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21,320 results for “Transcription”
Aminoacyl-tRNA synthetase gene alignments from multiple Sileneae species generated from full-length transcripts using Iso-Seq and raw microscopy image files
<p>Trimmed and untrimmed alignments for the final aminoacyl-tRNA synthetases in <em>Sileneae </em>species and <em>Arabidopsis thaliana. W</em>e investigated the evolution of subcellular localization of aaRS enzymes in five different species from the plant lineage <em>Sileneae</em> that has experienced extensive and rapid mitochondrial tRNA loss. By analyzing full-length mRNA transcripts with single-molecule sequencing technology (PacBio Iso-Seq) and searching genome sequences, we found instances of predicted retargeting of an ancestrally cytosolic aaRS to the mitochondrion as well as scenarios where enzyme localization does not appear to change despite functional tRNA replacement.</p> <p>Nikon .nd2 raw microscopy files for the transient expression and imaging of predicted transit peptides and colocalization assays in <em>N. benthamiana</em> epithelial cells. The amino acid sequence plus 10 upstream amino acids of the protein body were fused to GFP and co-transfected with an eqFP611-tagged transit peptide from a known mitochondrially localized protein (isovaleryl-CoA dehydrogenase).</p>
Raw single-molecule imaging data for "Tuning levels of low-complexity domain interactions to modulate endogenous oncogenic transcription"
<p><strong>Raw single-molecule imaging data for "Tuning levels of low-complexity domain interactions to modulate endogenous oncogenic transcription"</strong></p> <p>Shasha Chong<sup>1</sup>, Thomas G.W. Graham<sup>2</sup>, Claire Dugast-Darzacq<sup>2,5</sup>, Gina M. Dailey<sup>2</sup>, Xavier Darzacq<sup>2,5</sup>, Robert Tjian<sup>2,3,4,5</sup>*</p> <p><sup>1 </sup>Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA</p> <p><sup>2 </sup>Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.</p> <p><sup>3 </sup>Howard Hughes Medical Institute, University of California, Berkeley, CA, USA.</p> <p><sup>4</sup><sup> </sup>Li Ka Shing Center for Biomedical & Health Sciences, University of California, Berkeley, CA, USA.</p> <p><sup>5</sup><sup> </sup>CIRM Center of Excellence, University of California, Berkeley, CA. </p> <p>* Lead contact</p> <p><strong>Overview</strong></p> <p>This repository contains 1) movies of endogenously expressed EWS::FLI1-Halo in genome-edited A673 cells acquired using stroboscopic photo-activatable single particle tracking (spaSPT) and 2) images of exogenously expressed mNeonGreen-EWS-NPM1 fusion protein in the above cells before and after spaSPT movies were acquired. The uploaded files include data acquired from 80 live cells on 4 different days. The imaging data, after being processed, were used to generate Figure 4C-E of the manuscript in the title. </p> <p><strong>Method details</strong></p> <p>The genome-edited A673 cells (described in https://www.science.org/doi/10.1126/science.aar2555) with inducible expression of mNeonGreen-EWS-NPM1 were grown on 25 mm circular No. 1.5 cover glasses (Azer Scientific, 200251) that were plasma-cleaned prior to use. We induced the cells with 200 ng/ml of doxycycline for 96 hours, stained the cells with 20 nM PA-JF646 and 200 nM JFX549 HaloTag ligands, and performed single-molecule imaging of EWS::FLI1-Halo on a custom-built Nikon (Nikon Instruments Inc.) TI microscope described in (https://elifesciences.org/articles/25776). We took images with a 100x/NA 1.49 oil-immersion TIRF objective (Nikon apochromat CFI Apo TIRF 100x Oil) under highly inclined and laminated optical sheet (HILO) illumination (https://www.nature.com/articles/nmeth1171) using following laser lines: 488 nm for mNG; 561 nm for JFX549; 405 nm and 633 nm for photo-activation and excitation of PA-JF646, respectively. The incubation chamber maintained a humidified 37°C atmosphere with 5% CO<sub>2</sub> and the objective was similarly heated to 37°C for live-cell experiments. </p> <p>High-concentration JFX549 staining allows visualization of the intracellular distribution of EWS::FLI1-Halo. We chose cells with EWS::FLI1-Halo enriched in the nucleolus to perform spaSPT. The procedure of spaSPT largely follows what is described in (https://elifesciences.org/articles/25776). Both the excitation laser (633 nm) and the photo-activation laser (405 nm) for PA-JF646 were pulsed. Each frame consisted of a 7-ms camera exposure time followed by a ~500 μs camera ‘dead’ time. The excitation laser (633 nm) was pulsed for 1 ms starting at the beginning for the 7 ms camera exposure time. The photo-activation laser (405 nm) was pulsed during the ~500 μs camera ‘dead’ time, minimizing fluorescence background. Each cell was imaged for 20,000 frames corresponding to ~1.5 min. Images of mNeonGreen-EWS-NPM1 were collected with a camera exposure time of 500 ms before and after the acquisition of each spaSPT movie.</p>
