Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions
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{"title"=>"Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions", "type"=>"journal", "authors"=>[{"first_name"=>"Chieh Chun", "last_name"=>"Chen", "scopus_author_id"=>"24333901800"}, {"first_name"=>"Shu", "last_name"=>"Xiao", "scopus_author_id"=>"36515878000"}, {"first_name"=>"Dan", "last_name"=>"Xie", "scopus_author_id"=>"47761571200"}, {"first_name"=>"Xiaoyi", "last_name"=>"Cao", "scopus_author_id"=>"35730128500"}, {"first_name"=>"Chun Xiao", "last_name"=>"Song", "scopus_author_id"=>"37098955700"}, {"first_name"=>"Ting", "last_name"=>"Wang", "scopus_author_id"=>"7405563383"}, {"first_name"=>"Chuan", "last_name"=>"He", "scopus_author_id"=>"25937283000"}, {"first_name"=>"Sheng", "last_name"=>"Zhong", "scopus_author_id"=>"55682668900"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84892757801", "doi"=>"10.1371/journal.pcbi.1003367", "issn"=>"1553734X", "pui"=>"372170438", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "pmid"=>"24339764", "scopus"=>"2-s2.0-84892757801"}, "id"=>"2b88edd9-52aa-360f-898e-602f1f5c1ed1", "abstract"=>"Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from in vivo (ChIP-seq) and in vitro experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (Affinity Prediction by Epigenome and Genome, http://systemsbio.ucsd.edu/apeg).", "link"=>"http://www.mendeley.com/research/understanding-variation-transcription-factor-binding-modeling-transcription-factor-genomeepigenome-i", "reader_count"=>84, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>7, "Researcher"=>23, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>1, "Student > Master"=>11, "Student > Bachelor"=>4, "Lecturer"=>2, "Professor"=>8}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>7, "Researcher"=>23, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>1, "Student > Master"=>11, "Student > Bachelor"=>4, "Lecturer"=>2, "Professor"=>8}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>2, "Biochemistry, Genetics and Molecular Biology"=>13, "Mathematics"=>1, "Agricultural and Biological Sciences"=>58, "Medicine and Dentistry"=>1, "Social Sciences"=>1, "Computer Science"=>6}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>58}, "Computer Science"=>{"Computer Science"=>6}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>13}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Belgium"=>1, "United States"=>4, "Norway"=>1, "United Kingdom"=>1, "Germany"=>1, "Spain"=>4}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1305823", "https://ndownloader.figshare.com/files/1305824", "https://ndownloader.figshare.com/files/1305825", "https://ndownloader.figshare.com/files/1305826", "https://ndownloader.figshare.com/files/1305827", "https://ndownloader.figshare.com/files/1305828", "https://ndownloader.figshare.com/files/1305829", "https://ndownloader.figshare.com/files/1305830", "https://ndownloader.figshare.com/files/1305831", "https://ndownloader.figshare.com/files/1305832", "https://ndownloader.figshare.com/files/1305833", "https://ndownloader.figshare.com/files/1305834", "https://ndownloader.figshare.com/files/1305835", "https://ndownloader.figshare.com/files/1305836", "https://ndownloader.figshare.com/files/1305837", "https://ndownloader.figshare.com/files/1305838"], "description"=>"<div><p>Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from <i>in vivo</i> (ChIP-seq) and <i>in vitro</i> experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (<u>A</u>ffinity <u>P</u>rediction by <u>E</u>pigenome and <u>G</u>enome, <a href=\"http://systemsbio.ucsd.edu/apeg\" target=\"_blank\">http://systemsbio.ucsd.edu/apeg</a>).</p></div>", "links"=>[], "tags"=>["transcription", "binding", "modeling", "genome-epigenome"], "article_id"=>870331, "categories"=>["Biological Sciences"], "users"=>["Chieh-Chun Chen", "Shu Xiao", "Dan Xie", "Xiaoyi Cao", "Chun-Xiao Song", "Ting Wang", "Chuan He", "Sheng Zhong"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003367.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s005", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s006", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s007", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s008", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s009", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s010", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s011", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s012", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s013", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s014", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s015", "https://dx.doi.org/10.1371/journal.pcbi.1003367.s016"], "stats"=>{"downloads"=>36, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Understanding_Variation_in_Transcription_Factor_Binding_by_Modeling_Transcription_Factor_Genome_Epigenome_Interactions_/870331", "title"=>"Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-12-05 05:04:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305809"], "description"=>"<p>(A) The relationship between binding probability and epigenetic intensity. Given the transcription factor concentration, the binding probability (y axis) is shown as functions of the intensities (Epigenetic intensity, x axis) and types (solid: activation, dashed: repression) of epigenomic modifications and the strengths of the binding sites (red: strong, blue: weak). For a single binding site, the binding probability is monotonic