Coherent Functional Modules Improve Transcription Factor Target Identification, Cooperativity Prediction, and Disease Association
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{"title"=>"Coherent Functional Modules Improve Transcription Factor Target Identification, Cooperativity Prediction, and Disease Association", "type"=>"journal", "authors"=>[{"first_name"=>"Konrad J.", "last_name"=>"Karczewski", "scopus_author_id"=>"36110203000"}, {"first_name"=>"Michael", "last_name"=>"Snyder", "scopus_author_id"=>"35355673100"}, {"first_name"=>"Russ B.", "last_name"=>"Altman", "scopus_author_id"=>"7202798518"}, {"first_name"=>"Nicholas P.", "last_name"=>"Tatonetti", "scopus_author_id"=>"35604787200"}], "year"=>2014, "source"=>"PLoS Genetics", "identifiers"=>{"isbn"=>"1553-7404", "scopus"=>"2-s2.0-84901742688", "pui"=>"372548517", "doi"=>"10.1371/journal.pgen.1004122", "issn"=>"15537390", "pmid"=>"24516403", "sgr"=>"84901742688"}, "id"=>"526ed6b6-b383-3215-a6fc-53f61679e5c4", "abstract"=>"Transcription factors (TFs) are fundamental controllers of cellular regulation that function in a complex and combinatorial manner. Accurate identification of a transcription factor's targets is essential to understanding the role that factors play in disease biology. However, due to a high false positive rate, identifying coherent functional target sets is difficult. We have created an improved mapping of targets by integrating ChIP-Seq data with 423 functional modules derived from 9,395 human expression experiments. We identified 5,002 TF-module relationships, significantly improved TF target prediction, and found 30 high-confidence TF-TF associations, of which 14 are known. Importantly, we also connected TFs to diseases through these functional modules and identified 3,859 significant TF-disease relationships. As an example, we found a link between MEF2A and Crohn's disease, which we validated in an independent expression dataset. These results show the power of combining expression data and ChIP-Seq data to remove noise and better extract the associations between TFs, functional modules, and disease.", "link"=>"http://www.mendeley.com/research/coherent-functional-modules-improve-transcription-factor-target-identification-cooperativity-predict", "reader_count"=>84, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>4, "Researcher"=>23, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>3, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>4, "Researcher"=>23, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>3, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Biochemistry, Genetics and Molecular Biology"=>19, "Mathematics"=>2, "Agricultural and Biological Sciences"=>49, "Medicine and Dentistry"=>2, "Business, Management and Accounting"=>1, "Computer Science"=>8}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>49}, "Computer Science"=>{"Computer Science"=>8}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>19}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"Netherlands"=>1, "Korea (South)"=>1, "United States"=>9, "Brazil"=>1, "Mexico"=>1, "Italy"=>1, "France"=>1, "Australia"=>1, "Spain"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1557394"], "description"=>"<p>Transcription factors (blue) are connected to diseases (red) through modules in this bipartite graph. Prominent clusters of diseases are highlighted, as well as some highly-connected transcription factors. Importantly, STAT3 is connected to many fibrotic diseases, while E2F1 and E2F4 are connected to breast and ovarian cancer. (A): Expression of MEF2A and the projection of module 262 are significantly predictive of disease state. Individuals are ranked by their combined score (sum of normalized expression and module projection). (B): ROC curve for prediction of Crohn's disease from MEF2A expression, module 262 projection, and combined metric.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Functional genomics", "Genome expression analysis", "Regulatory networks", "Genomic medicine"], "article_id"=>1076994, "categories"=>["Biological Sciences"], "users"=>["Konrad J. Karczewski", "Michael Snyder", "Russ B. Altman", "Nicholas P. Tatonetti"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004122.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regulatory_network_of_human_disease_/1076994", "title"=>"Regulatory network of human disease.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-06 10:58:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1557396"], "description"=>"<p>Transcription factor-disease relationships derived from shared modules.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Functional genomics", "Genome expression analysis", "Regulatory networks", "Genomic medicine", "factor-disease", "relationships", "derived"], "article_id"=>1076996, "categories"=>["Biological Sciences"], "users"=>["Konrad J. Karczewski", "Michael Snyder", "Russ B. Altman", "Nicholas P. Tatonetti"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004122.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transcription_factor_disease_relationships_derived_from_shared_modules_/1076996", "title"=>"Transcription factor-disease relationships derived from shared modules.