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3,291 results for “transcription regulation”
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>
Trihelix transcription factor SlGT31 regulates fruit ripening mediated by ethylene in tomato
<p class="MsoNormal"><span>Trihelix proteins are plant-specific transcription factors that play crucial roles in plant development and stress responses. However, the involvement of trihelix proteins in fruit ripening and transcriptional regulatory mechanisms remains largely unclear. In this study, we cloned a trihelix <span>gene<em> SlGT31</em>, whose relative expression was significantly induced by the application of exogenous ethylene but repressed by 1-methylcyclopropene (1-MCP). Suppression of <em>SlGT31</em> resulted in delayed fruit ripening, decreased accumulation of total carotenoids and ethylene content, and inhibition of relative expression of genes related to ethylene and fruit ripening. Conversely, the opposite results were observed in <em>SlGT31</em>-overexpression lines. Yeast one-hybrid and dual-luciferase assays suggested that SlGT31 could bind to the promoters of two key ethylene biosynthesis genes <em>ACO1 </em>and <em>ACS4.</em> These results indicate that SlGT31 may act as a positive modulator during fruit ripening.</span></span></p>
Zip archive containing datasets described in the manuscript entitled "Distribution-agnostic Deep Learning Enables Accurate Single‐Cell Data Recovery and Transcriptional Regulation Interpretation"
<p>The datasets used in the manuscript entitled "Distribution-agnostic Deep Learning Enables Accurate Single‐Cell Data Recovery and Transcriptional Regulation Interpretation". These datasets encompass all the experiments conducted in the manuscript, including simulation experiments, downsampling experiments, clustering, differential expression analysis, enrichment analysis, trajectory inference, batch correction, and clinical case discovery.</p> <p>The open-source software is available at https://github.com/XuYuanchi/Bis.</p>
Differentiation trajectories of the Hydra nervous system reveal transcriptional regulators of neuronal fate
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Data from: Mechanisms of transcriptional regulation in Anopheles gambiae revealed by allele specific expression
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Data from: Decoupling transcription factor expression and activity enables dimmer switch gene regulation
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Sox2 interacts with Atoh1 and Huwe1 loci to regulate Atoh1 transcription and stability during hair cell differentiation
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Data from: MFSD12, transcriptionally regulated by PLAGL2, promotes bladder cancer progression
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Data from: Transcriptional profiling of lung macrophages following ozone exposure in mice identifies signaling pathways regulating immunometabolic activation
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Data from: Protein kinase FaSnRK2.6 phosphorylates transcription factor FabHLH3 to regulate anthocyanin homeostasis during strawberry fruit ripening
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Transcriptome-wide alternative mRNA splicing analysis reveals post-transcriptional regulation of neuronal differentiation
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Data from: Transcriptional regulation of human <em>NMNAT2</em>: Insights from 3D genome sequencing and bioinformatics
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Trihelix transcription factor SlGT31 regulates fruit ripening mediated by ethylene in tomato
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Transient intracellular acidification regulates the core transcriptional heat shock response
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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>
HAND2 Targets Define a Network of Transcriptional Regulators that Compartmentalize the Early Limb Bud Mesenchyme
<p>Highlights</p> <p>•</p> <p>ChIP-seq identifies the CRMs bound by endogenous HAND2 in embryos and limb buds</p> <p>•</p> <p>HAND2 controls key transcriptional regulators acting upstream of SHH in limb buds</p> <p>•</p> <p>These transcriptional circuits define proximal, anterior, and posterior identities</p> <p>•</p> <p>HAND2 establishes anterior and posterior compartments by regulating <em>Gli3</em> and <em>Tbx3</em></p>
Data from: Quantitative proteomics reveals key roles for post-transcriptional gene regulation in the molecular pathology of FSHD
DUX4 is a transcription factor whose misexpression in skeletal muscle causes facioscapulohumeral muscular dystrophy (FSHD). While DUX4's transcriptional activity has been extensively characterized, the DUX4-induced proteome remains undescribed. Here, we report concurrent measurement of RNA and protein levels in DUX4-expressing cells via RNA-seq and quantitative mass spectrometry. DUX4 transcriptional targets were robustly translated, confirming the likely clinical relevance of proposed FSHD biomarkers. However, a multitude of mRNAs and proteins exhibited discordant expression changes upon DUX4 expression. Our dataset revealed unexpected proteomic, but not transcriptomic, dysregulation of diverse molecular pathways, including Golgi apparatus fragmentation, as well as extensive post-transcriptional buffering of stress response genes. Key components of RNA degradation machineries, including UPF1, UPF3B, and XRN1, exhibited suppressed protein, but not mRNA, levels, explaining the build-up of aberrant RNAs that characterizes DUX4-expressing cells. Our results provide a resource for the FSHD community and illustrate the importance of post-transcriptional processes to DUX4-induced pathology.
Data from: Limits on information transduction through amplitude and frequency regulation of transcription factor activity
Signaling pathways often transmit multiple signals through a single shared transcription factor (TF) and encode signal information by differentially regulating TF dynamics. However, signal information will be lost unless it can be reliably decoded by downstream genes. To understand the limits on dynamic information transduction, we apply information theory to quantify how much gene expression information the yeast TF Msn2 can transduce to target genes in the amplitude or frequency of its activation dynamics. We find that although the amount of information transmitted by Msn2 to single target genes is limited, information transduction can be increased by modulating promoter cis-elements or by integrating information from multiple genes. By correcting for extrinsic noise, we estimate an upper bound on information transduction. Overall, we find that information transduction through amplitude and frequency regulation of Msn2 is limited to error-free transduction of signal identity, but not signal intensity information.
CRISPuRe-seq: pooled screening of barcoded ribonucleoprotein reporters reveals regulation of RNA polymerase III transcription by the Integrated Stress Response via mTOR
<p>NAR_Data_Package.zip contains the data required for generating figures, including original gel images for western blots and a step-by-step protocol for CRISPuRe-seq.</p> <p>SupplementaryData.xlsx contains all of the Supplemental Data directly referred to in the manuscript.</p> <p>Plasmid maps.zip contains plasmid maps for constructs used in the manuscript in genbank format.</p> <p>Scripts.zip contains the scripts used for screen processing with a text file explaining their usage.</p> <p>Look_Up_Table.zip contains the look-up-table for decoding barcode and sgRNA pairings.</p>
ScienceDex guides
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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.