type"=>"journal", "authors"=>[{"first_name"=>"John P.", "last_name"=>"Fahrenbach", "scopus_author_id"=>"14522227800"}, {"first_name"=>"Jorge", "last_name"=>"Andrade", "scopus_author_id"=>"55981722700"}, {"first_name"=>"Elizabeth M.", "last_name"=>"McNally", "scopus_author_id"=>"7007073463"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84897125250", "sgr"=>"84897125250", "pui"=>"372724644", "pmid"=>"24599084", "doi"=>"10.1371/journal.pone.0090408"}, "id"=>"59601f28-5513-3e63-8302-862f4fea2e8e", "abstract"=>"BACKGROUND: Meta-analysis of gene expression array databases has the potential to reveal information about gene function. The identification of gene-gene interactions may be inferred from gene expression information but such meta-analysis is often limited to a single microarray platform. To address this limitation, we developed a gene-centered approach to analyze differential expression across thousands of gene expression experiments and created the CO-Regulation Database (CORD) to determine which genes are correlated with a queried gene.\\n\\nRESULTS: Using the GEO and ArrayExpress database, we analyzed over 120,000 group by group experiments from gene microarrays to determine the correlating genes for over 30,000 different genes or hypothesized genes. CORD output data is presented for sample queries with focus on genes with well-known interaction networks including p16 (CDKN2A), vimentin (VIM), MyoD (MYOD1). CDKN2A, VIM, and MYOD1 all displayed gene correlations consistent with known interacting genes.\\n\\nCONCLUSIONS: We developed a facile, web-enabled program to determine gene-gene correlations across different gene expression microarray platforms. Using well-characterized genes, we illustrate how CORD's identification of co-expressed genes contributes to a better understanding a gene's potential function. The website is found at http://cord-db.org.", "link"=>"http://www.mendeley.com/research/coregulation-database-cord-tool-identify-coordinately-expressed-genes", "reader_count"=>33, "reader_count_by_academic_status"=>{"Student > Doctoral Student"=>2, "Researcher"=>3, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>1, "Student > Bachelor"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Student > Doctoral Student"=>2, "Researcher"=>3, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>1, "Student > Bachelor"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>4, "Mathematics"=>1, "Agricultural and Biological Sciences"=>18, "Medicine and Dentistry"=>1, "Neuroscience"=>1, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>18}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"Colombia"=>1, "Belgium"=>1, "Sri Lanka"=>1, "China"=>1, "Luxembourg"=>1, "Poland"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1408255"], "description"=>"<p><b>A</b>) The differentiation of muscle stem cells (satellite cells) to myoblasts and ultimately to skeletal muscle is under the control of muscle regulatory factors including the transcription factor MyoD. CORD output for <i>MYOD1</i> demonstrates co- expression of other muscle regulatory factors like myogenin (<i>MYOG</i>) and many genes implicated in muscle differentiation. <b>B</b>) The MyoD1 correlated genes were over representative for several KEGG pathways relating to muscle such as “Cardiac muscle contraction” and “Dilated cardiomyopathy.”</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genetic networks", "Genome databases", "Genome expression analysis", "microarrays", "genetics", "gene expression", "Gene function", "Gene networks", "Gene prediction"], "article_id"=>952557, "categories"=>["Biological Sciences"], "users"=>["John P. Fahrenbach", "Jorge Andrade", "Elizabeth M. McNally"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090408.g005", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CORD_results_for_MYOD1_/952557", "title"=>"CORD results for <i>MYOD1</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-05 03:21:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1408254"], "description"=>"<p><b>A</b>) The epithelial-to-mesenchymal (EMT) and mesenchymal-to-epithelial transitions are important oncogenic pathways where vimentin (<i>VIM</i>) plays a central role. Twelve of the top 20 most correlated <i>VIM</i> genes affect the EMT transition. <b>B</b>) The EMT transitions depend heavily on cell adhesion. The <i>VIM</i>-correlated genes were over representative for several KEGG pathways in cell adhesion and cancer pathways such as “ECM-receptor interaction”, “Focal adhesion”, and “Pathways in cancer.”</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genetic networks", "Genome databases", "Genome expression analysis", "microarrays", "genetics", "gene expression", "Gene function", "Gene networks", "Gene prediction"], "article_id"=>952556, "categories"=>["Biological Sciences"], "users"=>["John P. Fahrenbach", "Jorge Andrade", "Elizabeth M. McNally"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090408.g004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CORD_results_for_VIM_/952556", "title"=>"CORD results for <i>VIM</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-05 03:21:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1408251"], "description"=>"<p><b>A</b>) A part of the microarray study E-MEXP-3167 from the ArrayExpress database. <b>B</b>) The samples were grouped together by either the Individual or Grouped Factor Method and all the groups were compared to one another. <b>C</b>) The differentially genes for each comparison was determined. Genes with multiple probes were reduced to one entry by averaging the fold change for the multiple probes. If the multiple probes for a gene were differentially regulated in the opposite direction, the gene was removed from the list of differentially expressed genes.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genetic networks", "Genome databases", "Genome expression analysis", "microarrays", "genetics", "gene expression", "Gene function", "Gene networks", "Gene prediction", "gene-centric"], "article_id"=>952553, "categories"=>["Biological Sciences"], "users"=>["John P. Fahrenbach", "Jorge Andrade", "Elizabeth M. McNally"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090408.g001", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Construction_of_CORD_database_using_a_gene_centric_approach_/952553", "title"=>"Construction of CORD database using a gene-centric approach.