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-06 10:58:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1557398"], "description"=>"<p>Top 5 TF-module associations, as well as NFκB and a general transcription module (associated with 141 different TFs) are shown.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Functional genomics", "Genome expression analysis", "Regulatory networks", "Genomic medicine", "modules", "recapitulate", "transcription"], "article_id"=>1076998, "categories"=>["Biological Sciences"], "users"=>["Konrad J. Karczewski", "Michael Snyder", "Russ B. Altman", "Nicholas P. Tatonetti"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004122.t001", "stats"=>{"downloads"=>0, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Functional_modules_recapitulate_known_transcription_factor_biology_/1076998", "title"=>"Functional modules recapitulate known transcription factor biology.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-06 10:58:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1557331"], "description"=>"<p>(A): Prediction of (i) gene expression correlation, (ii) literature mentions, and (iii) shared functional annotations using a Naive approach, shared TFICA modules, and weighted TFICA modules. The Naive approach (“Naive”) links TFs to TFs by the similarity of their ChIP-Seq targets, “TFICA” links TFs to TFs by the similarity of their significantly associated modules, and weighted TFICA weights these modules in the similarity by their confidence. β coefficients in a linear model are shown with 95% confidence intervals. In each case, TFICA and weighted TFICA significantly outperforms the Naive approach. In addition, we used permutation testing to validate these results. In each case (expression, literature, function) the β coefficient for the permuted model was not significant (β<sub>exp</sub> = 0.16; 95%CI −0.02–0.34; β<sub>lit</sub> = −0.02 95%CI −0.08–0.05; β<sub>fun</sub> = −0.04 95%CI −0.14–0.06, P>0.05 for each). Data not drawn. (B): The top 30 highest-scoring pairs are shown, as measured by target module similarity, 14 of which are known associations (solid lines). Many of these factors form a tight sub-network of activators and repressors.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Functional genomics", "Genome expression analysis", "Regulatory networks", "Genomic medicine", "tf-tf", "interactions", "modules"], "article_id"=>1076938, "categories"=>["Biological Sciences"], "users"=>["Konrad J. Karczewski", "Michael Snyder", "Russ B. Altman", "Nicholas P. Tatonetti"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004122.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicting_TF_TF_interactions_using_shared_modules_as_a_measure_of_shared_function_/1076938", "title"=>"Predicting TF-TF interactions using shared modules as a measure of shared function.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-06 10:58:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1557305"], "description"=>"<p>(A): The classical example of ICA is the “cocktail party problem,” where a number of microphones are placed in a room, capturing a mixture of conversations. Source separation methods such as ICA attempt to deconvolve the recorded mixed signals into their separate source signals (individual conversations). (B): An analogous application involves identifying source signals of transcriptional regulators from complex gene expression measurements.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Functional genomics", "Genome expression analysis", "Regulatory networks", "Genomic medicine", "transcriptional", "modules"], "article_id"=>1076910, "categories"=>["Biological Sciences"], "users"=>["Konrad J. Karczewski", "Michael Snyder", "Russ B. Altman", "Nicholas P. Tatonetti"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004122.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Independent_Component_Analysis_ICA_can_be_used_to_identify_transcriptional_modules_from_gene_expression_data_/1076910", "title"=>"Independent Component Analysis (ICA) can be used to identify transcriptional modules from gene expression data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-06 10:58:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1557543", "https://ndownloader.figshare.com/files/1557544", "https://ndownloader.figshare.com/files/1557547", "https://ndownloader.figshare.com/files/1557548", "https://ndownloader.figshare.com/files/1557549", "https://ndownloader.figshare.com/files/1557550", "https://ndownloader.figshare.com/files/1557552", "https://ndownloader.figshare.com/files/1557553", "https://ndownloader.figshare.com/files/1557554", "https://ndownloader.figshare.com/files/1557555", "https://ndownloader.figshare.com/files/1557556", "https://ndownloader.figshare.com/files/1557558"], "description"=>"<div><p>Transcription factors (TFs) are fundamental controllers of cellular regulation that function in a complex and combinatorial manner. Accurate identification of a transcription factor's targets is essential to understanding the role that