to the strength of the binding site for all intensities of epigenomic modifications (red curves are always above blue curves). (B) Epigenomic boost of binding-site cooperativity. In the presence of an activation mark, the binding probabilities are monotonic for single strong (red), medium-strength (green), and weak (blue) binding sites. A pair of two weak binding sites has a smaller binding probability in the absence of the activation mark (dotted blue line at Epigenetic intensity = 10<sup>−2</sup>). While the intensity of the activation mark increases, the binding probability of this pair of weak sites gradually surpasses that of the medium-strength site and the strong binding site, breaking the monotonicity of single binding sites. (C) Activation mark H3K4me3 has larger average intensities in weak-TFBS regions (blue) than in strong-TFBS-containing regions (red). SD: standard deviation. (D) The difference of model-predicted binding probabilities with and without the epigenome (y axis) is larger in weak-TFBS-only regions (right column) than in the regions containing both strong and weak sites (mixed, middle column), which in turn is larger than in the strong-TFBS-only regions (left column). (E) H3K4me3 enables larger improvements of prediction accuracy on regions containing weak TFBSs. The predictions of binding for these regions were determined by applying three cutoffs on the predicted binding probability calculated from the model (Weak-cutoff (0.3), Medium-cutoff (0.35), Strong-cutoff (0.4)). The improvement of prediction accuracy was quantified by comparing the predictions by sequence and H3K4me3 to the predictions by sequence only (Y-axis). The improvement was defined as the difference of the proportions of correctly predicted binding regions between using and not using H3K4me3 data. Red, purple, and green bars represent the sequences that contain strong, weak, and mixed TFBSs as determined by PSWM matching scores. Error bars: standard deviations. The number of each type of sequences is in parenthesis.</p>", "links"=>[], "tags"=>["cooperativity", "binding"], "article_id"=>870325, "categories"=>["Biological Sciences"], "users"=>["Chieh-Chun Chen", "Shu Xiao", "Dan Xie", "Xiaoyi Cao", "Chun-Xiao Song", "Ting Wang", "Chuan He", "Sheng Zhong"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003367.g005", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Epigenomic_boost_of_cooperativity_of_weak_binding_sites_/870325", "title"=>"Epigenomic boost of cooperativity of weak binding sites.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 05:04:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305808"], "description"=>"<p>Transcriptional noise is introduced when the binding probability (y axis) between a TF and its target TFBS falls into a particular range (horizontal yellow band). There is nearly no noise above or below this range, because almost all cells would uniformly have this target TFBS in the bound or the unbound state, respectively. The binding probabilities are constrained by the realistic range (vertical blue band) of TF concentrations in eukaryotic cells (x axis). (A) In the presence of a strong binding site (S), the binding probabilities are shown as functions of the TF concentration and the presence of epigenomic marks (Red curve: activation mark, green: no epigenomic marks, blue: repression mark). Activation marks suppress transcriptional noise by reducing the range of feasible binding probabilities, whereas repression marks enhance transcriptional noise. (B) In the presence of a weak binding site (W), both activation (red) and repression (blue) marks tend to suppress transcriptional noise.</p>", "links"=>[], "tags"=>["transcriptional"], "article_id"=>870324, "categories"=>["Biological Sciences"], "users"=>["Chieh-Chun Chen", "Shu Xiao", "Dan Xie", "Xiaoyi Cao", "Chun-Xiao Song", "Ting Wang", "Chuan He", "Sheng Zhong"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003367.g004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Epigenomic_regulation_of_transcriptional_noise_/870324", "title"=>"Epigenomic regulation of transcriptional noise.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 05:04:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305805"], "description"=>"<p>(A) The states of the system and their probabilities. As an example, a hypothetical genomic sequence is occupied by two epigenomic modifications (orange and gray shades), which partially overlap. The sequence contains three TFBSs for two TFs (A and B). The two TFBSs for A (red boxes) are each occupied by one epigenomic modification, and the TFBS for B (green box) is located in the overlapping region of the two modifications. The first TFBS for A (red box on the left) and the TFBS for B are close enough for their bound TFs to interact (arrows in States 5 and 8). Because each of the three TFBSs can reside in either the bound or the unbound state, the whole sequence can reside in a total of 2<sup>3</sup> physical states (listed in the State column). <i>c</i>: a physical state; <i>W</i>(<i>c</i>): Boltzmann weight for state <i>c</i>, which is proportional to the probability that the system visits this state; <i>q<sup>epi</sup></i>: the binding affinity between a transcription factor and the sequence under the epigenomic context. (B) The workflow for inferring epigenetic marks that influence the binding of a TF. Central to this workflow is our epigenome-sensitive TF-DNA binding model (the Epigenetic biophysical model). Inputs to this model are TF binding data (ChIP-seq), PSWM of the TF and epigenomic modification data (ChIP-seq, 5-hmC-seq, MeDIP-seq, and MRE-seq). Outputs of the model include the influences of epigenomic marks to the binding of each transcription factor and the cooperativities between TFBSs.