A General Pairwise Interaction Model Provides an Accurate Description of In Vivo Transcription Factor Binding Sites
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{"title"=>"A general pairwise interaction model provides an accurate description of in Vivo transcription factor binding sites", "type"=>"journal", "authors"=>[{"first_name"=>"Marc", "last_name"=>"Santolini", "scopus_author_id"=>"55669619900"}, {"first_name"=>"Thierry", "last_name"=>"Mora", "scopus_author_id"=>"6701596511"}, {"first_name"=>"Vincent", "last_name"=>"Hakim", "scopus_author_id"=>"56024787900"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84903186941", "sgr"=>"84903186941", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0099015", "pmid"=>"24926895", "isbn"=>"1932-6203", "pui"=>"373380245"}, "id"=>"30686166-dc8f-3453-8435-9e6f0a2a940c", "abstract"=>"The identification of transcription factor binding sites (TFBSs) on genomic DNA is of crucial importance for understanding and predicting regulatory elements in gene networks. TFBS motifs are commonly described by Position Weight Matrices (PWMs), in which each DNA base pair contributes independently to the transcription factor (TF) binding. However, this description ignores correlations between nucleotides at different positions, and is generally inaccurate: analysing fly and mouse in vivo ChIPseq data, we show that in most cases the PWM model fails to reproduce the observed statistics of TFBSs. To overcome this issue, we introduce the pairwise interaction model (PIM), a generalization of the PWM model. The model is based on the principle of maximum entropy and explicitly describes pairwise correlations between nucleotides at different positions, while being otherwise as unconstrained as possible. It is mathematically equivalent to considering a TF-DNA binding energy that depends additively on each nucleotide identity at all positions in the TFBS, like the PWM model, but also additively on pairs of nucleotides. We find that the PIM significantly improves over the PWM model, and even provides an optimal description of TFBS statistics within statistical noise. The PIM generalizes previous approaches to interdependent positions: it accounts for co-variation of two or more base pairs, and predicts secondary motifs, while outperforming multiple-motif models consisting of mixtures of PWMs. We analyse the structure of pairwise interactions between nucleotides, and find that they are sparse and dominantly located between consecutive base pairs in the flanking region of TFBS. Nonetheless, interactions between pairs of non-consecutive nucleotides are found to play a significant role in the obtained accurate description of TFBS statistics. The PIM is computationally tractable, and provides a general framework that should be useful for describing and predicting TFBSs beyond PWMs.", "link"=>"http://www.mendeley.com/research/general-pairwise-interaction-model-provides-accurate-description-vivo-transcription-factor-binding-s", "reader_count"=>38, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Researcher"=>8, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>7, "Lecturer > Senior Lecturer"=>1, "Professor"=>1, "Other"=>1, "Student > Bachelor"=>6}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Researcher"=>8, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>7, "Lecturer > Senior Lecturer"=>1, "Professor"=>1, "Other"=>1, "Student > Bachelor"=>6}, "reader_count_by_subject_area"=>{"Engineering"=>7, "Biochemistry, Genetics and Molecular Biology"=>4, "Agricultural and Biological Sciences"=>17, "Physics and Astronomy"=>7, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>7}, "Physics and Astronomy"=>{"Physics and Astronomy"=>7}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>17}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>3, "Germany"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1535082"], "description"=>"<p>We study the effect of restricting the PIM to nearest-neighbor interactions, resulting in the NNM. (A) The BIC is shown for the PIM (red crosses) and NNM (cyan dots) as a function of the number of interactions added. Shade from light to dark indicates the iteration, similarly to <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099015#pone-0099015-g005\" target=\"_blank\">Figure 5</a>. The NNM performs less well than the PIM, which provides a quantitative ground for the addition of non-neighbor interactions. (B) Comparison between the observed and predicted frequencies of TFBS according to the PWM, NNM, and PIM. We show the number of added interactions for the PIM and NNM in the legend of each plot. (C) DKLs between the NNM or PIM predicted distributions, and the observed distribution, with the number of parameters that is optimal for the NNM (first two bars) and with the number of parameters that is optimal for the PIM (last bar). The improvement yielded by the PIM is clearly seen for factors like Klf4, CTCF, E2f4 or MyoD. (D) Cumulative distribution of nearest-neighbor (red) and non nearest-neighbor (black) interactions added as a function of the number of interactions added (ranked by strength).