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558 results for “Transcription Dynamics”
Analysis accompanying "Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the Drosophila larval brain"
<p>This repository documents the raw data processing and figure generation for the article “Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the <em>Drosophila </em>larval brain”, doi: 10.1261/rna.079552.122.</p>
Analysis Products: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains analysis products for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>. Please refer to the READMEs in the directories, which are summarized below.</p> <p>The record contains the following files:<br> <br> `clusters.tsv`: <strong> </strong>contains the cluster id, name and colour of clusters in the paper</p> <p><strong>scATAC.zip</strong></p> <p>Analysis products for the single-cell ATAC-seq data. Contains:</p> <p>- `cells.tsv`: list of barcodes that pass QC. Columns include:<br> - `barcode`<br> - `sample`: (time point)<br> - `umap1`<br> - `umap2`<br> - `cluster`<br> - `dpt_pseudotime_fibr_root`: pseudotime values treating a fibroblast cell as root<br> - `dpt_pseudotime_xOSK_root`: pseudotime values treating xOSK cell as root<br> - `peaks.bed`: list of peaks of 500bp across all cell states. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `features.tsv`: 50 dimensional representation of each cell <br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed` </p> <p><strong>scATAC_clusters.zip</strong></p> <p>Analysis products corresponding to cluster pseudo-bulks of the single-cell ATAC-seq data. </p> <p>- `clusters.tsv`: contains the cluster id, name and colour used in the paper<br> - `peaks`: contains `overlap_reproducibilty/overlap.optimal_peak` peaks called using ENCODE bulk ATAC-seq pipeline in the narrowPeak format.<br> - `fragments`: contains per cluster fragment files </p> <p><strong>scATAC_scRNA_integration.zip</strong></p> <p>Analysis products from the integration of scATAC with scRNA. Contains:</p> <p>- `peak_gene_links_fdr1e-4.tsv`: file with peak gene links passing FDR 1e-4. For analyses in the paper, we filter to peaks with absolute correlation >0.45.<br> - `harmony.cca.30.feat.tsv`: 30 dimensional co-embedding for scATAC and scRNA cells obtained by CCA followed by applying Harmony over assay type.<br> - `harmony.cca.metadata.tsv`: UMAP coordinates for scATAC and scRNA cells derived from the Harmony CCA embedding. First column contains barcode.</p> <p><strong>scRNA.zip</strong></p> <p>Analysis products for the single-cell RNA-seq data. Contains:</p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca), knn graphs, all associated metadata. Note that barcode suffix (1-9 corresponds to samples D0, D2, ..., D14, iPSC)<br> - `genes.txt`: list of all genes<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1-9 corresponding to D0, D2, ..., D14, iPSC) <br> - `sample`: sample name (D0, D2, .., D14, iPSC)<br> - `umap1`<br> - `umap2`<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `cluster`<br> - `percent.mt`: percent of mitochondrial transcripts in cell<br> - `percent.oskm`: percent of OSKM transcripts in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` <br> - `pca.tsv`: first 50 PC of each cell<br> - `oskm_endo_sendai.tsv`: estimated raw counts (cts, may not be integers) and log(1+ tp10k) normalized expression (norm) for endogenous and exogenous (Sendai derived) counts of POU5F1 (OCT4), SOX2, KLF4 and MYC genes. Rows are consistent with `seurat.rds` and `cells.tsv`</p> <p><strong>multiome.zip</strong></p> <p><em>multiome/snATAC:</em></p> <p>These files are derived from the integration of nuclei from multiome (D1M and D2M), with cells from day 2 of scATAC-seq (labeled D2). </p> <p>- `cells.tsv`: This is the list of nuclei barcodes that pass QC from multiome AND also cell barcodes from D2 of scATAC-seq. Includes:<br> - `barcode`<br> - `umap1`: These are the coordinates used for the figures involving multiome in the paper.<br> - `umap2`: ^^^ <br> - `sample`: D1M and D2M correspond to multiome, D2 corresponds to day 2 of scATAC-seq<br> - `cluster`: For multiome barcodes, these are labels transfered from scATAC-seq. For D2 scATAC-seq, it is the original cluster labels. <br> - `peaks.bed`: This is the same file as scATAC/peaks.bed. List of peaks of 500bp. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`.<br> - `features.no.harmony.50d.tsv`: 50 dimensional representation of each cell prior to running Harmony (to correct for batch effect between D2 scATAC and D1M,D2M snMultiome). Rows correspond to cells from `cells.tsv`.