canEvolve: A Web Portal for Integrative Oncogenomics
Publication Date
February 13, 2013
Journal
PLOS ONE
Authors
Mehmet Kemal Samur, Zhenyu Yan, Xujun Wang, Qingyi Cao, et al
Volume
8
Issue
2
Pages
e56228
DOI
http://doi.org/10.1371/journal.pone.0056228
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0056228
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/23418540
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3572035
Europe PMC
http://europepmc.org/abstract/MED/23418540
Web of Science
000315970300132
Scopus
84873922708
Mendeley
http://www.mendeley.com/research/canevolve-web-portal-integrative-oncogenomics
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Mendeley | Further Information

{"title"=>"canEvolve: A Web Portal for Integrative Oncogenomics", "type"=>"journal", "authors"=>[{"first_name"=>"Mehmet Kemal", "last_name"=>"Samur", "scopus_author_id"=>"54385926500"}, {"first_name"=>"Zhenyu", "last_name"=>"Yan", "scopus_author_id"=>"55597392300"}, {"first_name"=>"Xujun", "last_name"=>"Wang", "scopus_author_id"=>"37666288400"}, {"first_name"=>"Qingyi", "last_name"=>"Cao", "scopus_author_id"=>"23993897800"}, {"first_name"=>"Nikhil C.", "last_name"=>"Munshi", "scopus_author_id"=>"7005171559"}, {"first_name"=>"Cheng", "last_name"=>"Li", "scopus_author_id"=>"36537305600"}, {"first_name"=>"Parantu K.", "last_name"=>"Shah", "scopus_author_id"=>"7403285072"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"368342548", "sgr"=>"84873922708", "issn"=>"19326203", "pmid"=>"23418540", "scopus"=>"2-s2.0-84873922708", "doi"=>"10.1371/journal.pone.0056228", "isbn"=>"1932-6203"}, "id"=>"2e3a765b-5a9b-330f-b764-71eb55329c63", "abstract"=>"BACKGROUND & OBJECTIVE: Genome-wide profiles of tumors obtained using functional genomics platforms are being deposited to the public repositories at an astronomical scale, as a result of focused efforts by individual laboratories and large projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium. Consequently, there is an urgent need for reliable tools that integrate and interpret these data in light of current knowledge and disseminate results to biomedical researchers in a user-friendly manner. We have built the canEvolve web portal to meet this need.\\n\\nRESULTS: canEvolve query functionalities are designed to fulfill most frequent analysis needs of cancer researchers with a view to generate novel hypotheses. canEvolve stores gene, microRNA (miRNA) and protein expression profiles, copy number alterations for multiple cancer types, and protein-protein interaction information. canEvolve allows querying of results of primary analysis, integrative analysis and network analysis of oncogenomics data. The querying for primary analysis includes differential gene and miRNA expression as well as changes in gene copy number measured with SNP microarrays. canEvolve provides results of integrative analysis of gene expression profiles with copy number alterations and with miRNA profiles as well as generalized integrative analysis using gene set enrichment analysis. The network analysis capability includes storage and visualization of gene co-expression, inferred gene regulatory networks and protein-protein interaction information. Finally, canEvolve provides correlations between gene expression and clinical outcomes in terms of univariate survival analysis.\\n\\nCONCLUSION: At present canEvolve provides different types of information extracted from 90 cancer genomics studies comprising of more than 10,000 patients. The presence of multiple data types, novel integrative analysis for identifying regulators of oncogenesis, network analysis and ability to query gene lists/pathways are distinctive features of canEvolve. canEvolve will facilitate integrative and meta-analysis of oncogenomics datasets.\\n\\nAVAILABILITY: The canEvolve web portal is available at http://www.canevolve.org/.", "link"=>"http://www.mendeley.com/research/canevolve-web-portal-integrative-oncogenomics", "reader_count"=>65, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>5, "Researcher"=>23, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>5, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>5, "Researcher"=>23, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>5, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Unspecified"=>3, "Biochemistry, Genetics and Molecular Biology"=>13, "Agricultural and Biological Sciences"=>28, "Medicine and Dentistry"=>10, "Chemistry"=>1, "Social Sciences"=>1, "Computer Science"=>6}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>10}, "Chemistry"=>{"Chemistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>28}, "Computer Science"=>{"Computer