Regulatory Snapshots: Integrative Mining of Regulatory Modules from Expression Time Series and Regulatory Networks
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{"title"=>"Regulatory snapshots: Integrative mining of regulatory modules from expression time series and regulatory networks", "type"=>"journal", "authors"=>[{"first_name"=>"Joana P.", "last_name"=>"Gonçalves", "scopus_author_id"=>"35182908600"}, {"first_name"=>"Ricardo S.", "last_name"=>"Aires", "scopus_author_id"=>"37664421500"}, {"first_name"=>"Alexandre P.", "last_name"=>"Francisco", "scopus_author_id"=>"23476901700"}, {"first_name"=>"Sara C.", "last_name"=>"Madeira", "scopus_author_id"=>"6602138051"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "sgr"=>"84871185628", "doi"=>"10.1371/journal.pone.0035977", "scopus"=>"2-s2.0-84871185628", "pui"=>"364721366", "pmid"=>"22563474"}, "id"=>"308ee29c-709d-3d2c-9dda-43da45e9082e", "abstract"=>"Explaining regulatory mechanisms is crucial to understand complex cellular responses leading to system perturbations. Some strategies reverse engineer regulatory interactions from experimental data, while others identify functional regulatory units (modules) under the assumption that biological systems yield a modular organization. Most modular studies focus on network structure and static properties, ignoring that gene regulation is largely driven by stimulus-response behavior. Expression time series are key to gain insight into dynamics, but have been insufficiently explored by current methods, which often (1) apply generic algorithms unsuited for expression analysis over time, due to inability to maintain the chronology of events or incorporate time dependency; (2) ignore local patterns, abundant in most interesting cases of transcriptional activity; (3) neglect physical binding or lack automatic association of regulators, focusing mainly on expression patterns; or (4) limit the discovery to a predefined number of modules. We propose Regulatory Snapshots, an integrative mining approach to identify regulatory modules over time by combining transcriptional control with response, while overcoming the above challenges. Temporal biclustering is first used to reveal transcriptional modules composed of genes showing coherent expression profiles over time. Personalized ranking is then applied to prioritize prominent regulators targeting the modules at each time point using a network of documented regulatory associations and the expression data. Custom graphics are finally depicted to expose the regulatory activity in a module at consecutive time points (snapshots). Regulatory Snapshots successfully unraveled modules underlying yeast response to heat shock and human epithelial-to-mesenchymal transition, based on regulations documented in the YEASTRACT and JASPAR databases, respectively, and available expression data. Regulatory players involved in functionally enriched processes related to these biological events were identified. Ranking scores further suggested ability to discern the primary role of a gene (target or regulator). Prototype is available at: http://kdbio.inesc-id.pt/software/regulatorysnapshots.", "link"=>"http://www.mendeley.com/research/regulatory-snapshots-integrative-mining-regulatory-modules-expression-time-series-regulatory-network", "reader_count"=>23, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>7, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>4, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>7, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>4, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>4, "Agricultural and Biological Sciences"=>12, "Computer Science"=>5, "Decision Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>12}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}}, "reader_count_by_country"=>{"United States"=>3, "Norway"=>1, "Brazil"=>1, "Portugal"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/643808"], "description"=>"<p>This figure shows an overview of the proposed method, Regulatory Snapshots, to mine regulatory modules from expression time series and regulatory associations in two steps. First, biclustering is applied to expression time series to find transcriptionally coherent genes and group them in transcriptional modules (biclusters). A personalized ranking strategy is then used to compute relevance scores for the transcription factors targeting the genes in the biclusters at each time point. Finally, regulators are sorted by relevance and a graphical representation, termed regulatory snapshot, is depicted to expose the architecture of the regulatory module.</p>", "links"=>[], "tags"=>["snapshots"], "article_id"=>314299, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences", "Biophysics"], "users"=>["Joana P. Gonçalves", "Ricardo S. Aires", "Alexandre P. Francisco", "Sara C. Madeira"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0035977.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regulatory_Snapshots_method_/314299", "title"=>"Regulatory Snapshots method.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 05:39:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/644597"], "description"=>"<p>This figure shows the expression profiles of the genes in five of the regulatory modules obtained for the human epithelial-to-mesenchymal expression data and the human regulatory network containing regulations from the JASPAR database (modules 4554, 4544, 2485, 4499, and 5536), yielding some of the highest numbers of significantly annotated Gene Ontology terms. Expression levels were normalized by gene to zero mean and unit standard deviation.</p>", "links"=>[], "tags"=>["profiles", "enriched", "epithelial-to-mesenchymal"], "article_id"=>315092, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences", "Biophysics"], "users"=>["Joana P. Gonçalves", "Ricardo S. Aires", "Alexandre P. Francisco", "Sara C. Madeira"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0035977.g007", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Expression_profiles_of_five_enriched_human_epithelial_to_mesenchymal_regulatory_modules_/315092", "title"=>"Expression profiles of five enriched human epithelial-to-mesenchymal regulatory modules.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 05:43:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/644295"], "description"=>"<p>This figure shows regulatory snapshots obtained for yeast heat shock stress regulatory modules 39 and 151 over time (0’, 5’, 15’, 30’ and 60’), highlighting the ranks and interactions of regulators reportedly targeting the genes in these modules. Each snapshot along a row was obtained for a particular time point. Regulators and target genes are respectively represented in the top and bottom semi-circles, and regulators appear from left to right in decreasing order of ranking score. Orange and green arcs respectively identify “regulates\" and “regulated by\" relations for the highlighted regulators in each figure. The figures in the top row expose the ranks of Arr1p, Hsf1p, Msn2p, Rpn4p and Sok2p for bicluster 39, while the ones in the bottom row highlight the ranks of Arr1p, Ino4p, Mbp1p, Rpn4p and Swi4p for bicluster 151.