Using Network Component Analysis to Dissect Regulatory Networks Mediated by Transcription Factors in Yeast
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{"title"=>"Using network component analysis to dissect regulatory networks mediated by transcription factors in Yeast", "type"=>"journal", "authors"=>[{"first_name"=>"Chun", "last_name"=>"Ye", "scopus_author_id"=>"36187658500"}, {"first_name"=>"Simon J.", "last_name"=>"Galbraith", "scopus_author_id"=>"7004588137"}, {"first_name"=>"James C.", "last_name"=>"Liao", "scopus_author_id"=>"55628533238"}, {"first_name"=>"Eleazar", "last_name"=>"Eskin", "scopus_author_id"=>"7003344359"}], "year"=>2009, "source"=>"PLoS Computational Biology", "identifiers"=>{"pmid"=>"19300475", "sgr"=>"63549103905", "doi"=>"10.1371/journal.pcbi.1000311", "scopus"=>"2-s2.0-63549103905", "pui"=>"354408102", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "issn"=>"1553734X"}, "id"=>"9dbfbef5-b758-3009-bb6b-fb74655e03b1", "abstract"=>"Understanding the relationship between genetic variation and gene expression is a central question in genetics. With the availability of data from high-throughput technologies such as ChIP-Chip, expression, and genotyping arrays, we can begin to not only identify associations but to understand how genetic variations perturb the underlying transcription regulatory networks to induce differential gene expression. In this study, we describe a simple model of transcription regulation where the expression of a gene is completely characterized by two properties: the concentrations and promoter affinities of active transcription factors. We devise a method that extends Network Component Analysis (NCA) to determine how genetic variations in the form of single nucleotide polymorphisms (SNPs) perturb these two properties. Applying our method to a segregating population of Saccharomyces cerevisiae, we found statistically significant examples of trans-acting SNPs located in regulatory hotspots that perturb transcription factor concentrations and affinities for target promoters to cause global differential expression and cis-acting genetic variations that perturb the promoter affinities of transcription factors on a single gene to cause local differential expression. Although many genetic variations linked to gene expressions have been identified, it is not clear how they perturb the underlying regulatory networks that govern gene expression. Our work begins to fill this void by showing that many genetic variations affect the concentrations of active transcription factors in a cell and their affinities for target promoters. Understanding the effects of these perturbations can help us to paint a more complete picture of the complex landscape of transcription regulation. The software package implementing the algorithms discussed in this work is available as a MATLAB package upon request.", "link"=>"http://www.mendeley.com/research/using-network-component-analysis-dissect-regulatory-networks-mediated-transcription-factors-yeast", "reader_count"=>83, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>10, "Researcher"=>37, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>1, "Other"=>2, "Student > Master"=>4, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>10, "Researcher"=>37, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>1, "Other"=>2, "Student > Master"=>4, "Professor"=>9}, "reader_count_by_subject_area"=>{"Engineering"=>5, "Unspecified"=>2, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>7, "Agricultural and Biological Sciences"=>54, "Medicine and Dentistry"=>1, "Arts and Humanities"=>1, "Neuroscience"=>1, "Physics and Astronomy"=>2, "Computer Science"=>8, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>5}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>54}, "Computer Science"=>{"Computer Science"=>8}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>7}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "Sweden"=>1, "Belgium"=>1, "United States"=>9, "Norway"=>1, "China"=>1, "United Kingdom"=>2, "Israel"=>1, "Switzerland"=>1, "Germany"=>1}, "group_count"=>6}

Scopus | Further Information

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  • {"files"=>["https://ndownloader.figshare.com/files/447061", "https://ndownloader.figshare.com/files/447095", "https://ndownloader.figshare.com/files/447142", "https://ndownloader.figshare.com/files/447180"], "description"=>"<div><p>Understanding the relationship between genetic variation and gene expression is a central question in genetics. With the availability of data from high-throughput technologies such as ChIP-Chip, expression, and genotyping arrays, we can begin to not only identify associations but to understand how genetic variations perturb the underlying transcription regulatory networks to induce differential gene expression. In this study, we describe a simple model of transcription regulation where the expression of a gene is completely characterized by two properties: the concentrations and promoter affinities of active transcription factors. We devise a method that extends Network Component Analysis (NCA) to determine how genetic variations in the form of single nucleotide polymorphisms (SNPs) perturb these two properties. Applying our