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21,320 results for “Transcription”
Apical Localization of RNA Polymerases Modulate Transcription Dynamics and Supercoiling Domains Revealed by Cryo-ET
<p>Supplementary materials include all particle raw tilts, 3D reconstructions, models, FSC evaluations, statistical data analyses, gel images, and oxDNA molecular dynamics simulations for the manuscript titled "<strong>Apical Localization of RNA Polymerases Modulate Transcription Dynamics and Supercoiling Domains Revealed by Cryo-ET</strong>".</p>
Data from: Tryptophan and Cortisol modulate the Kynurenine and Serotonin transcriptional pathway in the kidney of Oncorhynchus kisutch
<p>Aquaculture fish are kept for long-periods in sea cages or tanks. Consequently, accumulated stress causes the fish to present serious problems with critical economic losses. Fish food has been supplemented to reduce the stress, using many compoment as amino acids such as tryptophan. This study aims to determine the transcriptional effect of tryptophan and cortisol on primary cell cultures of salmon head and posterior kidney. Our results indicate activation of the kynurenine pathway and serotonin activity when stimulated with tryptophan and cortisol. 95% of tryptophan is degraded by the kynurenine pathway, indicating the relevance of knowing how this pathway is activated and if stress levels associated with fish culture trigger its activation. Additionally, it is essential to know the consequence of increasing kynurenic acid "KYNA" levels in the short and long term, and even during the fish ontogeny.</p>
Transcriptional and epigenetic profiling of Arabidopsis thaliana exposed to low dose ionizing radiation
<p>RNA and methylation profiling of Arabidopsis seedlings to low dose ionizing radiation</p>
Transcription Factor Co-Expression Mediates Lineage Priming for Embryonic and Extra-Embryonic Differentiation
<p>In early mammalian development, cleavage stage blastomeres and inner cell mass (ICM) cells co-express embryonic and extra-embryonic transcriptional determinants. Using a double protein-based reporter we identify an embryonic stem cell (ESC)population that co-expresses the extra-embryonic factor GATA6 alongside the embryonic factor SOX2. Based on single cell transcriptomics, we find this population resembles the unsegregated ICM, exhibiting enhanced differentiation potential for endoderm while maintaining epiblast competence. To relate transcription factor binding in these to future fate, we describe a complete enhancer set in both ESCs and naïve extra-embryonic endoderm stem cells and assess SOX2 and GATA6 binding at these elements in the ICM-like ESC sub-population. Both factors support cooperative recognition in these lineages, with GATA6 bound alongside SOX2 on a fraction of pluripotency enhancers and SOX2 alongside GATA6 more extensively on endoderm enhancers, suggesting that cooperative binding between these antagonistic factors both supports self-renewal and prepares progenitor cells for later differentiation.</p>
Demo datasets for the protocol to identify shared transcriptional risks between diseases and compounds predicted to result in mutual benefit
<p>We present a computational protocol (https://github.com/ghbore/protocol-cancer-cvd-similarity), implemented as a Snakemake workflow, that was used in previous works (Gao et al., 2022; Baylis et al., 2023). This protocol allows researchers to identify shared transcriptional processes that drive disease and to screen existing compounds for mutual benefit. The protocol also includes a description of the pharmacovigilance study design used to validate the effect of novel compounds using electronic health records, where applicable. This repository bundles the datasets used in previous works as an example to run through the Snakemake workflow. These datasets include the TCGA cancer dataset, the STARNET and BiKE CVD datasets, and other dependent resources.</p>
PP2A complex disruptor SET prompts widespread hyper-transcription of growth-essential genes in the pancreatic cancer cells
<p>Hyper-activation of the oncogenic transcription reflects the epigenetic plasticity of the cancer cells. SET was described as a nuclear factor that stimulated transcription from the chromatin template. However, the mechanisms of SET-dependent transcription are unknown. Here, we found that over-expression of SET and CDK9 induced very similar transcriptome signatures in multiple cancer cell lines. SET localized in the transcription start site (TSS)-proximal regions and supported the RNA transcription. SET specifically bound the PP2A-C subunit and induced PP2A-A subunit repulsion from the C subunit, which indicated the role of SET as a PP2A-A/C complex disruptor in the TSS-proximal regions. Through blocking PP2A activity, SET assisted CDK9 to maintain Pol II CTD phosphorylation and activated mRNA transcription. Our findings position SET as a key factor that modulates chromatin PP2A activity, promoting the oncogenic transcription in pancreatic cancer.</p>
Differential gene expression table of the transcriptional response of Colorado potato beetle to aegerolysin-based complex
<p>The file in Excel format (xlsx) contains the results of differential gene expression analysis of a study with Colorado potato beetle larvae that explores the potential adaptive response of CPB larvae to feeding with feed complemented by the aegerolysin-based complex PlyA2/PlyB.</p> <p>Experimental design: Leptinotarsa decemlineata larvae fed on potato leaf disks supplemented with PlyA2/PlyB/buffer solution vs control larvae fed on leaves soaked in buffer. Whole larvae were sampled at 1 and 5 days post-treatment, and RNA was isolated and sequenced on an Illumina sequencing platform.</p>
Data from: Immune Transcriptional Response in Head Kidney Primary Cell Cultures Isolated from the Three Most Important Species in Chilean Salmonids Aquaculture.