Science"=>6}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>13}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"Netherlands"=>1, "United States"=>2, "Denmark"=>1, "Germany"=>1, "Spain"=>2}, "group_count"=>7}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/490100"], "description"=>"<p>(A) Boxplots of the expression of gene MAP2K4 (X-axis) is plotted against groups of samples with different levels of copy number alteration of the MAP2K4 gene (Y-axis). Different mutation types of the BRCA gene in these samples are also indicated. (B) Similar to (A), but Y-axis represents the protein expression of gene ACC1, and X-axis and mutation points are represent the copy number abnormalities and mutation of TP53. Both (A) and (B) use the TCGA LUAD dataset.</p>", "links"=>[], "tags"=>["genomic", "abnormality"], "article_id"=>160621, "categories"=>["Cancer", "Biological Sciences", "Genetics"], "users"=>["Mehmet Kemal Samur", "Zhenyu Yan", "Xujun Wang", "Qingyi Cao", "Nikhil C. Munshi", "Cheng Li", "Parantu K. Shah"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056228.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Visualization_of_the_association_between_genomic_abnormality_and_gene_or_protein_expression_/160621", "title"=>"Visualization of the association between genomic abnormality and gene or protein expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:10:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/490434"], "description"=>"<p>Identification of transcription factors and miRNAs whose target genes significantly change their overall expression compared to normal plasma cells during the evolution of myeloma from MGUS to relapsed stages by gene set enrichment analysis. The X-axis shows different evolutionary stages of myeloma. The Y-axis shows the –log10 (p-value) from the gene set enrichment analysis using the target genes of a TF or a miRNA based on MSigDB.</p>", "links"=>[], "tags"=>["myeloma"], "article_id"=>160955, "categories"=>["Cancer", "Biological Sciences", "Genetics"], "users"=>["Mehmet Kemal Samur", "Zhenyu Yan", "Xujun Wang", "Qingyi Cao", "Nikhil C. Munshi", "Cheng Li", "Parantu K. Shah"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056228.g007", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Modeling_of_multiple_myeloma_evolution_/160955", "title"=>"Modeling of multiple myeloma evolution.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:15:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/490593"], "description"=>"<p>Literature references to the analysis algorithms are provided in the main text.</p>", "links"=>[], "tags"=>["algorithms", "datasets"], "article_id"=>161114, "categories"=>["Cancer", "Biological Sciences", "Genetics"], "users"=>["Mehmet Kemal Samur", "Zhenyu Yan", "Xujun Wang", "Qingyi Cao", "Nikhil C. Munshi", "Cheng Li", "Parantu K. Shah"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056228.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Data_analysis_algorithms_and_total_analyzed_datasets_in_canEvolve_/161114", "title"=>"Data analysis algorithms and total analyzed datasets in canEvolve.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-13 00:18:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/489898"], "description"=>"<p>(A) The Kaplan-Meier plot on the lower-right shows the survival impact of E2F2 gene expression in the multiple myeloma dataset GSE2658. Two expression groups are defined using the median E2F2 gene expression across all samples as the splitting value, and log-rank test is used to compute the p-value. Higher expression of E2F2 leads to high-risk (red) while lower expression leads to lower risk (blue).</p>", "links"=>[], "tags"=>["interface", "visualization"], "article_id"=>160413, "categories"=>["Cancer", "Biological Sciences", "Genetics"], "users"=>["Mehmet Kemal Samur", "Zhenyu Yan", "Xujun Wang", "Qingyi Cao", "Nikhil C. Munshi", "Cheng Li", "Parantu K. Shah"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056228.g002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Query_interface_and_visualization_of_survival_analysis_/160413", "title"=>"Query interface and visualization of survival analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:06:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/490512"], "description"=>"<p>The number of datasets for different data types in canEvolve.</p>", "links"=>[], "tags"=>["datasets", "types"], "article_id"=>161032, "categories"=>["Cancer", "Biological Sciences", "Genetics"], "users"=>["Mehmet Kemal Samur", "Zhenyu Yan", "Xujun Wang", "Qingyi Cao", "Nikhil C. Munshi", "Cheng Li", "Parantu K. Shah"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056228.t001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_number_of_datasets_for_different_data_types_in_canEvolve_/161032", "title"=>"The number of datasets for different data types in canEvolve.