</p>", "links"=>[], "tags"=>["Biochemistry", "dna", "Nucleic acids", "biophysics", "Biophysics theory", "cell biology", "Signal transduction", "cell signaling", "Transcriptional signaling", "Molecular cell biology", "Computational biology", "genetics", "gene expression", "DNA transcription", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Sequence analysis", "Theoretical biology", "physics", "Statistical mechanics", "nearest-neighbor"], "article_id"=>1057453, "categories"=>["Biological Sciences"], "users"=>["Marc Santolini", "Thierry Mora", "Vincent Hakim"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0099015.g008", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_with_the_Nearest_Neighbor_Model_NNM_/1057453", "title"=>"Comparison with the Nearest-Neighbor Model (NNM).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-13 04:40:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1535096"], "description"=>"<div><p>The identification of transcription factor binding sites (TFBSs) on genomic DNA is of crucial importance for understanding and predicting regulatory elements in gene networks. TFBS motifs are commonly described by Position Weight Matrices (PWMs), in which each DNA base pair contributes independently to the transcription factor (TF) binding. However, this description ignores correlations between nucleotides at different positions, and is generally inaccurate: analysing fly and mouse <i>in vivo</i> ChIPseq data, we show that in most cases the PWM model fails to reproduce the observed statistics of TFBSs. To overcome this issue, we introduce the pairwise interaction model (PIM), a generalization of the PWM model. The model is based on the principle of maximum entropy and explicitly describes pairwise correlations between nucleotides at different positions, while being otherwise as unconstrained as possible. It is mathematically equivalent to considering a TF-DNA binding energy that depends additively on each nucleotide identity at all positions in the TFBS, like the PWM model, but also additively on pairs of nucleotides. We find that the PIM significantly improves over the PWM model, and even provides an optimal description of TFBS statistics within statistical noise. The PIM generalizes previous approaches to interdependent positions: it accounts for co-variation of two or more base pairs, and predicts secondary motifs, while outperforming multiple-motif models consisting of mixtures of PWMs. We analyse the structure of pairwise interactions between nucleotides, and find that they are sparse and dominantly located between consecutive base pairs in the flanking region of TFBS. Nonetheless, interactions between pairs of non-consecutive nucleotides are found to play a significant role in the obtained accurate description of TFBS statistics. The PIM is computationally tractable, and provides a general framework that should be useful for describing and predicting TFBSs beyond PWMs.</p></div>", "links"=>[], "tags"=>["Biochemistry", "dna", "Nucleic acids", "biophysics", "Biophysics theory", "cell biology", "Signal transduction", "cell signaling", "Transcriptional signaling", "Molecular cell biology", "Computational biology", "genetics", "gene expression", "DNA transcription", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Sequence analysis", "Theoretical biology", "physics", "Statistical mechanics", "pairwise", "provides", "transcription", "binding", "sites"], "article_id"=>1057467, "categories"=>["Biological Sciences"], "users"=>["Marc Santolini", "Thierry Mora", "Vincent Hakim"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0099015", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_General_Pairwise_Interaction_Model_Provides_an_Accurate_Description_of_In_Vivo_Transcription_Factor_Binding_Sites/1057467", "title"=>"A General Pairwise Interaction Model Provides an Accurate Description of <i>In Vivo</i> Transcription Factor Binding Sites", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-06-13 04:40:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1535078"], "description"=>"<p>The DNA sequence variety described by each model is illustrated using the software WebLogo <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099015#pone.0099015-Crooks1\" target=\"_blank\">[52]</a>. Shown are PWMs built from all TFBS, from the PWM-mixture model, and from the basins of attraction of the PIM for Twist (A), Esrrb (B), and MyoD (C). The attractor PWMs are grouped under the mixture PWMs with smallest distance (measured by DKL, in bits). Heatmaps showing the DKLs between attractor PWMs and mixture PWMs are displayed on the right for each factor (minimal DKLs are in black). The proportions of binding sites used for each logo are also indicated and serve to denote the corresponding PWM.