Raw confocal imaging and FRAP data for "Tuning levels of low-complexity domain interactions to modulate endogenous oncogenic transcription"
<p><strong>Raw confocal imaging and FRAP data of "Tuning levels of low-complexity domain interactions to modulate endogenous oncogenic transcription"</strong></p> <p>Shasha Chong<sup>1</sup>, Thomas G.W. Graham<sup>2</sup>, Claire Dugast-Darzacq<sup>2,5</sup>, Gina M. Dailey<sup>2</sup>, Xavier Darzacq<sup>2,5</sup>, Robert Tjian<sup>2,3,4,5</sup>*</p> <p><sup>1 </sup>Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA</p> <p><sup>2 </sup>Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.</p> <p><sup>3 </sup>Howard Hughes Medical Institute, University of California, Berkeley, CA, USA.</p> <p><sup>4</sup><sup> </sup>Li Ka Shing Center for Biomedical & Health Sciences, University of California, Berkeley, CA, USA.</p> <p><sup>5</sup><sup> </sup>CIRM Center of Excellence, University of California, Berkeley, CA. </p> <p>* Lead contact</p> <p><strong>Overview</strong></p> <p>This repository contains 1) raw three-color confocal fluorescence images of a transiently expressed protein (mNeonGreen-EWS, mNeonGreen, EGFP-TAF15, EGFP, mNeonGreen-EWS-NPM1, or mNeonGreen-NPM1), endogenously expressed EWS::FLI1-Halo labeled with JFX549 Halo ligand, and intron RNA fluorescence in situ hybridization (FISH) targeting <em>ABHD6</em>, <em>CAV1</em>, or<em> GAPDH </em>in genome-edited A673 cells, 2) raw fluorescence recovery after photobleaching (FRAP) movies of endogenously expressed EWS::FLI1-Halo labeled with TMR Halo ligand in genome-edited A673 cells in the presence and absence of transient expression of mNeonGreen-EWS-NPM1. The imaging data, after being processed, were used to generate Figure 1D-G (also S1A, S3, and S4), 2E-G (also S5A and S7), 3C-E (also S9), 4A, S2, S6, and S8 of the manuscript in the title. </p> <p><strong>Method details</strong></p> <p>1. RNA fluorescence in situ hybridization (FISH)</p> <p>The genome-edited A673 cells (described in https://www.science.org/doi/10.1126/science.aar2555) were plated on 18 mm circular No. 1 cover glasses (VWR VistaVision, 16004-300) and transfected with a protein expression plasmid using Lipofectamine 3000. 24 hours after transfection, we stained the cells with 200 nM JFX549 HaloTag ligand following the protocol described above, fixed the cells, and then proceeded with RNA FISH. To measure nascent transcription levels of <em>ABHD6</em>, <em>CAV1</em>, and <em>GAPDH </em>genes, we performed intron RNA FISH following the published Stellaris RNA FISH protocol for adherent cells (https://biosearchassets.blob.core.windows.net/assets/bti_stellaris_protocol_adherent_cell.pdf) using Quasar 670-labeled FISH probes designed with the online software Stellaris Probe Designer (https://www.biosearchtech.com/support/tools/design-software/stellaris-probe-designer) and purchased from LGC Biosearch Technologies. </p> <p>2. Confocal fluorescence imaging of protein and nucleic acid distribution</p> <p>Two confocal microscopes were used to image intron RNA FISH samples. One is an inverted laser scanning confocal microscope (Zeiss, LSM 710 AxioObserver) equipped with 34-channel spectral detection, a motorized stage, a full incubation chamber maintaining 37°C and 5% CO<sub>2</sub>, a heated stage, an X-Cite 120 illumination source as well as several laser lines (405, 458, 488, 514, 561, 591, 633 nm). Images were acquired with a 40x Plan NeoFluar NA1.3 oil-immersion objective under control of the Zeiss Zen software. The other is an inverted laser scanning confocal microscope with Airyscan super-resolution capability (Zeiss, LSM 900 with Airyscan 2) and equipped with four laser lines (405, 488, 561, 640 nm). Images were acquired with a 40x oil objective (Zeiss Plan-Apochromat 40x/1.3 Oil DIC) in the confocal (CO) mode under control of the Zen software. We acquired z stacks of RNA FISH samples with a slice interval of 0.3 mm. 405 nm, 488 nm, 561 nm, and 633 or 640 nm lasers were used to excite the fluorescence of Hoechst-labeled nuclei, EGFP or mNeonGreen-labeled proteins, JFX549-labeled EWS::FLI1-Halo, and Quasar 670-labeled intron RNA FISH, respectively. Before acquiring any fluorescence image, we carefully set the laser intensity and microscope detectors to make sure that no pixel in the image was saturated. We used proper emission filters for sequential four-color imaging and ensured no bleed-through between the four channels by imaging cell samples that contain only one of the four fluorophores (Hoechst, EGFP or mNeonGreen, JFX549, and Quasar 670) under the four-color imaging settings.