Maize Gene Atlas Developed by RNA Sequencing and Comparative Evaluation of Transcriptomes Based on RNA Sequencing and Microarrays
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{"title"=>"Maize Gene Atlas Developed by RNA Sequencing and Comparative Evaluation of Transcriptomes Based on RNA Sequencing and Microarrays", "type"=>"journal", "authors"=>[{"first_name"=>"Rajandeep S.", "last_name"=>"Sekhon", "scopus_author_id"=>"8324368300"}, {"first_name"=>"Roman", "last_name"=>"Briskine", "scopus_author_id"=>"53866053500"}, {"first_name"=>"Candice N.", "last_name"=>"Hirsch", "scopus_author_id"=>"55621571300"}, {"first_name"=>"Chad L.", "last_name"=>"Myers", "scopus_author_id"=>"7202314011"}, {"first_name"=>"Nathan M.", "last_name"=>"Springer", "scopus_author_id"=>"7004439661"}, {"first_name"=>"C. Robin", "last_name"=>"Buell", "scopus_author_id"=>"7003436847"}, {"first_name"=>"Natalia", "last_name"=>"de Leon", "scopus_author_id"=>"8656300700"}, {"first_name"=>"Shawn M.", "last_name"=>"Kaeppler", "scopus_author_id"=>"6701732823"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84876533257", "sgr"=>"84876533257", "pui"=>"368792887", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "pmid"=>"23637782", "doi"=>"10.1371/journal.pone.0061005"}, "id"=>"fe025075-5c4c-3b3a-8170-037a8f6e8871", "abstract"=>"Transcriptome analysis is a valuable tool for identification and characterization of genes and pathways underlying plant growth and development. We previously published a microarray-based maize gene atlas from the analysis of 60 unique spatially and temporally separated tissues from 11 maize organs [1]. To enhance the coverage and resolution of the maize gene atlas, we have analyzed 18 selected tissues representing five organs using RNA sequencing (RNA-Seq). For a direct comparison of the two methodologies, the same RNA samples originally used for our microarray-based atlas were evaluated using RNA-Seq. Both technologies produced similar transcriptome profiles as evident from high Pearson's correlation statistics ranging from 0.70 to 0.83, and from nearly identical clustering of the tissues. RNA-Seq provided enhanced coverage of the transcriptome, with 82.1% of the filtered maize genes detected as expressed in at least one tissue by RNA-Seq compared to only 56.5% detected by microarrays. Further, from the set of 465 maize genes that have been historically well characterized by mutant analysis, 427 show significant expression in at least one tissue by RNA-Seq compared to 390 by microarray analysis. RNA-Seq provided higher resolution for identifying tissue-specific expression as well as for distinguishing the expression profiles of closely related paralogs as compared to microarray-derived profiles. Co-expression analysis derived from the microarray and RNA-Seq data revealed that broadly similar networks result from both platforms, and that co-expression estimates are stable even when constructed from mixed data including both RNA-Seq and microarray expression data. The RNA-Seq information provides a useful complement to the microarray-based maize gene atlas and helps to further understand the dynamics of transcription during maize development.", "link"=>"http://www.mendeley.com/research/maize-gene-atlas-developed-rna-sequencing-comparative-evaluation-transcriptomes-based-rna-sequencing", "reader_count"=>145, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>8, "Researcher"=>49, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>49, "Student > Postgraduate"=>7, "Student > Master"=>12, "Other"=>5, "Student > Bachelor"=>4, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>8, "Researcher"=>49, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>49, "Student > Postgraduate"=>7, "Student > Master"=>12, "Other"=>5, "Student > Bachelor"=>4, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Biochemistry, Genetics and Molecular Biology"=>13, "Agricultural and Biological Sciences"=>116, "Medicine and Dentistry"=>2, "Computer Science"=>7, "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"=>116}, "Computer Science"=>{"Computer Science"=>7}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>13}, "Unspecified"=>{"Unspecified"=>6}}, "reader_count_by_country"=>{"Canada"=>1, "Argentina"=>1, "Belgium"=>1, "Hungary"=>1, "United States"=>8, "China"=>1, "Russia"=>1, "India"=>1, "Spain"=>2}, "group_count"=>5}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1032744"], "description"=>"<p>Correlations between RNA-Seq and microarray-based expression values.