method to a segregating population of <em>Saccharomyces cerevisiae</em>, we found statistically significant examples of <em>trans</em>-acting SNPs located in regulatory hotspots that perturb transcription factor concentrations and affinities for target promoters to cause global differential expression and <em>cis</em>-acting genetic variations that perturb the promoter affinities of transcription factors on a single gene to cause local differential expression. Although many genetic variations linked to gene expressions have been identified, it is not clear how they perturb the underlying regulatory networks that govern gene expression. Our work begins to fill this void by showing that many genetic variations affect the concentrations of active transcription factors in a cell and their affinities for target promoters. Understanding the effects of these perturbations can help us to paint a more complete picture of the complex landscape of transcription regulation. The software package implementing the algorithms discussed in this work is available as a MATLAB package upon request.</p></div>", "links"=>[], "tags"=>["dissect", "networks", "mediated", "transcription", "factors", "yeast"], "article_id"=>148128, "categories"=>["Genetics", "Medicine", "Cancer", "Biological Sciences"], "users"=>["Chun Ye", "Simon J. Galbraith", "James C. Liao", "Eleazar Eskin"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000311.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000311.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000311.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000311.s004"], "stats"=>{"downloads"=>6, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Using_Network_Component_Analysis_to_Dissect_Regulatory_Networks_Mediated_by_Transcription_Factors_in_Yeast/148128", "title"=>"Using Network Component Analysis to Dissect Regulatory Networks Mediated by Transcription Factors in Yeast", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-03-20 02:15:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/904396"], "description"=>"<p>Regulatory hotspots and the transcription factors whose promoter affinities are perturbed to achieve global regulation.</p>", "links"=>[], "tags"=>["hotspots", "transcription", "factors", "promoter", "affinities", "perturbed"], "article_id"=>574848, "categories"=>["Genetics", "Medicine", "Cancer", "Computational Biology"], "users"=>["Chun Ye", "Simon J. Galbraith", "James C. Liao", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000311.t002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regulatory_hotspots_and_the_transcription_factors_whose_promoter_affinities_are_perturbed_to_achieve_global_regulation_/574848", "title"=>"Regulatory hotspots and the transcription factors whose promoter affinities are perturbed to achieve global regulation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-03-20 01:20:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/903777"], "description"=>"<p>(A) The same small toy example as <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000311#pcbi-1000311-g001\" target=\"_blank\">Figure 1A</a>. Log-linear equation representations of (B) the unperturbed NCA regulatory network model, (C) SNP1 perturbing the concentration of TF1, (D) SNP2 perturbing the promoter affinities of TF2 for its targets, (E) SNP3 perturbing the promoter affinities of TF1 and TF2 for G3.</p>", "links"=>[], "tags"=>["nca"], "article_id"=>574238, "categories"=>["Genetics", "Medicine", "Cancer", "Computational Biology"], "users"=>["Chun Ye", "Simon J. Galbraith", "James C. Liao", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000311.g002", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Matrix_representation_of_NCA_and_extension_of_NCA_to_include_genetic_perturbations_/574238", "title"=>"Matrix representation of NCA and extension of NCA to include genetic perturbations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-20 01:10:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/903937"], "description"=>"<p>Heatmap showing the correlations between concentrations of transcription factors and the expressions of their downstream targets linked to hotspot 2 on chromosome 2 ((A) <i>ACE2</i> and (B) <i>SWI4</i>) and hotspot 7 on chromosome 12 ((C) <i>YAP5</i> and (D) <i>GAT3</i>). The bar above each heatmap designates the concentration profile of each transcription factor.</p>", "links"=>[], "tags"=>["concentrations", "transcription", "factors", "expressions"], "article_id"=>574395, "categories"=>["Genetics", "Medicine", "Cancer", "Computational Biology"], "users"=>["Chun Ye", "Simon J. Galbraith", "James C. Liao", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000311.g003", "stats"=>{"downloads"=>3, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlations_between_concentrations_of_transcription_factors_and_the_expressions_of_their_targets_/574395", "title"=>"Correlations between concentrations of transcription factors and the expressions of their targets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-20 01:13:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/904201"], "description"=>"<p>(A) Percent of top genes with promoter affinities perturbed detected by likelihood ratio test concordant with those with <i>cis</i> linkages detected by t-test (red: Harbison dataset, blue: Lee dataset, gray: random). (B) Percent of top genes concordant between Lee and Harbison datasets using different tests (red: t-test for <i>cis</i> linkages, blue: likelihood ratio test for perturbed promoter affinities, gray: random).