</p> <p>3. Fluorescence recovery after photobleaching (FRAP)</p> <p>FRAP was performed on the inverted laser scanning confocal microscope (Zeiss, LSM 710 AxioObserver) described above. The 561 nm laser and the epi-illumination mode were used for FRAP measurements. Images were acquired with a 40x Plan NeoFluar NA1.3 oil-immersion objective. The knock-in A673 cells were grown on glass-bottom (No. 1.5, 14 mm diameter) 35 mm dishes (MatTek, P35G-1.5-14-C). To measure the FRAP dynamics of EWS::FLI1-Halo in the nucleolus, we transfected the knock-in cells with a plasmid encoding mNG-EWS-NPM1 and stained the cells with 500 nM HaloTag TMR ligand (Promega, G8251) following the protocol described above. We acquired 1000 frames at one frame per 0.3 seconds with the first 5 frames acquired before the bleach pulse for the measurement of baseline fluorescence of the bleach spot and the whole nucleus. We chose to photobleach a circular spot with a radius of 1 μm within a nucleolus using the 561 nm laser at maximum intensity. To measure the FRAP dynamics of EWS::FLI1-Halo in the nucleoplasm, we followed the same procedure as above, except that the knock-in cells were not transfected and a circular bleach spot with a radius of 1 μm was chosen within the nucleoplasm of a cell and at least 1 μm from nuclear and nucleolar boundaries. </p>
Differential analysis of gene regulation at transcript resolution by RNA-Seqcount table
<p>Expression profiling by high throughput sequencing</p>
Dataset and tools for the PSST Challenge on Post-Stroke Speech Transcription
<p>Initial Release, for archival/DOI purposes.</p>
What do we mean by "data" in the arts and humanities? Interview transcripts (University of Bologna, FICLIT) and qualitative data coding
<p>This dataset contains the anonymised transcripts of the interviews conducted between November and December 2021 at the department of Classical Philology and Italian Studies (FICLIT) at the University of Bologna. It further includes the qualitative data analysis of the interviews, carried out using a grounded theory approach and the open source software QualCoder version 2.9.</p>
Hijacking of transcriptional condensates by endogenous retroviruses
<p>This dataset provides companion data to the paper "Hijacking of transcriptional condensates by endogenous retroviruses", Nature Genetics, 2022 (NG-A58214). It includes raw data, images and computational code to generate the figures in the study.</p>
Epigenetic variation impacts ancestry-associated differences in the transcriptional response to influenza infection (preprint)
<p>Processed inputs necessary to run the main analyses described in https://github.com/katiearacena/EU_AF_ancestry_flu_code</p>
On taming the effect of transcript level intra-condition count variation during differential expression analysis: a story of dogs, foxes and wolves: Bowtie2 counts and kallisto abundances
<p>Intra [1] and inter [2-5] study RNA-seq read datasets representing the varying brain compartments of foxes (n=24), as well as dogs (n=14) and wolves (n=6), as described in Lobo <em>et al.</em>, (2022) (under review), were mapped to the dog reference transcriptome [6], which contained 26,107 annotated transcripts (Ensembl CanFam3.1, release 92) [7], using Bowtie2 v.2.3.4.1 [8] and using kallisto v0.46.1 [9]. Count data obtained following each mapping approach for each dataset had high correlations (Lobo <em>et al.</em>, Figure S2). Bowtie2 counts were subsequently used in multiple differential analysis experiments in order to explore the effects of intra-condition count variation on the detection of differentially expressed transcripts. The individual count and abundance datasets for each corresponding RNA-seq dataset are available here.