<br> - `features.harmony.10d.tsv`: 10 dimensional representation of each cell after running Harmony. Rows correspond to cells from `cells.tsv`.</p> <p><em>multiome/snRNA:</em></p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca),associated metadata. Note that barcode suffix (1,2 corresponds to samples D1M, D2M). Please use the UMAP/features from snATAC/ for consistency.<br> - `genes.txt`: list of all genes (this is different from the list in scRNA analysis)<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1,2 corresponding to D1M, D2M respectively) <br> - `sample`: sample name (D1M, D2M)<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `percent.oskm`: percent of OSKM genes in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` </p>
Supporting data for "Dynamics of RNA polymerase II and elongation factor Spt4/5 recruitment during activator-dependent transcription"
<p>Supporting data for</p> <p><strong>Dynamics of RNA polymerase II and elongation factor Spt4/5 recruitment</strong></p> <p><strong>during activator-dependent transcription </strong></p> <p>Grace A. Rosen<sup>a,1</sup>, Inwha Baek<sup>b,1</sup>, Larry J. Friedman<sup>a</sup>, Yoo Jin Joo<sup>b</sup>, Stephen Buratowski<sup>b,2</sup>, Jeff Gelles<sup>a,2</sup></p> <p><sup>a</sup>Department of Biochemistry, Brandeis University, Waltham, Massachusetts 02454, USA.</p> <p><sup>b</sup>Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, USA.</p> <p><sup>1</sup>Equal contributions</p> <p><sup>2</sup>Corresponding authors: <a href="mailto:steveb@hms.harvard.edu">steveb@hms.harvard.edu</a>; +1 (617) 432-0696 (S.B.) and <a href="mailto:gelles@brandeis.edu">gelles@brandeis.edu</a>; +1 (781) 736-2377 (J.G.)</p> <p>See <strong>Source data index.pdf</strong> for description of files.</p>
Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023
<p>Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023</p> <p> </p> <p> </p>
Data from: Foraging-induced craniofacial plasticity is associated with an early, robust, and dynamic transcriptional response
<p>Phenotypic plasticity is the ability of a single genotype to vary its phenotype in response to the environment. Plasticity of the skeletal system in response to mechanical input is widely studied, but the timing of its transcriptional regulation is not well-understood. Here we used the cichlid feeding apparatus to examine the transcriptional dynamics of skeletal plasticity over time. Using three closely related species that vary in their ability to remodel bone and a panel of 11 genes, including well studied skeletal differentiation markers and newly characterized environmentally sensitive genes, we examined plasticity at 1, 2, 4 and 8 weeks following the onset of alternate foraging challenges. We found that the plastic species exhibited environment-specific bursts in gene expression at 1 week, followed by a sharp decline in levels, while the species with more limited plasticity exhibited consistently low levels of gene expression. This trend held across nearly all genes, suggesting that it is a hallmark of the larger plasticity regulatory network. We conclude that plasticity of the cichlid feeding apparatus is not the result of slowly accumulating gene expression difference over time, but rather is stimulated by early bursts of environment-specific gene expression followed by a return to homeostatic levels.</p>
Transcription feedback dynamics in the wake of cytoplasmic mRNA degradation shutdown
<p>In the last decade, multiple studies demonstrated that cells maintain a balance of mRNA production and degradation, but the mechanisms by which cells implement this balance remain unknown. Here, we monitored cells’ total and recently-transcribed mRNA profiles immediately following an acute depletion of Xrn1—the main 5′-3′ mRNA exonuclease—which was previously implicated in balancing mRNA levels. We captured the detailed dynamics of the adaptation to rapid degradation of Xrn1 and observed a significant accumulation of mRNA, followed by a delayed global reduction in transcription and a gradual return to baseline mRNA levels. We found that this transcriptional response is not unique to Xrn1 depletion; rather, it is induced earlier when upstream factors in the 5′-3′ degradation pathway are perturbed. Our data suggest that the mRNA feedback mechanism monitors the accumulation of inputs to the 5′-3′ exonucleolytic pathway rather than its outputs.</p>