</p>", "links"=>[], "tags"=>["genetics", "Plant genetics", "Crop genetics", "Gene networks", "genomics", "Genome analysis tools", "Genetic networks", "transcriptomes", "Functional genomics", "Genome expression analysis", "Plant science", "plants", "Flowering plants", "Plant genomics", "rna-seq", "microarray-based"], "article_id"=>688940, "categories"=>["Biological Sciences"], "users"=>["Rajandeep S. Sekhon", "Roman Briskine", "Candice N. Hirsch", "Chad L. Myers", "Nathan M. Springer", "C. Robin Buell", "Natalia de Leon", "Shawn M. Kaeppler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061005.t002", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlations_between_RNA_Seq_and_microarray_based_expression_values_/688940", "title"=>"Correlations between RNA-Seq and microarray-based expression values.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-04-23 02:29:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1032741"], "description"=>"<p>(A) The density of Fisher-transformed and normalized edge weights are shown for both the microarray (y-axis) and RNA-Seq (x-axis) co-expression networks. (B) The frequency of correlation coefficient (R) values for a series of 1000 random co-expression networks is plotted relative to the observed value (red arrow). The random co-expression networks were generated by selecting a mixture of RNA-Seq and microarray data for each of the two networks.</p>", "links"=>[], "tags"=>["genetics", "Plant genetics", "Crop genetics", "Gene networks", "genomics", "Genome analysis tools", "Genetic networks", "transcriptomes", "Functional genomics", "Genome expression analysis", "Plant science", "plants", "Flowering plants", "Plant genomics", "rna-seq", "microarray", "co-expression"], "article_id"=>688937, "categories"=>["Biological Sciences"], "users"=>["Rajandeep S. Sekhon", "Roman Briskine", "Candice N. Hirsch", "Chad L. Myers", "Nathan M. Springer", "C. Robin Buell", "Natalia de Leon", "Shawn M. Kaeppler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061005.g005", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_RNA_Seq_and_microarray_co_expression_networks_/688937", "title"=>"Comparison of RNA-Seq and microarray co-expression networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-23 02:28:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1032739"], "description"=>"<p>Distribution of the entropy values is shown for both Microarray and RNA-Seq datasets. Tissue-specific expression patterns are more prevalent in the RNA-Seq dataset (Mann-Whitney U test, p<0.01) indicating higher sensitivity of the platform to the expression differences between genes.</p>", "links"=>[], "tags"=>["genetics", "Plant genetics", "Crop genetics", "Gene networks", "genomics", "Genome analysis tools", "Genetic networks", "transcriptomes", "Functional genomics", "Genome expression analysis", "Plant science", "plants", "Flowering plants", "Plant genomics", "entropy", "calculated"], "article_id"=>688935, "categories"=>["Biological Sciences"], "users"=>["Rajandeep S. Sekhon", "Roman Briskine", "Candice N. Hirsch", "Chad L. Myers", "Nathan M. Springer", "C. Robin Buell", "Natalia de Leon", "Shawn M. Kaeppler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061005.g003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Shannon_entropy_was_calculated_for_each_gene_expression_profile_to_assess_their_tissue_specificity_/688935", "title"=>"Shannon entropy was calculated for each gene expression profile to assess their tissue specificity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-23 02:28:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/1032740"], "description"=>"<p>A. Expression patterns of endosperm-specific <i>Brittle-2</i> (<i>Bt2</i>) gene B. Expression patterns of leaf specific <i>Agpslzm/L</i> gene. C. Expression patterns of five individual probes representing <i>Agpslzm/L</i> gene. Sequence differences of each of the 60-mer probes from the paralogous <i>Bt2</i>, shown as number of mismatches, are in the inset of each graph.</p>", "links"=>[], "tags"=>["genetics", "Plant genetics", "Crop genetics", "Gene networks", "genomics", "Genome analysis tools", "Genetic networks", "transcriptomes", "Functional genomics", "Genome expression analysis", "Plant science", "plants", "Flowering plants", "Plant genomics", "efficiencies", "rna-seq", "microarray", "discerning", "paralogous"], "article_id"=>688936, "categories"=>["Biological Sciences"], "users"=>["Rajandeep S. Sekhon", "Roman Briskine", "Candice N. Hirsch", "Chad L. Myers", "Nathan M. Springer", "C. Robin Buell", "Natalia de Leon", "Shawn M. Kaeppler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061005.g004", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relative_efficiencies_of_RNA_Seq_and_microarray_in_discerning_expression_of_two_paralogous_genes_/688936", "title"=>"Relative efficiencies of RNA-Seq and microarray in discerning expression of two paralogous genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-23 02:28:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1032738"], "description"=>"<p>PCA was performed independently for both datasets. First principal component (PC1) is shown on x-axis while the second principal component (PC2) is shown on y-axis. Tissues belonging to same organ group are represented by different colors of the same shape.