</p> <p> </p> <p>A preprint of Lobo et al., 2022, currently under review for PLOS ONE, is available [10]. The preprint however does not contain reviewer requested information on simulations as this, along with other additions including an additional author RL, has been subsequently added during the review process. These additions will be made available following review via a link to the final paper. </p> <p> </p> <p>Related software to this project are:<br> 1. <a href="http://sourceforge.net/projects/cstone/">CStone</a> <br> 2. <a href="http://sourceforge.net/projects/csreadgen/">CSReadGen</a><br> 3. <a href="https://sourceforge.net/projects/cview/">CView</a> <br> 4. <a href="https://sourceforge.net/projects/chimsim/">ChimSim</a><br> 5. <a href="https://sourceforge.net/projects/tvscript/">TVScript</a> <</p> <p> </p> <p>General details of the projects involved are available: <a href="https://cibio.up.pt/en/projects/is-hybridization-between-wolves-and-dogs-shaping-the-evolutionary-trajectory-of-wolf-populations-in-human-dominated-landscapes/">dog-wolf</a> and <a href="https://cibio.up.pt/en/projects/de-novo-based-sequence-assembly-of-next-generation-sequence-data-without-chimeras-improved-annotation-gene-expression-profiles-and-haplotype-br-reconstruction/">chimerism</a>.</p> <p> </p> <p><strong>References</strong></p> <p>1. Wang X, Pipes L, Trut L, Herbeck Y, Vladimirova A, Gulevich R, et al. Genomic responses to selection for tame/aggressive behaviors in the silver fox (Vulpes vulpes). Proc Natl Acad Sci. 2018;115: 10398–10403. doi:10.1073/pnas.1800889115</p> <p> </p> <p>2. Roy M, Kim N, Kim K, Chung WH, Achawanantakun R, Sun Y, et al. Analysis of the canine brain transcriptome with an emphasis on the hypothalamus and cerebral cortex. Mamm Genome. 2013;24: 484–499. doi:10.1007/s00335-013-9480-0</p> <p> </p> <p>3. Fushan AA, Turanov AA, Lee SG, Kim EB, Lobanov A V, Yim SH, et al. Gene expression defines natural changes in mammalian lifespan. Aging Cell. 2015;14: 352–365. doi:10.1111/acel.12283</p> <p> </p> <p>4. Hoeppner MP, Lundquist A, Pirun M, Meadows JRS, Zamani N, Johnson J, et al. An improved canine genome and a comprehensive catalogue of coding genes and non-coding transcripts. PLoS One. 2014;9(3):91172. doi:10.1371/journal.pone.0091172</p> <p> </p> <p>5. Albert FW, Somel M, Carneiro M, Aximu-Petri A, Halbwax M, Thalmann O, et al. A Comparison of Brain Gene Expression Levels in Domesticated and Wild Animals. Akey JM, editor. PLoS Genet. 2012;8:e1002962. doi:10.1371/journal.pgen.1002962</p> <p> </p> <p>6. Hoeppner MP, Lundquist A, Pirun M, Meadows JRS, Zamani N, Johnson J, et al. An improved canine genome and a comprehensive catalogue of coding genes and non-coding transcripts. PLoS One. 2014;9(3):91172. doi:10.1371/journal.pone.0091172</p> <p> </p> <p>7. Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids Res. 2020;48: D682–D688. doi:10.1093/NAR/GKZ966</p> <p> </p> <p>8. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012. doi:10.1038/nmeth.1923</p> <p> </p> <p>9. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 2016 345. 2016;34: 525–527. doi:10.1038/nbt.3519</p> <p> </p> <p>10. Lobo D, Godinho R, Archer JP. On taming the effect of transcript level intra-condition count variation during differential expression analysis: a story of dogs, foxes and wolves. bioRxiv. 2022; 2022.01.24.477470. doi:10.1101/2022.01.24.477470</p>
Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies
<p>Pathogenic variants in genes that cause dilated cardiomyopathy (DCM) and arrhythmogenic cardiomyopathy</p> <p>(ACM) convey high risks for the development of heart failure through unknown mechanisms. Using</p> <p>single-nucleus RNA sequencing, we characterized the transcriptome of 880,000 nuclei from 18 control and</p> <p>61 failing, nonischemic human hearts with pathogenic variants in DCM and ACM genes or idiopathic</p> <p>disease. We performed genotype-stratified analyses of the ventricular cell lineages and transcriptional</p> <p>states. The resultant DCM and ACM ventricular cell atlas demonstrated distinct right and left ventricular</p> <p>responses, highlighting genotype-associated pathways, intercellular interactions, and differential gene</p> <p>expression at single-cell resolution. Together, these data illuminate both shared and distinct cellular and</p> <p>molecular architectures of human heart failure and suggest candidate therapeutic targets.</p> <p> </p> <p>Link to article: https://www.science.org/doi/10.1126/science.abo1984</p> <p>To match Clinical information (supplementary information) with Sample IDs on Zenodo, please access the Excel table "Additional_note_samples.xlsx" on the github repository: https://github.com/heiniglab/DCM_heart_cell_atlas</p>