<p>Data from Immune Transcriptional Response in Head Kidney Primary Cell Cultures Isolated from the Three Most Important Species in Chilean Salmonids Aquaculture. Files in .JBN and .xlsx format</p>
Human keratinocytes transcriptional response to IL1B stimulation.
Open the record for dataset details and reuse information.
Supplemental Material - An IL-6 promoter variant (-174 G/C) augments IL-6 production and alters skeletal muscle transcription in response to exercise in mice.
<p>Supplemental Material for article: "An IL-6 promoter variant (-174 G/C) augments IL-6 production and alters skeletal muscle transcription in response to exercise in mice."</p>
Supplementary data for 'An extreme mutational hotspot in nlpD depends on transcriptional induction of rpoS'
<p>This zip file contains raw data and calculations for each figure of the manuscript "An extreme mutational hotspot in <em>nlpD </em>depends on transcriptional induction of <em>rpoS</em>". Please refer to the README tab of each excel file, or the README txt files in each folder, for a description of each set of data. </p>
Atlas of nascent RNA transcripts reveals enhancer to gene linkages
<p>Data associated with the paper "Atlas of nascent RNA transcripts reveals enhancer to gene linkages"</p> <p>GitHub repository for the analyses: <a href="https://github.com/Dowell-Lab/DBNascent_Analysis">https://github.com/Dowell-Lab/DBNascent_Analysis</a></p> <p>Below are the summaries of the files associated with this publication. </p> <p> </p> <p>1. muMerge calls for each paper used in the merging</p> <p><strong>paper_mumerge_calls</strong></p> <p>- The calls are separated by the bidirectional caller (dreg, tfit)</p> <p>- In the folders are bed files (e.g. <em>Allen2014global_hg38_dreg_MUMERGE.bed</em>) for each paper and species (hg38, mm10)</p> <p> </p> <p>2. Base content for regions called by dREG and Tfit in each paper in mouse and human</p> <p><strong>mumerge_base_composition</strong></p> <p>- The base content for each paper after the first round of muMerge</p> <p>- The files contain the id and the base nucleotide content in 300bp around the center region (id, cg, at)</p> <p> </p> <p>3. Bidirectional regions called by Tfit and dREG after merging. Regions are for mouse and human datasets. (See <a href="https://github.com/Dowell-Lab/bidirectionals_merged">https://github.com/Dowell-Lab/bidirectionals_merged</a>)</p> <p><strong>bidirectional_regions</strong></p> <p>- Bidirectional regions called after muMerge and filtering</p> <p>- Calls for both human and mouse datasets are reported (<em>hg38_tfit_dreg_bidirectionals.bed.gz</em> and <em>mm10_tfit_dreg_bidirectionals.bed.gz</em>)</p> <p>- The bed files are in bed6 format with the following columns:</p> <p>chromosome, start, stop, bidirectional, number of papers a bidirectional was called, strand (it is . since bidirectionals are not stranded)</p> <p> </p> <p>4. Metadata for samples used in the SPECS and correlation analysis</p> <p><strong>metadata</strong></p> <p><span> - Sample metadata for filtered samples (human_samples_QC_GC_protocol_filtered.tsv.gz) in the downstream analyses </span></p> <p> </p> <p>5. SPECS scores across genes and bidirectional regions</p> <p><strong>specs_scores</strong></p> <p><span> - The SPECS scores for all tissues analyzed (filt_qc123_all_specs_all.txt.gz) are reported,</span></p> <p><span> - Along with the maximum (filt_qc123_all_specs_maxval.txt.gz) </span></p> <p><span> - And minimum SPECS scores (filt_qc123_all_specs_minval.txt.gz).</span></p> <p><span> - The SPECS scores were also split by disease vs non-disease samples</span></p> <p><span> - The TPMs summaries are also included</span></p> <p> </p> <p><span>6. Normalized counts </span></p> <p><strong><span>normalized_counts</span></strong></p> <p><span> - Gene and bidirectional region normalized counts (gene_bidir_tpm.tsv.gz)</span></p> <p> </p> <p>7. Bidirectional Region and gene pairs (See https://github.com/Dowell-Lab/bidir_gene_pairs)</p> <p><strong>bidirectional_gene_pairs</strong></p> <p>- Gene and bidirectional region pairs (<em>dbnascent_pairs.txt.gz</em>) across tissues in high-quality samples. </p> <p>- The pairs are reported in a bed12 file</p> <p> - Where the first 6 columns are gene coordinates and the following 6 are bidirectional coordinates.