</p>", "links"=>[], "tags"=>["Biochemistry", "dna", "Nucleic acids", "biophysics", "Biophysics theory", "cell biology", "Signal transduction", "cell signaling", "Transcriptional signaling", "Molecular cell biology", "Computational biology", "genetics", "gene expression", "DNA transcription", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Sequence analysis", "Theoretical biology", "physics", "Statistical mechanics", "corresponding", "basins"], "article_id"=>1057449, "categories"=>["Biological Sciences"], "users"=>["Marc Santolini", "Thierry Mora", "Vincent Hakim"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0099015.g006", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PWMs_corresponding_to_the_different_basins_of_attraction_of_the_PIM_/1057449", "title"=>"PWMs corresponding to the different basins of attraction of the PIM.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-13 04:40:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1535075"], "description"=>"<p>(A) Venn diagrams showing the overlap between the ChIP predicted by the PWM model(blue) and PIM (red). (B) Difference (one minus the proportion of shared binding sites) between the best binding sites predicted by the PIM and PWM model on ChIPseq peaks (light red), and the same quantity when including the next best predicted binding sites on each peak (dark red). In several cases (<i>e.g.</i> Fosl1, Max, N-Myc, Srf, STAT3, Usf1), the difference between predicted binding sites is much smaller when the two best binding sites are considered, indicating that the PIM and the PWM model rank differently the two best binding sites in ChIP peaks with multiple bound sites.</p>", "links"=>[], "tags"=>["Biochemistry", "dna", "Nucleic acids", "biophysics", "Biophysics theory", "cell biology", "Signal transduction", "cell signaling", "Transcriptional signaling", "Molecular cell biology", "Computational biology", "genetics", "gene expression", "DNA transcription", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Sequence analysis", "Theoretical biology", "physics", "Statistical mechanics"], "article_id"=>1057447, "categories"=>["Biological Sciences"], "users"=>["Marc Santolini", "Thierry Mora", "Vincent Hakim"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0099015.g004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overlap_between_predicted_TFBSs_/1057447", "title"=>"Overlap between predicted TFBSs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-13 04:40:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1535077"], "description"=>"<p>(A) Minimisation of the Bayesian information criterion (BIC, see <i>Methods</i>) is used to select the optimal number of model parameters and avoid over-fitting the training set. The evolution of the BIC is shown for the PIM (red crosses) and the PWM-mixture model (green lines) as a function of the number of model parameters. Shades from light to dark indicate the iteration number (main loop in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099015#pone-0099015-g001\" target=\"_blank\">Figure 1</a>), the darkest shade being assigned to the final model. (B) Kullback-Leibler divergences (DKL) between the PWM, PWM-mixture and PIM distributions and the observed distribution for the different TFs, for the BIC optimal parameters. In all cases the PIM outcompetes both the PWM and PWM-mixture models. The DKL between the PIM and a finite-size distribution of sequences drawn from it is also displayed (pink, see <i>Methods</i>) to assess the DKL magnitude simply due to the finite number of TFBS in the dataset. The result show that the PIM generally fits the available dataset as well as possible given its finite size. Error bars represent two standard deviations.</p>", "links"=>[], "tags"=>["Biochemistry", "dna", "Nucleic acids", "biophysics", "Biophysics theory", "cell biology", "Signal transduction", "cell signaling", "Transcriptional signaling", "Molecular cell biology", "Computational biology", "genetics", "gene expression", "DNA transcription", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Sequence analysis", "Theoretical biology", "physics", "Statistical mechanics"], "article_id"=>1057448, "categories"=>["Biological Sciences"], "users"=>["Marc Santolini", "Thierry Mora", "Vincent Hakim"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0099015.g005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_selection_/1057448", "title"=>"Model selection.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-13 04:40:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1535090"], "description"=>"<p>For each TF, we show the Participation Ratios computed for the Normalized Direct Information and Normalized Mutual Information matrices (see <i>Methods</i>). Interactions are generally more localized than correlations.</p>", "links"=>[], "tags"=>["Biochemistry", "dna", "Nucleic acids", "biophysics", "Biophysics theory", "cell biology", "Signal transduction", "cell signaling", "Transcriptional signaling", "Molecular cell biology", "Computational biology", "genetics", "gene expression", "DNA transcription", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Sequence analysis", "Theoretical biology", "physics", "Statistical mechanics"], "article_id"=>1057461, "categories"=>["Biological Sciences"], "users"=>["Marc Santolini", "Thierry Mora", "Vincent Hakim"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0099015.t002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Participation_Ratios_/1057461", "title"=>"Participation Ratios.