Data Deposition: Time-resolved analysis of transcription kinetics in single live mammalian cells
<p>Actb_result_ACF_fitting: Results of the steady-state autocorrelation method from Actb gene. </p> <p>Actb_result_FP: Results of the time-resolved measurement from Actb gene with Flavopiridol. </p> <p>Actb_result_Trp: Results of the time-resolved measurement from Actb gene with triptolide</p> <p>Arc_result_ACF_fitting: Results of the steady-state autocorrelation method from Arc gene. </p> <p>Arc_result_FP: Results of the time-resolved measurement from Arc gene with Flavopiridol. </p> <p>Arc_result_Trp: Results of the time-resolved measurement from Arc gene with triptolide</p> <p>Simulation_steady_state_errors_Actb_210707_n100</p> <p>: results of autocorrelation fitting from steady-state simulation of Actb transcription</p> <p>Simulation_steady_state_errors_Arc_210707_n100</p> <p>: results of autocorrelation fitting from steady-state simulation of Arc transcription</p> <p>Simulation_time_resolved_errors_Actb_210707_n100</p> <p>: results of time-resolved model fitting from initiation inhibited simulation of Actb transcription</p> <p>Simulation_time_resolved_errors_Arc_210707_n100</p> <p>: results of time-resolved model fitting from initiation inhibited simulation of Arc transcription</p>
Custom codes related to the publication "Mitigating transcription-replication conflicts: In early Drosophila embryos, rapid onset of transcription after mitosis depends on DNA replication"
<p>This dataset includes the custom codes related to the publication: Mitigating transcription-replication conflicts: In early Drosophila embryos, rapid onset of transcription after mitosis depends on DNA replication, Cell Reports 2022.</p> <p>The raw imaging data can be found at https://doi.org/10.5281/zenodo.7102432</p>
Transcription factor expression is the main determinant of variability in gene co-activity
<p><strong>Summary</strong></p> <p>Co-activity scores for 343 GEUVADIS LCLs and ABC scores for 68 LCLs, of which 30 are contained in both.</p> <p><strong>Project abstract</strong></p> <p>Many genes are co-regulated and, when proximal, form domains of coordinated gene activity. However, the regulatory determinants of domain co-activity remain unclear. Here, we leverage human individual variation in gene expression to characterize the regulatory processes underlying the activities of such domains and systematically quantify their effect sizes. We employ transcriptional decomposition to extract from RNA expression data an expression component related to co-activity revealed by genomic positioning. This strategy reveals close to 1,500 domains of co-activity, covering most expressed genes, of which the large majority are invariable across individuals. Focusing specifically on domains with high variation in co-activity reveals that neighboring genes contained within variable co-activity domains have a higher sharing of eQTLs, a higher variability in enhancer interactions, and a specific enrichment of binding by variably expressed transcription factors. Through careful quantification of the relative contributions of regulatory activities underlying co-activity, we find transcription factor expression levels to be the main determinant of gene co-activity, indicating that distal <em>trans</em> effects contribute more than local genetic variation to individual variation in co-activity domains. </p> <p><strong>Included files</strong></p> <p>Co_activity_scores_343_individuals.tsv.zip - Contains co-activity scores for included individuals. Columns include chromosome, bin, start, end and one column per individual containing the co-activity score.</p> <p>ABC_scores_68_individuals.tsv.zip - Contains ABC scores for included individuals. Columns include chromosome, start of putative enhancer region, end of putative enhancer region, name of putative enhancer region, target gene, TSS of target gene, LCL identifier, ABC score</p>
Implication of polymerase recycling for nascent transcript quantification by live cell imaging
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The developmental and evolutionary characteristics of transcription factor binding site clustered regions based on an explainable machine learning model