</p>", "links"=>[], "tags"=>["snapshots", "documented", "regulators", "yeast"], "article_id"=>314781, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences", "Biophysics"], "users"=>["Joana P. Gonçalves", "Ricardo S. Aires", "Alexandre P. Francisco", "Sara C. Madeira"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0035977.g005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regulatory_snapshots_of_documented_regulators_in_yeast_heat_shock_modules_/314781", "title"=>"Regulatory snapshots of documented regulators in yeast heat shock modules.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 05:41:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/644440"], "description"=>"<p>This figure shows regulatory snapshots obtained for yeast heat shock stress regulatory modules 39 and 151 over time (0’, 5’, 15’, 30’ and 60’), highlighting the ranks of regulators exhibiting large relevance variations. In every row, each figure highlights the relevance of a particular regulator placed among the 30 best ranked TFs for a specific time point. Regulators appear in the top semi-circle from left to right in decreasing order of ranking score, and target genes are shown in the bottom semi-circle. Orange and green arcs respectively identify “regulates\" and “regulated by\" relations for the highlighted regulator. From top to bottom, first and second rows expose the ranks of Mig1p and Rim101p targeting the genes in biclusters 39, while third and fourth rows expose the ranks of Hcm1p and Arr1p in bicluster 151.</p>", "links"=>[], "tags"=>["snapshots", "regulators", "relevance", "variations", "yeast"], "article_id"=>314926, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences", "Biophysics"], "users"=>["Joana P. Gonçalves", "Ricardo S. Aires", "Alexandre P. Francisco", "Sara C. Madeira"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0035977.g006", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regulatory_snapshots_of_regulators_with_large_relevance_variations_in_yeast_heat_shock_modules_/314926", "title"=>"Regulatory snapshots of regulators with large relevance variations in yeast heat shock modules.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 05:42:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/644002"], "description"=>"<p>This figure shows all transcriptional modules, or maximal CCC-Biclusters with at least two rows, obtained by applying CCC-Biclustering to the transformed matrix in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035977#pone-0035977-g002\" target=\"_blank\">Figure 2</a>. Maximal CCC-Biclusters are represented: (left) in the transformed discretized matrix of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035977#pone-0035977-g002\" target=\"_blank\">Figure 2</a>; and (right) in the generalized suffix tree built for the strings in the rows of this matrix.</p>", "links"=>[], "tags"=>["module", "maximal"], "article_id"=>314492, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences", "Biophysics"], "users"=>["Joana P. Gonçalves", "Ricardo S. Aires", "Alexandre P. Francisco", "Sara C. Madeira"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0035977.g003", "stats"=>{"downloads"=>4, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transcriptional_module_identification_maximal_biclusters_/314492", "title"=>"Transcriptional module identification: maximal biclusters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 05:40:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/643937"], "description"=>"<p>Illustrative example of discretization and alphabet transformation for a time series gene expression matrix: (left) original expression matrix <i>M</i>'; (center) discretized matrix <i>M</i>, obtained by applying a discretization based on transitions between time points to the original matrix <i>M</i>' using a three-symbol alphabet <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035977#pone.0035977-Ji1\" target=\"_blank\">[28]</a>; and (right) matrix <i>M</i> after the alphabet transformation that appends the column number to every symbol in the matrix.</p>", "links"=>[], "tags"=>["module", "discretization", "alphabet"], "article_id"=>314428, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences", "Biophysics"], "users"=>["Joana P. Gonçalves", "Ricardo S. Aires", "Alexandre P. Francisco", "Sara C. Madeira"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0035977.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transcriptional_module_identification_discretization_and_alphabet_transformation_/314428", "title"=>"Transcriptional module identification: discretization and alphabet transformation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 05:40:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/644134"], "description"=>"<p>This figure shows the expression profiles of the genes targeted in the regulatory modules 39 (left) and 151 (right) obtained for the yeast heat shock expression data and the yeast regulatory network containing regulations from the YEASTRACT database. Expression levels were normalized by gene to zero mean and unit standard deviation.</p>", "links"=>[], "tags"=>["profiles", "yeast"], "article_id"=>314628, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences", "Biophysics"], "users"=>["Joana P. Gonçalves", "Ricardo S. Aires", "Alexandre P. Francisco", "Sara C. Madeira"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0035977.g004", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Expression_profiles_of_yeast_heat_shock_regulatory_modules_/314628", "title"=>"Expression profiles of yeast heat shock regulatory modules.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 05:41:03"}

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Relative Metric

{"start_date"=>"2012-01-01T00:00:00Z", "end_date"=>"2012-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Computational biology", "average_usage"=>[375, 629, 760, 889, 1000, 1110, 1203, 1298, 1399, 1492, 1603, 1699, 1774, 1855, 1918, 1998, 2062, 2152, 2227, 2312, 2378, 2461, 2528, 2600, 2664]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[352, 587, 696, 809, 901, 989, 1072, 1156, 1257, 1334, 1422, 1486, 1555, 1647, 1714, 1780, 1844, 1919, 1997, 2051, 2138, 2198, 2267, 2324, 2391]}, {"subject_area"=>"/Computer and information sciences/Data visualization", "average_usage"=>[404, 618, 724, 839, 946, 1045, 1135, 1232, 1287, 1372, 1448, 1523, 1607, 1675, 1720, 1780, 1824, 1937, 1969, 2097, 2163, 2206, 2256, 2279, 2374]}]}
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