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-13 00:17:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/485244", "https://ndownloader.figshare.com/files/485245", "https://ndownloader.figshare.com/files/485248", "https://ndownloader.figshare.com/files/485251"], "description"=>"<div><h3>Background & Objective</h3><p>Genome-wide profiles of tumors obtained using functional genomics platforms are being deposited to the public repositories at an astronomical scale, as a result of focused efforts by individual laboratories and large projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium. Consequently, there is an urgent need for reliable tools that integrate and interpret these data in light of current knowledge and disseminate results to biomedical researchers in a user-friendly manner. We have built the canEvolve web portal to meet this need.</p> <h3>Results</h3><p>canEvolve query functionalities are designed to fulfill most frequent analysis needs of cancer researchers with a view to generate novel hypotheses. canEvolve stores gene, microRNA (miRNA) and protein expression profiles, copy number alterations for multiple cancer types, and protein-protein interaction information. canEvolve allows querying of results of primary analysis, integrative analysis and network analysis of oncogenomics data. The querying for primary analysis includes differential gene and miRNA expression as well as changes in gene copy number measured with SNP microarrays. canEvolve provides results of integrative analysis of gene expression profiles with copy number alterations and with miRNA profiles as well as generalized integrative analysis using gene set enrichment analysis. The network analysis capability includes storage and visualization of gene co-expression, inferred gene regulatory networks and protein-protein interaction information. Finally, canEvolve provides correlations between gene expression and clinical outcomes in terms of univariate survival analysis.</p> <h3>Conclusion</h3><p>At present canEvolve provides different types of information extracted from 90 cancer genomics studies comprising of more than 10,000 patients. The presence of multiple data types, novel integrative analysis for identifying regulators of oncogenesis, network analysis and ability to query gene lists/pathways are distinctive features of canEvolve. canEvolve will facilitate integrative and meta-analysis of oncogenomics datasets.</p> <h3>Availability</h3><p>The canEvolve web portal is available at <a href=\"http://www.canevolve.org/\">http://www.canevolve.org/</a>.</p> </div>", "links"=>[], "tags"=>["portal", "integrative", "oncogenomics"], "article_id"=>156785, "categories"=>["Cancer", "Biological Sciences", "Genetics"], "users"=>["Mehmet Kemal Samur", "Zhenyu Yan", "Xujun Wang", "Qingyi Cao", "Nikhil C. Munshi", "Cheng Li", "Parantu K. Shah"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0056228.s001", "https://dx.doi.org/10.1371/journal.pone.0056228.s002", "https://dx.doi.org/10.1371/journal.pone.0056228.s003", "https://dx.doi.org/10.1371/journal.pone.0056228.s004"], "stats"=>{"downloads"=>3, "page_views"=>46, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/canEvolve_A_Web_Portal_for_Integrative_Oncogenomics__/156785", "title"=>"canEvolve: A Web Portal for Integrative Oncogenomics", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-02-13 01:53:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/490559"], "description"=>"<p>Top 10 genes identified from integrative analysis of copy number profiles with gene expression profiles from the multiple myeloma dataset GSE26863 <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056228#pone.0056228-Chapman1\" target=\"_blank\">[39]</a>.</p>", "links"=>[], "tags"=>["10", "genes", "integrative", "profiles", "myeloma", "dataset", "gse26863"], "article_id"=>161072, "categories"=>["Cancer", "Biological Sciences", "Genetics"], "users"=>["Mehmet Kemal Samur", "Zhenyu Yan", "Xujun Wang", "Qingyi Cao", "Nikhil C. Munshi", "Cheng Li", "Parantu K. Shah"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056228.t003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Top_10_genes_identified_from_integrative_analysis_of_copy_number_profiles_with_gene_expression_profiles_from_the_multiple_myeloma_dataset_GSE26863_39_/161072", "title"=>"Top 10 genes identified from integrative analysis of copy number profiles with gene expression profiles from the multiple myeloma dataset GSE26863 [39].", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-13 00:17:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/490192"], "description"=>"<p>The