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-05 03:21:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1408263", "https://ndownloader.figshare.com/files/1408264", "https://ndownloader.figshare.com/files/1408265", "https://ndownloader.figshare.com/files/1408266"], "description"=>"<div><p>Background</p><p>Meta-analysis of gene expression array databases has the potential to reveal information about gene function. The identification of gene-gene interactions may be inferred from gene expression information but such meta-analysis is often limited to a single microarray platform. To address this limitation, we developed a gene-centered approach to analyze differential expression across thousands of gene expression experiments and created the CO-Regulation Database (CORD) to determine which genes are correlated with a queried gene.</p><p>Results</p><p>Using the GEO and ArrayExpress database, we analyzed over 120,000 group by group experiments from gene microarrays to determine the correlating genes for over 30,000 different genes or hypothesized genes. CORD output data is presented for sample queries with focus on genes with well-known interaction networks including p16 (<i>CDKN2A</i>), vimentin (<i>VIM)</i>, MyoD (<i>MYOD1</i>). <i>CDKN2A</i>, <i>VIM</i>, and <i>MYOD1</i> all displayed gene correlations consistent with known interacting genes.</p><p>Conclusions</p><p>We developed a facile, web-enabled program to determine gene-gene correlations across different gene expression microarray platforms. Using well-characterized genes, we illustrate how CORD's identification of co-expressed genes contributes to a better understanding a gene's potential function. The website is found at <a href=\"http://cord-db.org\" target=\"_blank\">http://cord-db.org</a>.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genetic networks", "Genome databases", "Genome expression analysis", "microarrays", "genetics", "gene expression", "Gene function", "Gene networks", "Gene prediction", "co-regulation", "coordinately"], "article_id"=>952561, "categories"=>["Biological Sciences"], "users"=>["John P. Fahrenbach", "Jorge Andrade", "Elizabeth M. McNally"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0090408.s001", "https://dx.doi.org/10.1371/journal.pone.0090408.s002", "https://dx.doi.org/10.1371/journal.pone.0090408.s003", "https://dx.doi.org/10.1371/journal.pone.0090408.s004"], "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_CO_Regulation_Database_CORD_A_Tool_to_Identify_Coordinately_Expressed_Genes_/952561", "title"=>"The CO-Regulation Database (CORD): A Tool to Identify Coordinately Expressed Genes", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-03-05 03:21:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1408256"], "description"=>"<p>Top 20 Genes Co-expressed with vimentin (<i>VIM</i>) identified by CORD.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genetic networks", "Genome databases", "Genome expression analysis", "microarrays", "genetics", "gene expression", "Gene function", "Gene networks", "Gene prediction", "20", "genes", "co-expressed", "vimentin"], "article_id"=>952558, "categories"=>["Biological Sciences"], "users"=>["John P. Fahrenbach", "Jorge Andrade", "Elizabeth M. McNally"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090408.t001", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Top_20_Genes_Co_expressed_with_vimentin_VIM_identified_by_CORD_/952558", "title"=>"Top 20 Genes Co-expressed with vimentin (<i>VIM</i>) identified by CORD.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-03-05 03:21:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1408253"], "description"=>"<p><b>A</b>) <i>CDKN2A</i> encoding p16 plays a significant role in the cell cycle by regulating the initiation of DNA replication. A simplified diagram shows select genes that play a major role in the cell cycle. CORD identifies many genes known to play major roles in the cell cycle by determining genes co-regulated with <i>CDKN2A</i> (bolded text.) <b>B</b>) The <i>CDKN2A</i>-correlated genes were over representative for several KEGG pathways in cancer and the cell cycle including “DNA replication”, “p53 signaling”, and “cell cycle.”</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genetic networks", "Genome databases", "Genome expression analysis", "microarrays", "genetics", "gene expression", "Gene function", "Gene networks", "Gene prediction"], "article_id"=>952555, "categories"=>["Biological Sciences"], "users"=>["John P. Fahrenbach", "Jorge Andrade", "Elizabeth M. McNally"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090408.g003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CORD_results_for_CDKN2A_/952555", "title"=>"CORD results for <i>CDKN2A</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-05 03:21:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1408252"], "description"=>"<p><b>A</b>) The list of co-regulated genes was determined for each gene using the Individual and Grouped Factor Method. The two gene lists were then compared to one another by determining the % overlap (similarity) of the lists for the top 10 to top 1000 most correlated genes. The % overlap reached a plateau at 47%. <b>B</b>) The first derivative of the % overlap vs. the gene list size shows that on average, after comparing the top 400 genes the lists are no longer similar. <b>C, D</b>) This analysis was repeated for randomly generated gene lists and showed no change in the rate of % overlap vs. gene list size. <b>E</b>) To determine how using the Individual or Grouped Factor method effected gene-gene correlation co-efficients, we analyzed the ratio of the correlation co-efficient for each gene-gene pair. A histogram of this data shows that on average, the Grouped Factor method yielded higher correlation co-efficients.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genetic networks", "Genome databases", "Genome expression analysis", "microarrays", "genetics", "gene expression", "Gene function", "Gene networks", "Gene prediction", "co-regulated"], "article_id"=>952554, "categories"=>["Biological Sciences"], "users"=>["John P. Fahrenbach", "Jorge Andrade", "Elizabeth M. McNally"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090408.g002", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Determination_of_co_regulated_genes_/952554", "title"=>"Determination of co-regulated genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-05 03:21:41"}

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Relative Metric

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