</p>", "links"=>[], "tags"=>["genetics", "Plant genetics", "Crop genetics", "Gene networks", "genomics", "Genome analysis tools", "Genetic networks", "transcriptomes", "Functional genomics", "Genome expression analysis", "Plant science", "plants", "Flowering plants", "Plant genomics", "similarities", "transcriptome", "profiles", "produced", "rna-seq", "microarray"], "article_id"=>688934, "categories"=>["Biological Sciences"], "users"=>["Rajandeep S. Sekhon", "Roman Briskine", "Candice N. Hirsch", "Chad L. Myers", "Nathan M. Springer", "C. Robin Buell", "Natalia de Leon", "Shawn M. Kaeppler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061005.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Principal_Component_Analysis_PCA_showing_similarities_between_transcriptome_profiles_produced_by_RNA_Seq_A_and_microarray_B_/688934", "title"=>"Principal Component Analysis (PCA) showing similarities between transcriptome profiles produced by RNA-Seq (A) and microarray (B).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-23 02:28:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/1032748", "https://ndownloader.figshare.com/files/1032749", "https://ndownloader.figshare.com/files/1032750", "https://ndownloader.figshare.com/files/1032751", "https://ndownloader.figshare.com/files/1032752", "https://ndownloader.figshare.com/files/1032753", "https://ndownloader.figshare.com/files/1032754", "https://ndownloader.figshare.com/files/1032755", "https://ndownloader.figshare.com/files/1032756", "https://ndownloader.figshare.com/files/1032757", "https://ndownloader.figshare.com/files/1032758"], "description"=>"<div><p>Transcriptome analysis is a valuable tool for identification and characterization of genes and pathways underlying plant growth and development. We previously published a microarray-based maize gene atlas from the analysis of 60 unique spatially and temporally separated tissues from 11 maize organs <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061005#pone.0061005-Sekhon1\" target=\"_blank\">[1]</a>. To enhance the coverage and resolution of the maize gene atlas, we have analyzed 18 selected tissues representing five organs using RNA sequencing (RNA-Seq). For a direct comparison of the two methodologies, the same RNA samples originally used for our microarray-based atlas were evaluated using RNA-Seq. Both technologies produced similar transcriptome profiles as evident from high Pearson's correlation statistics ranging from 0.70 to 0.83, and from nearly identical clustering of the tissues. RNA-Seq provided enhanced coverage of the transcriptome, with 82.1% of the filtered maize genes detected as expressed in at least one tissue by RNA-Seq compared to only 56.5% detected by microarrays. Further, from the set of 465 maize genes that have been historically well characterized by mutant analysis, 427 show significant expression in at least one tissue by RNA-Seq compared to 390 by microarray analysis. RNA-Seq provided higher resolution for identifying tissue-specific expression as well as for distinguishing the expression profiles of closely related paralogs as compared to microarray-derived profiles. Co-expression analysis derived from the microarray and RNA-Seq data revealed that broadly similar networks result from both platforms, and that co-expression estimates are stable even when constructed from mixed data including both RNA-Seq and microarray expression data. The RNA-Seq information provides a useful complement to the microarray-based maize gene atlas and helps to further understand the dynamics of transcription during maize development.</p> </div>", "links"=>[], "tags"=>["genetics", "Plant genetics", "Crop genetics", "Gene networks", "genomics", "Genome analysis tools", "Genetic networks", "transcriptomes", "Functional genomics", "Genome expression analysis", "Plant science", "plants", "Flowering plants", "Plant genomics", "atlas", "developed", "rna", "sequencing", "comparative"], "article_id"=>688944, "categories"=>["Biological Sciences"], "users"=>["Rajandeep S. Sekhon", "Roman Briskine", "Candice N. Hirsch", "Chad L. Myers", "Nathan M. Springer", "C. Robin Buell", "Natalia de Leon", "Shawn M. Kaeppler"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0061005.s001", "https://dx.doi.org/10.1371/journal.pone.0061005.s002", "https://dx.doi.org/10.1371/journal.pone.0061005.s003", "https://dx.doi.org/10.1371/journal.pone.0061005.s004", "https://dx.doi.org/10.1371/journal.pone.0061005.s005", "https://dx.doi.org/10.1371/journal.pone.0061005.s006", "https://dx.doi.org/10.1371/journal.pone.0061005.s007", "https://dx.doi.org/10.1371/journal.pone.0061005.s008", "https://dx.doi.org/10.1371/journal.pone.0061005.s009", "https://dx.doi.org/10.1371/journal.pone.0061005.s010", "https://dx.doi.org/10.1371/journal.pone.0061005.s011"], "stats"=>{"downloads"=>11, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Maize_Gene_Atlas_Developed_by_RNA_Sequencing_and_Comparative_Evaluation_of_Transcriptomes_Based_on_RNA_Sequencing_and_Microarrays_/688944", "title"=>"Maize Gene Atlas Developed by RNA Sequencing and Comparative Evaluation of Transcriptomes Based on RNA Sequencing and Microarrays", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-04-23 02:29:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1032743"], "description"=>"<p>H, hours; DAS, days after sowing; GH, greenhouse; V, vegetative; DAP, days after pollination; VT, vegetative tasseling; R, reproductive.