Single-molecule dynamics and genome-wide transcriptomics reveal that NF-kB (p65)-DNA binding times can be decoupled from transcriptional activation
<p>Data and analysis code repository for: </p> <p>Single-molecule dynamics and genome-wide transcriptomics reveal that NF-kB (p65)-DNA binding times can be decoupled from transcriptional activation</p> <p>https://doi.org/10.1101/255380</p>
Interaction of N-3-oxododecanoyl homoserine lactone with transcriptional regulator LasR of Pseudomonas aeruginosa: Insights from molecular docking and dynamics simulations
<p>Dataset and supplementary files of the research: Interaction of N-3-oxododecanoyl homoserine lactone with transcriptional regulator LasR of Pseudomonas aeruginosa: Insights from molecular docking and dynamics simulations (https://doi.org/10.1101/121681)</p> <p>- Supporting Information</p> <p>- Input: Parameters and initial structures</p> <p>- Output: Trajectories, Docking poses</p> <p>Gromacs (multi-core with CUDA) was used for the simulations.</p> <p>Autodock Vina, FlexAid and rDock were used for molecular docking.</p>
Decoding the Epigenetics and Chromatin Loop Dynamics of Androgen Receptor-Mediated Transcription
<p>This repository stores the datasets for the "Decoding the Dynamic Regulation and Chromatin Architecture of Androgen Receptor-mediated Gene Expression" paper.</p>
Raw Data supporting Promoters adopt distinct dynamic manifestations depending on transcription factor context
<p>All source data is provided as MAT-files, which can be opened in Matlab. In total, the source data contain 270 core data files and 540 processed data files. The data for each individual promoter is stored in a different directory and the 9 promoters are:</p> <ul> <li> <p><em>ALD3 </em></p> </li> <li> <p><em>DCS2 </em></p> </li> <li> <p><em>DDR2 </em></p> </li> <li> <p><em>HXK1 </em></p> </li> <li> <p><em>RTN2 </em></p> </li> <li> <p><em>TKL2 </em></p> </li> <li> <p><em>SIP18 </em></p> </li> <li> <p><em>pSIP18_mut6</em> (also referred to as mutant A4)</p> </li> <li> <p><em>pSIP18_mut21</em> (also referred to as mutant D6)</p> </li> </ul> <p>The data for the first 7 promoters was previously reported in (Hansen and O’Shea, 2013), though in an unnormalized form. That is, it was previously reported as a concentration per cell in arbitrary fluorescence units (AU). In the present manuscript, we have calibrated the data to obtain absolute abundances, such that the MAT-files now contain both the old AU concentration as well as absolute abundances, i.e. number of YFP molecules per cell. The calibration was performed as described in (Huang et al., 2016). Similarly, the data for the last 2 promoters (A4 and D6) was previously reported in its unnormalized form in (Hansen and O’Shea, 2015) and it is here also reported in the form of absolute abundances.</p> <p> </p> <p>The MAT-files containing the raw data have the suffix “_size.mat”. The name of the MAT files describes the experiment. If the file name contains “DM”, then it is a single pulse. Thus, “SIP18_DM_40min_275nM_size.mat” refers to a single 40 min pulse with 275 nM 1-NM-PP1 for the <em>SIP18</em> promoter. Similarly if the file name contains “FM”, e.g. “RTN2_FM_8_5min_690nM_size.mat” then it refers to eight 5 min pulses separated by 5 min intervals at 690 nM for the <em>RTN2</em> promoter. Finally, if the file name contains “FM4”, e.g. “TKL2_FM4_15minINT_690nM_size.mat” then the experiment was four 5 min pulses separated by 15 min intervals at 690 nM for the <em>TKL2</em> promoter. The concentration is the concentration of 1-NM-PP1 that was used and 100 nM, 275 nM, 690 nM and 3mM refers to approximately, 25%, 50%, 75% and 100% Msn2 activation. For full experimental details please see (Hansen and O’Shea, 2013; Hansen et al., 2015).</p> <p> </p> <p>The “_size.mat” MAT-files contain the following variables:</p> <ul> <li> cell_size_pixels</li> <li> CFP</li> <li> CFP_molecules</li> <li> CFP_raw</li> <li> inhibitor_conc</li> <li> MSN2_raw</li> <li> MSN2_RFP</li> <li> pulse_parameters • time</li> <li> YFP</li> <li> YFP_molecules</li> <li> YFP_raw</li> </ul> <p>CFP, CFP_molecules, CFP_raw and YFP, YFP_molecules, YFP_raw are Nx64 matrices, where each row N correspond to a different cell and the 64 columns correspond to the 64 experimentally measured timepoints corresponding to the “time” vector running from -5 min to 152.5 min in increments of 2.5 min and the 1NM-PP1 inhibitor was added at time 0. “CFP_raw” and “YFP_raw” contains raw, uncorrected data, so without photobleaching correction and background subtraction. “CFP” and “YFP” contain corrected data in arbitrary fluorescence units (AU) and report on the concentration (i.e. size normalized). Finally, “CFP_molecules” and “YFP_molecules” contains the total number of CFP and YFP molecules per cell (i.e. this is not a concentration, but the absolute abundance). The area of each cell at each timepoint can be found in the matrix “cell_size_pixels”. Since the cells are live and growing, this will tend to increase during the experiments. Occasionally large fluctuations can occur due to errors in cell segmentation or due to division. For full details on the image analysis and cell segmentation, please see (Hansen and O’Shea, 2013; Hansen et al., 2015).