summary bubble-bar plot from GemiNI analysis using the lung cancer dataset GSE18805 to identifies candidate transcription factors, miRNAs, and TF-miRNA feed-forward loops (FFL) involved in cancer pathogenesis. TFs and miRNAs are ranked by the percentage of normal-cancer differentially expressed genes explained by all the significant FFLs involving a TF or miRNA (the height of bars). The top 20 TFs and miRNAs are displayed. The bubble size indicates the number of differentially expressed FFL target genes, and color indicates the FFL significance. For more details on the figure and the methodology see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056228#pone.0056228-Yan2\" target=\"_blank\">[28]</a>.</p>", "links"=>[], "tags"=>["integrating", "mirna"], "article_id"=>160712, "categories"=>["Cancer", "Biological Sciences", "Genetics"], "users"=>["Mehmet Kemal Samur", "Zhenyu Yan", "Xujun Wang", "Qingyi Cao", "Nikhil C. Munshi", "Cheng Li", "Parantu K. Shah"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056228.g005", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_dChip_GemiNI_analysis_integrating_gene_expression_with_miRNA_expression_/160712", "title"=>"dChip-GemiNI analysis integrating gene expression with miRNA expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:11:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/489766"], "description"=>"<p>(A) Visualization of differential gene expression for B-Cell Activation pathway members in normal versus multiple myeloma (MM) comparison using the GSE6477 data. (B) Heatmap of differential miRNA expression in normal versus MM comparison using the GSE16558 data. The MM samples are a subset that has no cytological abnormalities.</p>", "links"=>[], "tags"=>["interface", "visualization"], "article_id"=>160283, "categories"=>["Cancer", "Biological Sciences", "Genetics"], "users"=>["Mehmet Kemal Samur", "Zhenyu Yan", "Xujun Wang", "Qingyi Cao", "Nikhil C. Munshi", "Cheng Li", "Parantu K. Shah"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056228.g001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Query_interface_and_visualization_of_primary_analysis_/160283", "title"=>"Query interface and visualization of primary analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:04:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/490331"], "description"=>"<p>(A) The colors in the heatmap show the Fisher's Exact test p-value for the enrichment of differentially expressed genes between normal-cancer comparisons (X-axis) in a KEGG or Biocarta pathway (Y-axis). (B) Similar to (A), but gene sets on the X-axis are selected for their significant correlation with survival using the cox proportional hazards model.</p>", "links"=>[], "tags"=>["studies"], "article_id"=>160859, "categories"=>["Cancer", "Biological Sciences", "Genetics"], "users"=>["Mehmet Kemal Samur", "Zhenyu Yan", "Xujun Wang", "Qingyi Cao", "Nikhil C. Munshi", "Cheng Li", "Parantu K. Shah"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056228.g006", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Meta_analysis_of_multiple_studies_in_canEvolve_/160859", "title"=>"Meta-analysis of multiple studies in canEvolve.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:14:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/490015"], "description"=>"<p>(A) ARACNE reconstructed gene regulatory network for the transcription factor SP1 using the multiple myeloma dataset GSE6477. (B) Experimentally validated and predicted Human protein-protein interaction network of SP1 derived from the STRING database at the threshold of 0.993. The three lines connecting SP1 to different proteins show distinct evidence types as used by STRING.</p>", "links"=>[], "tags"=>["visualization", "interfacing", "cytoscape"], "article_id"=>160534, "categories"=>["Cancer", "Biological Sciences", "Genetics"], "users"=>["Mehmet Kemal Samur", "Zhenyu Yan", "Xujun Wang", "Qingyi Cao", "Nikhil C. Munshi", "Cheng Li", "Parantu K. Shah"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0056228.g003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_visualization_by_interfacing_Cytoscape_from_canEvolve_/160534", "title"=>"Network visualization by interfacing Cytoscape from canEvolve.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-13 00:08:54"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Biology and life sciences/Molecular biology", "average_usage"=>[272, 466, 589, 702, 806, 903, 995, 1086, 1176, 1258, 1347, 1422, 1493]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[297, 488, 616, 724, 828, 939, 1038, 1127, 1223, 1311, 1393, 1479, 1556]}]}
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