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-06-13 04:40:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1535074"], "description"=>"<p>Similarly to <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099015#pone-0099015-g002\" target=\"_blank\">Figure 2</a>, we show the observed frequencies (gray bars) of the most represented TFBSs for Twist (A), Esrrb (B) and MyoD (C) TFs, together with the probabilities of these sequences predicted by the PWM model (blue bars), the PIM taking into account interactions between nucleotides (red bars), and the PWM-mixture model (green bars). (B,D,F) show the comparison between frequencies for all binding sequences and predicted sequence probabilities for the three models (same color code). The predicted probabilities of the PIM and to a lesser extent of the mixture model are in much better agreement with the observed frequencies than those of the PWM model.</p>", "links"=>[], "tags"=>["Biochemistry", "dna", "Nucleic acids", "biophysics", "Biophysics theory", "cell biology", "Signal transduction", "cell signaling", "Transcriptional signaling", "Molecular cell biology", "Computational biology", "genetics", "gene expression", "DNA transcription", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Sequence analysis", "Theoretical biology", "physics", "Statistical mechanics", "correlations", "tfbs"], "article_id"=>1057446, "categories"=>["Biological Sciences"], "users"=>["Marc Santolini", "Thierry Mora", "Vincent Hakim"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0099015.g003", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Models_with_correlations_improve_TFBS_statistics_prediction_/1057446", "title"=>"Models with correlations improve TFBS statistics prediction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-13 04:40:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1535089"], "description"=>"<p>For each TF, we show the number of ChIP sequences retrieved, the numbers , , and of different ChIP sequences used for training between either two models, and the numbers , , and of TFBSs used to learn each model.</p>", "links"=>[], "tags"=>["Biochemistry", "dna", "Nucleic acids", "biophysics", "Biophysics theory", "cell biology", "Signal transduction", "cell signaling", "Transcriptional signaling", "Molecular cell biology", "Computational biology", "genetics", "gene expression", "DNA transcription", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Sequence analysis", "Theoretical biology", "physics", "Statistical mechanics"], "article_id"=>1057460, "categories"=>["Biological Sciences"], "users"=>["Marc Santolini", "Thierry Mora", "Vincent Hakim"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0099015.t001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Information_about_the_TFs_/1057460", "title"=>"Information about the TFs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-06-13 04:40:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1535079"], "description"=>"<p>(A) Heat maps showing the values of the Normalized Direct Information between pairs of nucleotides. The matrix is symmetric by definition. PWMs are shown on the side for better visualization of the interacting nucleotides. The participation ratio R is indicated below each heat map. (B) Distances between interacting nucleotides. The box plots show the relative importance of the Normalized Direct Information as a function of the distance between interacting nucleotides. Red dots denote average values. (C) Sum of normalized direct informations in the TFBSs at a given position, averaged over all considered factors (blue line). The average site information content relative to background as a function of position is also shown (red line). In both quantities, the average over the two TFBS orientations has been taken.</p>", "links"=>[], "tags"=>["Biochemistry", "dna", "Nucleic acids", "biophysics", "Biophysics theory", "cell biology", "Signal transduction", "cell signaling", "Transcriptional signaling", "Molecular cell biology", "Computational biology", "genetics", "gene expression", "DNA transcription", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Sequence analysis", "Theoretical biology", "physics", "Statistical mechanics", "nucleotide", "pairwise"], "article_id"=>1057450, "categories"=>["Biological Sciences"], "users"=>["Marc Santolini", "Thierry Mora", "Vincent Hakim"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0099015.g007", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Location_and_strength_of_the_nucleotide_pairwise_interactions_/1057450", "title"=>"Location and strength of the nucleotide pairwise interactions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-13 