</p> <p> </p> <p>The variables “inhibitor_conc” and “pulse_parameters” refer to the type of experiment and is also given by the name. “inhibitor_conc” gives the 1NMPP1 concentration: 100 nM, 275 nM, 690 nM or 3000 nM. “pulse_parameters” contains either 2 or 3 elements and given the dynamical pulse sequence parameters. Column 1 contains the number of pulses and column 2 the duration of the pulses. Column 3 gives the interval between the pulses if more than one pulse is used – otherwise column 3 is zero.</p> <p> </p> <p>Moreover, on a more technical note it should be noted that the signal-to-noise of the CFP reporter is worse than the YFP reporter. Therefore, we always use the YFP reporter for quantitative analysis. Furthermore, the two other MAT-files “…MSN2.mat” and “…YFP.mat” contain processed data. Please see the ReadMe file on the code for a full description and how these were derived.</p> <p> </p> <p>Finally, Supplementary Table 1 contains the model-inferred parameters for each promoter and condition.</p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Hansen, A.S., and O’Shea, E.K. (2013). Promoter decoding of transcription factor dynamics involves a trade-off between noise and control of gene expression. Mol. Syst. Biol.</p> <p>Hansen, A.S., and O’Shea, E.K. (2015). Cis Determinants of Promoter Threshold and Activation Timescale. Cell Rep.</p> <p>Hansen, A.S., Hao, N., and OShea, E.K. (2015). High-throughput microfluidics to control and measure signaling dynamics in single yeast cells. Nat. Protoc.</p> <p>Huang, L., Pauleve, L., Zechner, C., Unger, M., Hansen, A.S., and Koeppl, H. (2016). Reconstructing dynamic molecular states from single-cell time series. J. R. Soc. Interface.</p>
Data for "Dynamic switching of transcriptional regulators between two distinct low-mobility chromatin states"
<p>This deposit contains all the single-molecule trajectories reported in "Dynamic switching of transcriptional regulators between two distinct low-mobility chromatin states", Science Advances, 2023.</p> <p>To access the tracks, open the mat file in MATLAB. This contains a MATLAB table with the following fields:</p> <p><strong>summary_table.cell_protein{i}</strong> identifies the i<sup>th</sup> dataset i.e. cell line + protein + treatment.</p> <p><strong>summary_table.X{i}{j}</strong> is an Nx2 array of x and y coordinates (in microns) for track j in condition i. N is the number of localizations in that track.</p> <p>Time interval between localizations is 200 ms.</p> <p>Details on data acquisition and tracking parameters can be found in the associated manuscript.</p>
Stochastic motion and transcriptional dynamics of pairs of distal DNA loci on a compacted chromosome
<p>This repository contains the trajectory data from "Stochastic motion and transcriptional dynamics of pairs of distal DNA loci on a compacted chromosome".</p> <p><strong>Data sets</strong></p> <p>We provide data sets for the following constructs and imaging conditions, where we give the name of the construct and the MS2-parS genomic separation in kb:</p> <ul> <li>data_line0.csv: parS-homie-evePr-PP7, 58 kb</li> <li>data_line1.csv: parS-homie-evePr-PP7, 82 kb</li> <li>data_line2.csv: parS-homie-evePr-PP7, 88 kb</li> <li>data_line3.csv: parS-homie-evePr-PP7, 149 kb</li> <li>data_line4.csv: parS-homie-evePr-PP7, 190 kb</li> <li>data_line5.csv: parS-homie-evePr-PP7, 595 kb</li> <li>data_line6.csv: parS-homie-evePr-PP7, 3.3 Mb</li> <li>data_line3_5s.csv: parS-homie-evePr-PP7, 149 kb, 5 second time interval</li> <li>data_line0_nohomie.csv: parS-lambda-evePr-PP7, 58 kb</li> <li>data_line3_nohomie.csv: parS-lambda-evePr-PP7, 149 kb</li> </ul> <p><strong>Structure of Data</strong></p> <p>The trajectory data are provided as csv files consisting of 13 columns. The column headers are:</p> <ul> <li>cell_id: a unique cell index</li> <li>time_point: the