PhenoTimer: Software for the Visual Mapping of Time-Resolved Phenotypic Landscapes
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{"title"=>"PhenoTimer: Software for the Visual Mapping of Time-Resolved Phenotypic Landscapes", "type"=>"journal", "authors"=>[{"first_name"=>"Maria", "last_name"=>"Secrier", "scopus_author_id"=>"36088015900"}, {"first_name"=>"Reinhard", "last_name"=>"Schneider", "scopus_author_id"=>"23098235400"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"369566300", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0072361", "scopus"=>"2-s2.0-84881528383", "pmid"=>"23951317", "sgr"=>"84881528383"}, "id"=>"ec594677-ffd0-3cb5-8524-e2aba417775e", "abstract"=>"Timing common and specific modulators of disease progression is crucial for treatment, but the understanding of the underlying complex system of interactions is limited. While attempts at elucidating this experimentally have produced enormous amounts of phenotypic data, tools that are able to visualize and analyze them are scarce and the insight obtained from the data is often unsatisfactory. Linking and visualizing processes from genes to phenotypes and back, in a temporal context, remains a challenge in systems biology. We introduce PhenoTimer, a 2D/3D visualization tool for the mapping of time-resolved phenotypic links in a genetic context. It uses a novel visualization approach for relations between morphological defects, pathways or diseases, to enable fast pattern discovery and hypothesis generation. We illustrate its capabilities of tracing dynamic motifs on cell cycle datasets that explore the phenotypic order of events upon perturbations of the system, transcriptional activity programs and their connection to disease. By using this tool we are able to fine-grain regulatory programs for individual time points of the cell cycle and better understand which patterns arise when these programs fail. We also illustrate a way to identify common mechanisms of misregulation in diseases and drug abuse.", "link"=>"http://www.mendeley.com/research/phenotimer-software-visual-mapping-timeresolved-phenotypic-landscapes", "reader_count"=>18, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Librarian"=>1, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>5, "Student > Master"=>3, "Student > Bachelor"=>2, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Librarian"=>1, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>5, "Student > Master"=>3, "Student > Bachelor"=>2, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>2, "Biochemistry, Genetics and Molecular Biology"=>3, "Agricultural and Biological Sciences"=>7, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Unspecified"=>{"Unspecified"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"United States"=>3}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1150534"], "description"=>"<p>(A) Peaks of transcriptional activity for periodic genes within the cell cycle are shown throughout the cell cycle phases, along with the cancer pathways in which they are enriched. Two cancer pathways are connected if they share at least one enriched gene at the particular time point. The network neighborhood of the genes involved in the connections is also shown, as retrieved from STRING. Gene <i>VEGFC</i> is highlighted in red. (B) The genes shared by several cancer pathways are highlighted through the course of the cell cycle.</p>", "links"=>[], "tags"=>["linking", "cancer"], "article_id"=>770237, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Maria Secrier", "Reinhard Schneider"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0072361.g002", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transcription_events_linking_cancer_pathways_/770237", "title"=>"Transcription events linking cancer pathways.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-12 03:33:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1150530"], "description"=>"<p>Three arc representations are shown: (A) 3D; (B) linear 2D; (C) circular 2D. An arc represents a transition from one phenotype to the other at consecutive time points. The color of the arc corresponds to the phenotype into which the cells transition. The height (3D) and width (2D), respectively, of arcs indicates the number of genes whose suppression causes the respective phenotypic transition at that particular moment (at most 5 genes for this dataset). The GO term network in the boxed picture in the upper right corner highlights (in red) the molecular functions of the genes whose knockdown causes a transition at time point 41. The respective transitions are shown as arcs in the plot for the particular time point. The size of the nodes in the network is proportional to the number of genes in the dataset that are enriched for the respective function. The GO network was generated using BiNGO [37] and subsequently loaded into PhenoTimer.</p>", "links"=>[], "tags"=>["patterns", "populations", "knockdown", "genes"], "article_id"=>770233, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Maria Secrier", "Reinhard Schneider"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0072361.g001", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phenotypic_transition_patterns_in_cell_populations_upon_knockdown_of_genes_essential_for_cell_division_/770233", "title"=>"Phenotypic transition patterns in cell populations upon knockdown of genes essential for cell division.