</p> <p> - The remaining columns are the summary statistics for correlation and the relationship between the gene and bidirectional.</p> <p><span> - Additional columns note whether the pair overlaps eQTLs from GTEx (eQTL) or polII ChIA-PET loops</span></p> <ul> <li>transcript1_chrom : Gene chromosome</li> <li>transcript1_start : Gene start coordinate</li> <li>transcript1_stop : Gene stop coordinate</li> <li>transcript_1 : Gene id</li> <li>transcript1_score : Gene score (. since none was assigned)</li> <li>transcript1_strand : Gene strand</li> <li>transcript2_chrom : Bidirectional chromosome</li> <li>transcript2_start : Bidirectional start coordinate</li> <li>transcript2_stop : Bidirectional stol coordinate</li> <li>transcript_2 : Bidirectional id</li> <li>transcript2_score : Bidirectional score (i.e. the number of papers that support a bidirectional from muMerge)</li> <li>transcript2_strand : Bidirectional strand (. since these are not stranded)</li> <li>pcc : Pearsons correlation coefficient</li> <li>pval : P-value</li> <li>adj_p_BH : Adjusted p-value (Benjamini-Hochberg correction)</li> <li>nObs : Number of observations in correlation analysis</li> <li>t : T statistic</li> <li>distance_tss : Distance between the gene start (TSS) and the bidirectional start coordinate</li> <li>distance_tes : Distance between the gene stop (TES) and the bidirectional start coordinate</li> <li>position : Is the bidirectional upstream or downstream of the TSS</li> <li>tissue : Tissue id based on metadata for tissue-derived correlations (labeled All_samples if all samples are used)</li> <li>percent_transcribed_both : Percent of the number of observed samples used in the analysis</li> <li><span>pair_id : Gene:Transcript~Bidirectional pair name</span></li> <li><span>gene_id : Gene id</span></li> <li><span>chiapet : Binary indicator for whether pair overlaps overlap polII ChIA-PET </span></li> <li><span>gtex : Bindary Indicator whether a pair is overlapping GTEx pairs</span></li> </ul> <p> </p>
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>
Upfront whole blood transcriptional patterns in patients receiving immune checkpoint inhibitors associate with clinical outcome
<p>This repository contains supplementary materials for the research paper "Upfront whole blood transcriptional patterns in patients receiving immune checkpoint inhibitors associate with clinical outcome". The materials are organized in the following folders:</p> <ul> <li>01_code: code to reproduce the results</li> <li>02_raw_data_public: raw gene count data of the 14,085 whole blood transcriptomes from public datasets (PUBLIC)</li> <li>03_raw_data_primero: raw gene count data of the 145 pre-ICI whole blood samples from the PRIMERO cohort, including sample annotation with response data ("primero_sample_anno.tsv")</li> <li>04_ica: results of the independent component analysis on the PUBLIC dataset, including the transcriptional components ("ica_public_flipped_consensus_independent_components.tsv"), the gene set enrichment analysis results on these components, and the projected activities of the PRIMERO samples for these transcriptional components ("ica_primero_mixing_matrix_projected_corrected.tsv")<br><br></li> </ul>
Transcriptional analysis of peripheral memory T cells reveals Parkinson's disease-specific gene signatures--Fluorospot SFC
<p>Fluorospot SFC (combined IFNg, IL-5, and IL-10) data and corresponding HC-PD group IDs described in "Transcriptional analysis of peripheral memory T cells reveals Parkinson’s disease-specific gene signatures"</p>
Transcriptional acclimation and spatial differentiation characterize drought response by the ectomycorrhizal fungus Suillus pungens