04:40:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1535073"], "description"=>"<p>Given a set of TFBSs predicted by the PWM model on ChIP fragments, we computed the TFBS frequencies (how many times a given sequence appears in the set, gray bars), and compared them to the PWM predicted frequencies (blue bars) computed using single nucleotide frequencies alone. We show the results for the most frequent sequences for the TFs Twist (A), Esrrb (B) and MyoD (C). We can see that the use of single nucleotide frequencies alone does not allow one to reproduce the statistics of the most observed binding sites. (D) Kullback-Leibler Divergence (DKL) between the observed probability distribution and the PWM model distribution (blue). As a control we show the mean (cyan bars) along with two standard deviations of the DKL between the PWM model and a finite sample drawn from it (see Methods). A significant discrepancy between the observed and predicted sequence probabilities is reported for 22 out of 28 factors.</p>", "links"=>[], "tags"=>["Biochemistry", "dna", "Nucleic acids", "biophysics", "Biophysics theory", "cell biology", "Signal transduction", "cell signaling", "Transcriptional signaling", "Molecular cell biology", "Computational biology", "genetics", "gene expression", "DNA transcription", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Sequence analysis", "Theoretical biology", "physics", "Statistical mechanics", "tfbs", "frequencies", "poorly", "pwm"], "article_id"=>1057445, "categories"=>["Biological Sciences"], "users"=>["Marc Santolini", "Thierry Mora", "Vincent Hakim"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0099015.g002", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Observed_TFBS_frequencies_are_poorly_predicted_by_a_PWM_model_/1057445", "title"=>"Observed TFBS frequencies are poorly predicted by a PWM model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-13 04:40:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1535088"], "description"=>"<p>The full interaction matrix is approximated by a matrix built from the Hopfield patterns with highest eigenvalue moduli. We show the Normalized Direct Information matrices computed from , , and the full matrix . For MyoD, the correspondence between successive pairs of patterns and distinct interaction domains (middle, upper left and bottom right) is particularly clear. In all cases the full Direct Information matrix is already well approximated by . The bottom plots show histograms of the eigenvalues of highest moduli (red) and of the other ones (blue). The high eigenvalues lie on both sides of a core of smaller eigenvalues centered around .</p>", "links"=>[], "tags"=>["Biochemistry", "dna", "Nucleic acids", "biophysics", "Biophysics theory", "cell biology", "Signal transduction", "cell signaling", "Transcriptional signaling", "Molecular cell biology", "Computational biology", "genetics", "gene expression", "DNA transcription", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Sequence analysis", "Theoretical biology", "physics", "Statistical mechanics", "interactions", "hopfield"], "article_id"=>1057459, "categories"=>["Biological Sciences"], "users"=>["Marc Santolini", "Thierry Mora", "Vincent Hakim"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0099015.g009", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Representation_of_interactions_by_Hopfield_patterns_/1057459", "title"=>"Representation of interactions by Hopfield patterns.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-13 04:40:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/1535072"], "description"=>"<p>An initial Position Weight Matrix (PWM) is used to find a set of binding sites on ChIPseq data. Models are then learned using single-point frequencies (PWM), two-point correlations (PIM) or a mixture of PWM models learned on sites clustered by K-Means with increasing complexity, <i>i.e.</i> increasing number of features in the model. Finally the models with best Bayesian Information Criteria (BIC) are used to predict new binding sites until convergence to a stable set of TFBSs.</p>", "links"=>[], "tags"=>["Biochemistry", "dna", "Nucleic acids", "biophysics", "Biophysics theory", "cell biology", "Signal transduction", "cell signaling", "Transcriptional signaling", "Molecular cell biology", "Computational biology", "genetics", "gene expression", "DNA transcription", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Sequence analysis", "Theoretical biology", "physics", "Statistical mechanics"], "article_id"=>1057444, "categories"=>["Biological Sciences"], "users"=>["Marc Santolini", "Thierry Mora", "Vincent Hakim"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0099015.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Workflow_/1057444", "title"=>"Workflow.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-13 04:40:41"}

PMC Usage Stats | Further Information

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Relative Metric

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