</p>", "links"=>[], "tags"=>["epigenome", "genome"], "article_id"=>870321, "categories"=>["Biological Sciences"], "users"=>["Chieh-Chun Chen", "Shu Xiao", "Dan Xie", "Xiaoyi Cao", "Chun-Xiao Song", "Ting Wang", "Chuan He", "Sheng Zhong"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003367.g001", "stats"=>{"downloads"=>4, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Modeling_the_epigenome_and_the_genome_as_a_physical_system_/870321", "title"=>"Modeling the epigenome and the genome as a physical system.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 05:04:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305810"], "description"=>"<p>The average intensities of H3K9ac and H3K4me2 are higher in DSNDB regions (blue) than in DSDB regions (red). The centers of all NFκB ChIP-seq peaks are superimposed to ‘Position 0’ on the x axis. DSNDB: <u>d</u>ifferent <u>s</u>equence <u>n</u>o <u>d</u>ifference in <u>b</u>inding. DSDB: <u>d</u>ifferent <u>s</u>equence <u>d</u>ifferent <u>b</u>inding. SD: standard deviation.</p>", "links"=>[], "tags"=>["h3k4me2", "dampen"], "article_id"=>870326, "categories"=>["Biological Sciences"], "users"=>["Chieh-Chun Chen", "Shu Xiao", "Dan Xie", "Xiaoyi Cao", "Chun-Xiao Song", "Ting Wang", "Chuan He", "Sheng Zhong"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003367.g006", "stats"=>{"downloads"=>0, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_H3K9ac_and_H3K4me2_dampen_personal_variation_of_NF_954_B_binding_/870326", "title"=>"H3K9ac and H3K4me2 dampen personal variation of NFκB binding.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 05:04:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305807"], "description"=>"<p>Model predicted binding intensities are correlated to ChIP-seq reported binding intensities (y axis: Pearson correlation). The model predictions are based on sequence data alone (Control-1), sequence data plus randomized epigenomic data (Control-2), or sequence data plus one epigenomic mark (other columns). Results on both training data (shaded bars) and testing data (hollow bars) are plotted. Epigenomic marks that significantly improve the predictions of Nanog binding (marked by *) are identified by using the standard deviations of the control experiments (error bars). Combined with the Nanog motif (PSWM) derived from <i>in vivo</i> experiments (red bars), several epigenomic marks can increase the accuracy of predicted binding intensities, achieving Pearson Correlations above 0.47 (H3K4me3 and H3K27ac, red bars). However, combined with the Nanog motif derived from <i>in vitro</i> experiments (blue bars), no epigenetic mark except H3K27ac can improve the predictions of ChIP-seq measurements. Even for H3K27ac, the Pearson Correlation obtained from the <i>in vitro</i> motif (0.29) is much smaller than the Pearson correlation obtained from the <i>in vivo</i> motif (0.47). None of the four measured epigenomic marks in adipose cells help to better predict Nanog binding in stem cells (green bars), suggesting that cell-type-specific epigenetic data are required for increasing the prediction accuracy.</p>", "links"=>[], "tags"=>["marks", "predictions", "nanog"], "article_id"=>870323, "categories"=>["Biological Sciences"], "users"=>["Chieh-Chun Chen", "Shu Xiao", "Dan Xie", "Xiaoyi Cao", "Chun-Xiao Song", "Ting Wang", "Chuan He", "Sheng Zhong"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003367.g003", "stats"=>{"downloads"=>0, "page_views"=>37, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Epigenomic_marks_improve_model_predictions_of_Nanog_binding_/870323", "title"=>"Epigenomic marks improve model predictions of Nanog binding.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 05:04:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305806"], "description"=>"<p>(A) An interaction network between TFs (orange nodes) and epigenetic modifications (blue nodes) in mES cells (p-value cutoff = 0.05). The interactions include positive (red edges) and negative correlations (green edges) of TF binding and epigenetic marks. This network suggests that each TF has its specific epigenetic marks for interaction. (B) TF-specific epigenomic motifs. The influences of every epigenetic mark to the binding of a TF ( in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003367#pcbi.1003367.e041\" target=\"_blank\">Equation (5)</a>) are summarized as a column vector. In analogy to matrix presentation of DNA recognition motifs, we propose to use a column vector to represent the epigenomic motif of a TF. Each column represents an epigenomic motif {, …, }, and the first column is {, …, }.</p>", "links"=>[], "tags"=>["factor-specific", "epigenomic"], "article_id"=>870322, "categories"=>["Biological Sciences"], "users"=>["Chieh-Chun Chen", "Shu Xiao", "Dan Xie", "Xiaoyi Cao", "Chun-Xiao Song", "Ting Wang", "Chuan He", "Sheng Zhong"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003367.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transcription_factor_specific_epigenomic_codes_/870322", "title"=>"Transcription factor-specific epigenomic codes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 05:04:27"}

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Relative Metric

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