time frame</li> <li>x_blue: x-coordinate of the locus in the blue channel (units in nm)</li> <li>y_blue: y-coordinate of the locus in the blue channel (units in nm)</li> <li>z_blue: z-coordinate of the locus in the blue channel (units in nm)</li> <li>x_green: x-coordinate of the locus in the green channel (units in nm) </li> <li>y_green: y-coordinate of the locus in the green channel (units in nm)</li> <li>z_green: z-coordinate of the locus in the green channel (units in nm) </li> <li>x_Rij: x-component of the aberration corrected 3D distance vector connecting the blue and green loci (units in nm)</li> <li>y_Rij: y-component of the aberration corrected 3D distance vector connecting the blue and green loci (units in nm)</li> <li>z_Rij: z-component of the aberration corrected 3D distance vector connecting the blue and green loci (units in nm)</li> <li>red: intensity in the red channel (a.u.)</li> <li>state: inferred state using a 3-state HMM, with entries 0 (O_off), 1 (P_off), 2 (P_on)</li> </ul>
Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution
<p>Supplementary Tables for article "Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution"<br> </p>
Mapped data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains mapped sequencing data for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>. It contains single-cell RNA-seq (scRNA) and single-cell ATAC-seq (scATAC) data from a time course of human dermal fibroblasts induced with Yamanaka factors OSKM using a Sendai virus based delivery system. The scRNA and scATAC data is performed at days 0, 2, 4, 6, 8, 10, 12, 14 and the final iPSCs. The experiment was re-performed and single-nucleus multiome (ATAC+RNA) was collected on days 1 and 2. </p> <p>The data is as follows:</p> <p><strong>scATAC</strong>: We used Chromap (commit <a href="https://github.com/haowenz/chromap/tree/6e97125b9">https://github.com/haowenz/chromap/tree/6e97125b9</a>, <a href="https://doi.org/10.1038/s41467-021-26865-w">https://doi.org/10.1038/s41467-021-26865-w</a>) to perform barcode correction, alignment and filtering for each of our samples. The corresponding fragment files (tab separated file containing mapped fragments with columns: chr, start, end, barcode, number of reads) and their tabix indices are available for each sample.</p> <p><strong>scRNA</strong>: We used cellranger v6.0.2 for read mapping and quantification to obtain the counts matrix. We used the GRCh38 2020-A reference. For each sample, the raw and filtered counts matrices are provided. E.g. `D0/raw_feature_bc_matrix.h5` contains an HDF5 object containing gene counts for each barcode and associated metadata for the Day 0 sample. Similarly, the files in `D0/raw_feature_bc_matrix/` contain the same gene x barcode matrix, with the counts matrix in Matrix Market format (`matrix.mtx.gz`), and gene (`features.tsv.gz`) and barcode names (`barcodes.tsv.gz`). </p> <p><strong>multiome</strong>: The ATAC and RNA components are separately processed using the same tools as mentioned above for scATAC and scRNA. Outputs are in the `snATAC` and `snRNA` subdirectories respectively. In addition, the `ATAC.RNA.bc.map.tsv` file contains a map to link snATAC barcodes to snRNA barcodes. </p>
ChromBPNet models and data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains ChromBPNet models and data used to train the models for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>.</p> <p>`data` contains bigwigs and regions (peaks + non-peaks) used for training each of the models. See `data/README.txt` for more details.</p> <p><strong>Models:</strong></p> <p><em>Loading the model:</em></p> <p>The models were trained using tf1.14. The models are provided in h5 format for tf1.14 (py3.7) and SavedModel format for tf2.X. tf2.X tested only for py3.8-11, tf2.8-13.</p> <p>To load the models in tf1.14:</p> <pre><code class="language-python">model = tf.keras.models.load_model("path/to/model.h5")</code></pre> <p>In tf2:</p> <pre><code class="language-python">model = tf.keras.models.load_model("path/to/model_dir")</code></pre> <p>If all fails, you can load the architecture as provided in `model_arch.py` with default parameters (`bpnet_seq` for bias model and `chrombpnet` for chrombpnet model), and then load the weights using `model.load_weights` from the weights provided in the `weights` directory.