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-12 03:33:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1150548", "https://ndownloader.figshare.com/files/1150549", "https://ndownloader.figshare.com/files/1150550", "https://ndownloader.figshare.com/files/1150551", "https://ndownloader.figshare.com/files/1150552", "https://ndownloader.figshare.com/files/1150553", "https://ndownloader.figshare.com/files/1150554", "https://ndownloader.figshare.com/files/1150555", "https://ndownloader.figshare.com/files/1150556", "https://ndownloader.figshare.com/files/1150558", "https://ndownloader.figshare.com/files/1150559", "https://ndownloader.figshare.com/files/1150560", "https://ndownloader.figshare.com/files/1150561", "https://ndownloader.figshare.com/files/1150562", "https://ndownloader.figshare.com/files/1150563", "https://ndownloader.figshare.com/files/1150564", "https://ndownloader.figshare.com/files/1150565"], "description"=>"<div><p>Timing common and specific modulators of disease progression is crucial for treatment, but the understanding of the underlying complex system of interactions is limited. While attempts at elucidating this experimentally have produced enormous amounts of phenotypic data, tools that are able to visualize and analyze them are scarce and the insight obtained from the data is often unsatisfactory. Linking and visualizing processes from genes to phenotypes and back, in a temporal context, remains a challenge in systems biology. We introduce PhenoTimer, a 2D/3D visualization tool for the mapping of time-resolved phenotypic links in a genetic context. It uses a novel visualization approach for relations between morphological defects, pathways or diseases, to enable fast pattern discovery and hypothesis generation. We illustrate its capabilities of tracing dynamic motifs on cell cycle datasets that explore the phenotypic order of events upon perturbations of the system, transcriptional activity programs and their connection to disease. By using this tool we are able to fine-grain regulatory programs for individual time points of the cell cycle and better understand which patterns arise when these programs fail. We also illustrate a way to identify common mechanisms of misregulation in diseases and drug abuse.</p> </div>", "links"=>[], "tags"=>["time-resolved", "phenotypic"], "article_id"=>770245, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Maria Secrier", "Reinhard Schneider"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0072361.s001", "https://dx.doi.org/10.1371/journal.pone.0072361.s002", "https://dx.doi.org/10.1371/journal.pone.0072361.s003", "https://dx.doi.org/10.1371/journal.pone.0072361.s004", "https://dx.doi.org/10.1371/journal.pone.0072361.s005", "https://dx.doi.org/10.1371/journal.pone.0072361.s006", "https://dx.doi.org/10.1371/journal.pone.0072361.s007", "https://dx.doi.org/10.1371/journal.pone.0072361.s008", "https://dx.doi.org/10.1371/journal.pone.0072361.s009", "https://dx.doi.org/10.1371/journal.pone.0072361.s010", "https://dx.doi.org/10.1371/journal.pone.0072361.s011", "https://dx.doi.org/10.1371/journal.pone.0072361.s012", "https://dx.doi.org/10.1371/journal.pone.0072361.s013", "https://dx.doi.org/10.1371/journal.pone.0072361.s014", "https://dx.doi.org/10.1371/journal.pone.0072361.s015", "https://dx.doi.org/10.1371/journal.pone.0072361.s016", "https://dx.doi.org/10.1371/journal.pone.0072361.s017"], "stats"=>{"downloads"=>21, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PhenoTimer_Software_for_the_Visual_Mapping_of_Time_Resolved_Phenotypic_Landscapes_/770245", "title"=>"PhenoTimer: Software for the Visual Mapping of Time-Resolved Phenotypic Landscapes", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-08-12 03:33:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1150538"], "description"=>"<p>(A) Pair-wise connections between drugs and networks of genes affected by the same drug are visualized using PhenoTimer for every time point. Two drugs are connected if they similarly regulate the same gene. Two genes are connected in the network if they respond to the same drug(s). Thickness of links corresponds to the number of genes, or drugs, respectively, shared by two partners. Only genes with transcription values in the lower quartile (top) or upper quartile (bottom) are taken into account. The core gene network is depicted in yellow and the variable gene elements (i.e. that don’t appear at every time point) are highlighted in orange (lower quartile) and green (upper quartile). Links between drugs are depicted in magenta and pink if they contain the highest number of commonly regulated genes. The genes specific for that particular link only are circled in the same color in the network below. (B) Heat map of the gene expression values after 8 hours of drug induction is shown for every drug. The line corresponding to gene <i>Tnfrsf25</i> is highlighted and the columns corresponding to ethanol and heroin are also indicated. The graphic of transcription counts throughout the time course for this gene after heroin induction is shown below. Both images have been generated using PhenoTimer. (C) The network of human homologs for the relatively lowly and highly expressed genes. The variable genes are highlighted in orange (lower quartile) and green (upper quartile), along with the time points when their values are in the required quartile range.</p>", "links"=>[], "tags"=>["mechanisms", "drug-induced", "hours"], "article_id"=>770239, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Maria Secrier", "Reinhard Schneider"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0072361.g003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Similar_mechanisms_of_drug_induced_gene_regulation_for_up_to_8_hours_after_treatment_/770239", "title"=>"Similar mechanisms of drug-induced gene regulation for up to 8 hours after treatment.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-12 03:33:08"}

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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/Cell biology", "average_usage"=>[272, 472, 600, 713, 815, 911, 1004, 1094, 1185, 1273, 1358, 1441]}, {"subject_area"=>"/Biology and life sciences/Computational biology", "average_usage"=>[295, 511, 651, 775, 882, 992, 1100, 1201, 1304, 1400, 1486, 1570, 1650]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[297, 488, 616, 724, 828, 939, 1038, 1127, 1223, 1311, 1393, 1479, 1556]}, {"subject_area"=>"/Medicine and health sciences/Pharmacology", "average_usage"=>[265, 471, 604, 723, 827, 926, 1022, 1113, 1210, 1290, 1377, 1454, 1532]}]}
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