<ul> <li>Changing precipitation regimes are a challenge for forest health under future climate scenarios. If belowground symbionts can acclimate to changing moisture regimes it may buffer forest trees from these changes.</li> <li>In this study we exposed the ectomycorrhizal fungus <i>Suillus pungens</i> to acute and chronic drought stress and used RNASeq of both ectomycorrhizal roots and extraradical mycelium to gauge the magnitude of stress, identify key genes involved in drought response, and gauge potential ecosystem consequences of drought.</li> <li>We found that there were major transcriptional differences for <i>S. pungens</i> in ectomycorrhizal roots (28% of genes) and extraradical mycelium (41% of genes) under acute drought stress, but only 0.1-2% of genes were differentially expressed in chronic drought treatments. Up to 56% of differentially expressed genes under acute drought were unique to either roots or mycelium. While a number of implicated genes, such as those encoding for trehalose, have well-known roles in osmotic stress, others, such as fungal hydrophobins and atromentin, have received less study and may also impact other ecosystem functions.</li> <li>These results suggest that functional compartmentalization is key to ectomycorrhizal fungal adaptation to stressful climatic conditions and there is high potential for fungal acclimation to ameliorate future climate stress. </li> </ul>
Transcriptional kinetics and molecular functions of long non-coding RNAs
<p>R code, R markdowns and data (count tables) for the analysis described in 'Transcriptional kinetics and molecular functions of long non-coding RNAs'. Code and data is also available at https://github.com/sandberg-lab/lncRNAs_bursting.</p>
Supplementary File to "Both binding strength and evolutionary accessibility affect the population frequency of transcription factor binding sequences in Arabidopsis thaliana" (Genome Biology and Evolution)
<p>This data is supplementary file 1 of the following publication:</p> <p>Schweizer G, Wagner A. "Both binding strength and evolutionary accessibility affect the population frequency of transcription factor binding sequences in Arabidopsis thaliana" (Genome Biology and Evolution)</p>
Transcript of Pelliot chinoise 2504 Dunhuang Table of Offices
<p>Transcript of Pelliot chinoise 2504 Dunhuang Table of Offices 唐職官表. Based on YAMAMOTO Tatsuro, IKEDA On, and OKANO Makoto (eds.), <em>Tun-huang and Turfan Documents Concerning Social and Economic History, Vol. I: Legal Texts (A) Introduction & Texts</em> (Tokyo: The Toyo Bunko, 1980), 45(84)–48(81), and ‘Tableau synoptique à l'usage des fonctionnaires, datant de l'époque de Xuan zong 玄宗 (712–756) des Tang’, Pelliot chinoise 2504, <em>Bibliothèque nationale de France</em> (https://gallica.bnf.fr/ark:/12148/btv1b8302008r).</p>
The underground life of homeodomain-leucine zipper transcription factors
<p class="western"><span>Roots are the anchorage organs of plants, responsible for water and nutrient uptake, exhibiting high plasticity. Root architecture is driven by the interactions of biomolecules, including transcription factors (TFs) and hormones that are crucial players regulating root plasticity. Multiple TF families are involved in root development; some, such as ARFs and LBDs, have been well characterized, whereas others remain less investigated. In this review, we synthesize the current knowledge about the involvement of the large family of homeodomain-leucine zipper (HD-Zip) TFs in root development. This family is divided into four subfamilies (I to IV), mainly according to structural features, such as additional motifs aside from HD-Zip, as well as their size, gene structure, and expression patterns. We explored and analyzed public databases and the scientific literature regarding HD-Zip TFs in Arabidopsis and other species. Most members of the four HD-Zip subfamilies are expressed in specific cell types and several ones from each group have assigned functions in root development. Notably, a high proportion of the studied proteins are part of intricate regulation pathways involved in primary and lateral root growth and development.</span></p>
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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.