</p>", "links"=>[], "tags"=>["genetics", "Plant genetics", "Crop genetics", "Gene networks", "genomics", "Genome analysis tools", "Genetic networks", "transcriptomes", "Functional genomics", "Genome expression analysis", "Plant science", "plants", "Flowering plants", "Plant genomics", "tissues", "included", "rna-seq-based"], "article_id"=>688939, "categories"=>["Biological Sciences"], "users"=>["Rajandeep S. Sekhon", "Roman Briskine", "Candice N. Hirsch", "Chad L. Myers", "Nathan M. Springer", "C. Robin Buell", "Natalia de Leon", "Shawn M. Kaeppler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061005.t001", "stats"=>{"downloads"=>6, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_List_of_tissues_included_in_RNA_Seq_based_gene_atlas_/688939", "title"=>"List of tissues included in RNA-Seq-based gene atlas.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-04-23 02:28:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1032742"], "description"=>"<p>The 3,354 genes with significant differences (p<0.01) in expression conservation between RNA-Seq and microarray data were assessed. The mean expression level in microarray samples (x-axis; log<sub>2</sub> tranformed) and RNA-Seq samples (y-axis; inverse hyperbolic sine transformed) was compared. The color coding indicates connectivity in the two co-expression networks; red indicates the 122 genes with more connections in the microarray network, blue indicates the 796 genes with more connections in the RNA-Seq co-expression network and grey indicates relatively similar connectivity in both networks. The circles indicate two clusters of genes with divergent EC scores.</p>", "links"=>[], "tags"=>["genetics", "Plant genetics", "Crop genetics", "Gene networks", "genomics", "Genome analysis tools", "Genetic networks", "transcriptomes", "Functional genomics", "Genome expression analysis", "Plant science", "plants", "Flowering plants", "Plant genomics", "profiles", "genes", "rna-seq", "microarray", "co-expression", "networks"], "article_id"=>688938, "categories"=>["Biological Sciences"], "users"=>["Rajandeep S. Sekhon", "Roman Briskine", "Candice N. Hirsch", "Chad L. Myers", "Nathan M. Springer", "C. Robin Buell", "Natalia de Leon", "Shawn M. Kaeppler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061005.g006", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_expression_profiles_for_individual_genes_in_RNA_Seq_and_microarray_co_expression_networks_based_on_expression_conservation_/688938", "title"=>"Comparison of expression profiles for individual genes in RNA-Seq and microarray co-expression networks based on expression conservation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-23 02:28:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1032736"], "description"=>"<p>Clustering was based on log<sub>2</sub>-transformed Fragments Per Kilobase Exon model per Million mapped fragments (FPKM) values of 29,038 genes that were detected in at least one tissue based on the FPKM 95% confidence interval lower boundary greater than zero. Red, yellow, and blue colors indicate high, medium, and low levels of gene log<sub>2</sub>-transformed expression, respectively.</p>", "links"=>[], "tags"=>["genetics", "Plant genetics", "Crop genetics", "Gene networks", "genomics", "Genome analysis tools", "Genetic networks", "transcriptomes", "Functional genomics", "Genome expression analysis", "Plant science", "plants", "Flowering plants", "Plant genomics", "hierarchical", "clustering", "tissues"], "article_id"=>688932, "categories"=>["Biological Sciences"], "users"=>["Rajandeep S. Sekhon", "Roman Briskine", "Candice N. Hirsch", "Chad L. Myers", "Nathan M. Springer", "C. Robin Buell", "Natalia de Leon", "Shawn M. Kaeppler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061005.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heat_map_showing_hierarchical_clustering_of_tissues_based_on_global_gene_expression_/688932", "title"=>"Heat map showing hierarchical clustering of tissues based on global gene expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-23 02:28:52"}

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Relative Metric

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