factors play in disease biology. However, due to a high false positive rate, identifying coherent functional target sets is difficult. We have created an improved mapping of targets by integrating ChIP-Seq data with 423 functional modules derived from 9,395 human expression experiments. We identified 5,002 TF-module relationships, significantly improved TF target prediction, and found 30 high-confidence TF-TF associations, of which 14 are known. Importantly, we also connected TFs to diseases through these functional modules and identified 3,859 significant TF-disease relationships. As an example, we found a link between MEF2A and Crohn's disease, which we validated in an independent expression dataset. These results show the power of combining expression data and ChIP-Seq data to remove noise and better extract the associations between TFs, functional modules, and disease.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Functional genomics", "Genome expression analysis", "Regulatory networks", "Genomic medicine", "modules", "transcription", "cooperativity"], "article_id"=>1077109, "categories"=>["Biological Sciences"], "users"=>["Konrad J. Karczewski", "Michael Snyder", "Russ B. Altman", "Nicholas P. Tatonetti"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1004122.s001", "https://dx.doi.org/10.1371/journal.pgen.1004122.s002", "https://dx.doi.org/10.1371/journal.pgen.1004122.s003", "https://dx.doi.org/10.1371/journal.pgen.1004122.s004", "https://dx.doi.org/10.1371/journal.pgen.1004122.s005", "https://dx.doi.org/10.1371/journal.pgen.1004122.s006", "https://dx.doi.org/10.1371/journal.pgen.1004122.s007", "https://dx.doi.org/10.1371/journal.pgen.1004122.s008", "https://dx.doi.org/10.1371/journal.pgen.1004122.s009", "https://dx.doi.org/10.1371/journal.pgen.1004122.s010", "https://dx.doi.org/10.1371/journal.pgen.1004122.s011", "https://dx.doi.org/10.1371/journal.pgen.1004122.s012"], "stats"=>{"downloads"=>66, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Coherent_Functional_Modules_Improve_Transcription_Factor_Target_Identification_Cooperativity_Prediction_and_Disease_Association_/1077109", "title"=>"Coherent Functional Modules Improve Transcription Factor Target Identification, Cooperativity Prediction, and Disease Association", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-02-06 10:58:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1557314"], "description"=>"<p>(A): A TF is associated to a module if its targets are significantly enriched in a particular module. TF are connected to their targets using ChIP-Seq data, which may (solid) or may not (dashed) be contained with an expression module. GO annotations (colored blue/yellow) are used in enrichment analysis to associate modules and their factors to functional pathways. (B): We evaluated the quality of TFICA derived TF targets based on the hypothesis that if a TF does regulate a target, then it is more likely that the TF and the target will share a functional annotation. Across ChIP-Seq scores, TFICA outperforms the naive method, and this performance is further increased when only considering high and medium-confidence modules (see text).</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Functional genomics", "Genome expression analysis", "Regulatory networks", "Genomic medicine", "tfs"], "article_id"=>1076920, "categories"=>["Biological Sciences"], "users"=>["Konrad J. Karczewski", "Michael Snyder", "Russ B. Altman", "Nicholas P. Tatonetti"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004122.g002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Association_of_TFs_to_expression_modules_/1076920", "title"=>"Association of TFs to expression modules.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-06 10:58:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1557349"], "description"=>"<p>Transcription factors are connected solely on the basis of the similarity of the modules that they regulate. Transcription factors are colored according to a selection of diseases; (A, green): AIDS; (B, blue): arrhythmia; (C, pink): breast cancer; (D, red): hemorrhage. Nodes are annotated with strong (dashed black borders) and weak (solid grey borders) literature support. See <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004122#pgen-1004122-t002\" target=\"_blank\">Table 2</a> for details.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Functional genomics", "Genome expression analysis", "Regulatory networks", "Genomic medicine", "reveals"], "article_id"=>1076955, "categories"=>["Biological Sciences"], "users"=>["Konrad J. Karczewski", "Michael Snyder", "Russ B. Altman", "Nicholas P. Tatonetti"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004122.g004", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transcription_factor_interaction_network_reveals_functional_and_disease_sub_networks_/1076955", "title"=>"Transcription factor interaction network reveals functional and disease sub-networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-06 10:58:31"}

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Relative Metric

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