</p>", "links"=>[], "tags"=>["applying", "protein-dna", "binding"], "article_id"=>574665, "categories"=>["Genetics", "Medicine", "Cancer", "Computational Biology"], "users"=>["Chun Ye", "Simon J. Galbraith", "James C. Liao", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000311.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Concordances_between_applying_different_statistical_tests_and_using_different_protein_DNA_binding_datasets_/574665", "title"=>"Concordances between applying different statistical tests and using different protein-DNA binding datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-20 01:17:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/904355"], "description"=>"<p>Regulatory hotspots and the transcription factors whose active concentrations are perturbed to achieve global regulation.</p>", "links"=>[], "tags"=>["hotspots", "transcription", "factors", "concentrations", "perturbed"], "article_id"=>574810, "categories"=>["Genetics", "Medicine", "Cancer", "Computational Biology"], "users"=>["Chun Ye", "Simon J. Galbraith", "James C. Liao", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000311.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regulatory_hotspots_and_the_transcription_factors_whose_active_concentrations_are_perturbed_to_achieve_global_regulation_/574810", "title"=>"Regulatory hotspots and the transcription factors whose active concentrations are perturbed to achieve global regulation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-03-20 01:20:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/904072"], "description"=>"<p>Eleven hotspots and the networks of transcription factors and target genes perturbed. Large circular nodes represent transcription factors and square nodes represent target genes. The thickness of an edge represents how much a hotspot perturbs the promoter affinity. Red edges designate a change of a transcription factor from an activator to a repressor or vice versa. Notice that some perturbed networks share transcription factors. We show two hotspots and the corresponding networks in detail. Hotspot 2 in addition to affecting the promoter affinities of <i>ACE2</i> and <i>SWI4</i>, also affects the promoter affinities of several other transcription factors, including <i>UME6</i>, which is known to interact with <i>ACE2</i>. Hotspot 7 affects the promoter affinities of <i>YAP5</i> (thick edges) but its affect on <i>GAT3</i> promoter affinities is not statistically significant (thin edges). Figure was generated using the Cytoscape software <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000311#pcbi.1000311-Shannon1\" target=\"_blank\">[42]</a>.</p>", "links"=>[], "tags"=>["perturbed"], "article_id"=>574536, "categories"=>["Genetics", "Medicine", "Cancer", "Computational Biology"], "users"=>["Chun Ye", "Simon J. Galbraith", "James C. Liao", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000311.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Networks_perturbed_by_regulatory_hotspots_/574536", "title"=>"Networks perturbed by regulatory hotspots.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-20 01:15:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/903652"], "description"=>"<p>(A) A small toy example of three individuals with known genotyping and expression levels and inferred concentrations of active transcription factors. Each row corresponds to the genotypes, gene expressions and inferred transcription factor concentrations collected in one individual. (B) NCA regulatory network model when the network is unperturbed and the expression levels of G1, G2, G3 and G4 are determined by the concentrations of TF1, TF2 and the corresponding promoter affinities. (C) Between individuals with the A allele (1) and C allele (2,3) at SNP1, the concentrations of TF1 is perturbed by SNP1 causing differential expression of G1 and G3. (D) Between individuals with the G allele (1,2) and T allele (3) at SNP2, the promoter affinities of TF2 are perturbed globally by SNP2 (i.e. edges from TF2 are perturbed) to cause differential expression in all of TF2's targets G2, G3, and G4. (E) Between individuals with the A allele (1) and T allele (2,3) at SNP3, the affinities of TF1 and TF2 for the G3 promoter is perturbed locally by SNP3 to cause differential expression of G3.</p>", "links"=>[], "tags"=>["nca"], "article_id"=>574109, "categories"=>["Genetics", "Medicine", "Cancer", "Computational Biology"], "users"=>["Chun Ye", "Simon J. Galbraith", "James C. Liao", "Eleazar Eskin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000311.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graphical_illustration_of_NCA_and_extension_of_NCA_to_include_genetic_perturbations_/574109", "title"=>"Graphical illustration of NCA and extension of NCA to include genetic perturbations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-20 01:08:29"}

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