</p> <p> </p> <p><em>Usage:</em></p> <p>The bias models take as input one-hot sequence of length 2000. It has 2 outputs, a vector of logits of length 2000, and 1 logcounts scalar:</p> <pre><code class="language-python"># seq_one_hot of length B x 2000 x 4 out_bias_logits, out_bias_logcounts = bias_model.predict(seq_one_hot) # out_bias_logits: B x 2000 # out_bias_logcounts: B x 1</code></pre> <p>The ChromBPNet model takes as input a one-hot sequence of length 2000, bias logits of length 2000 and bias log-counts scalar. It has the same output types as the bias model. To run the chrombpnet model to obtain predictions:</p> <pre><code class="language-python">pred_profile, pred_logcounts = chrombpnet_model.predict([seq_one_hot, out_bias_logits, out_bias_logcounts]) # pred_profile: B x 2000 # pred_logcounts: B x 1 </code></pre> <p>If you wish to obtain the "de-biased" predictions (see Methods), simply pass in zeros instead of the bias model predictions as:</p> <pre><code class="language-python">pred_profile_debiased, pred_logcounts_debiased = chrombpnet_model.predict([seq_one_hot, np.zeros((seq_one_hot.shape[0], 2000)), np.zeros((seq_one_hot.shape[0], 1))])</code></pre> <p>To obtain predicted per-base predicted counts (with or without bias):</p> <pre><code class="language-python">pred_per_base_counts = scipy.special.softmax(pred_profile, axis=-1) * (np.exp(pred_logcounts)-1) # pred_per_base_counts: B x 2000 </code></pre> <p>Note that in general predicted counts can't be compared across models as they are not corrected for sequencing depth.</p> <p> </p> <p><em>Note:</em></p> <p>All bias models used across folds are identical, except for the final intercept term in the counts output (see Methods), that is specific to each cell state, fold combination.</p> <p> </p> <p><em>Folds:</em></p> <p>The splits used for training the different folds are as below:</p> Fold Test Chromosomes Validation Chromosomes 0 chr1 chr8, chr10 1 chr2, chr19 chr1 2 chr3, chr20 chr2, chr19 3 chr6, chr13, chr22 chr3, chr20 4 chr5, chr16, chrY chr6, chr13, chr22 5 chr4, chr15, chr21 chr5, chr16, chrY 6 chr7, chr18, chr14 chr4, chr15, chr21 7 chr11, chr17, chrX chr7, chr18, chr14 8 chr9, chr12 chr11, chr17, chrX 9 chr8, chr10 chr9, chr12 <p>Remaining chromosomes were used as the training chromosome for each fold.</p>
Data from: Foraging-induced craniofacial plasticity is associated with an early, robust, and dynamic transcriptional response
Open the record for dataset details and reuse information.
Data from: Differences in RAD51 transcriptional response and cell cycle dynamics reveal varying sensitivity to DNA damage among Arabidopsis thaliana root cell types
Open the record for dataset details and reuse information.
Single gene analysis in yeast suggests nonequilibrium regulatory dynamics for transcription
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DNA methylation regulates transcription factor specific neurodevelopmental but not sexually dimorphic gene expression dynamics in zebra finch telencephalon
<p>Supplementary Information for the manuscript: "DNA methylation regulates transcription factor specific neurodevelopmental but not sexually dimorphic gene expression dynamics in zebra finch telencephalon"</p>
Dissecting the in vivo dynamics of transcription locking due to positive supercoiling buildup
<p>Positive supercoiling buildup (PSB) is a pervasive phenomenon in the transcriptional programs of Escherichia coli. After finding a range of Gyrase concentrations where the inverse of the transcription rate of a chromosome-integrated gene changes linearly with the inverse of Gyrase concentration, we apply a Line Weaver-Burk plot to dissect the expected in vivo transcription rate in absence of PSB. We validate the estimation by time-lapse microscopy of single-RNA production kinetics of the same gene when single-copy plasmid-borne, shown to be impervious to Gyrase inhibition. Next, we estimate the fraction of time in locked states and number of transcription events prior to locking, which we validate by measurements under Gyrase inhibition. Replacing the gene of interest by one with slower transcription rate decreases the fraction of time in locked states due to PSB. Finally, we combine data from both constructs to infer a range of possible transcription initiation locking kinetics in a chromosomal location, obtainable by tuning the transcription rate. We validate with measurements of transcription activity at different induction levels. This strategy for dissecting transcription initiation locking kinetics due to PSB can contribute to resolve the transcriptional programs of E. coli and in the engineering of synthetic genetic circuits.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.