<p>## Identification of transcription factor binding sites clustered regions</p> <p>First, the TFBSs were identified from ATAC-seq peaks by FIMO. The position-specific weight matrices (PWMs) of transcription factors were downloaded from CIS-BP databases. The genomic sequences under the open chromatin regions were used as inputs for FIMO with a custom library of all motifs for each species to scan for motif instances at a p-value threshold of 1e-5. </p> <p>Then, an established method was used to identify TFCRs by performing the Gaussian kernel density estimations across the genome (with a bandwidth of 300bp centered on each TFBS). Each peak in density profile was considered a TFCR. To determine the complexity of each TFCR, the Gaussian kernelized distances from each peak that contributed at least 0.1 to its strength were determined. The complexity of each TFCR was determined by the quantity and proximity of the contributing TFBS. We combined motif instances based on the TF family information from CIS-BP to calculate the complexity of TFCR. The window for each TFCR was determined by finding the maximum distance (in bp) from the TFCR to a contributing TF and then adding 150 bp (one-half of the bandwidth). Each window was centered on the TFCR. The identified TFCR was grouped into 10 groups based on their complexity from low to high. </p> <p>usage: <br>indir="Human_fimo" # the directory where you put the output files of FIMO <br>motifMap="Homo_sapiens_2020_0920/TF_Information_all_motifs_plus.txt" # the mapping relationship of TF and its TF family from CIS-BP <br>cd Codes/TFCR_embryo <br>perl d-motif_combine.pl $indir TFfamily $motifMap <br>perl e-tfpos_combine.pl TFfamily <br>perl f1-tf_bed-new-c.pl TFfamily <br>perl 0-merge-TFCR.pl $indir TFfamily </p>
Interview Transcript of 'Agrikultural ta Paddakal na Danum: A Synthesis of Collective Knowledge through a Farmer-Nature Landscape Approach for the Agricity of Ilagan, Isabela'
<p><span>This document separately presents the interview transcriptions of the four key informant interviews and focus group discussion that were conducted in Inere’s undergraduate thesis project entitled Agrikultura ta Paddakal na Danum: A Synthesis of Collective Knowledge through a Farmer-Nature Landscape Approach for the Agricity of Ilagan, Isabela (2024). </span></p>
Data for "The glucocorticoid receptor potentiates aldosterone-induced transcription by the mineralocorticoid receptor"
<p>This deposit contains all the single-molecule trajectories reported in "The glucocorticoid receptor potentiates aldosterone-induced transcription by the mineralocorticoid receptor".</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>
Transcriptional profiling of ARPE-19 cells infected with high capacity MCMV vector.
<p><span>To gain a detailed knowledge about the virus cycle of murine cytomegalovirus (MCMV) and its high capacity vector in cross-species settings, we analyzed the early and late viral and host transcriptome upon infection of human cells. ARPE-19, A549, and 911 cells (5.0×10<sup>5 </sup>cells/well) were infected in a 24-well format 4 h post-seeding with MCMV-wt or Q4-LRBAs-GLuc, a high capacity replication competent vector based on MCMV, at an MOI of 3. As control, we used infection of mouse embryonic fibroblast, which is the natural host of the wild type MCMV, treated similarly. Harvesting occurred at 8 hpi and 31 hpi through centrifugation at 1.000 g for 5 min. Cell pellets were washed with PBS, re-suspended in 350 µL of RLT buffer, and processed using the RNeasy Mini kit as per the manufacturer’s instructions in independent triplicates. At least 1.500 ng of RNA in a 30 µL volume was isolated and sent for Illumina next-generation sequencing, resulting in paired-end sequencing with a read length of 2×100 bp and a depth of 20 million reads. This submission contains the reads, we obtained analyzing the infections of ARPE-19 cells. The data sheet for the samples and the reference genomes (.gb), which we used in the analysis published in Riedl et al. Vaccines 2024 can be found in the REFERENCES_ARPE-19.zip folder. Please, find the control reads for uninfected cells in a separate upload entitled: Mock infected controls for transcriptional profiling of infections with high capacity MCMV vector (DOI 10.5281/zenodo.1250410).</span></p>
Transcriptional profiling of 911 cells infected with high capacity MCMV vector.
<p>To gain a detailed knowledge about the virus cycle of murine cytomegalovirus (MCMV) and its high capacity vector in cross-species settings, we analyzed the early and late viral and host transcriptome upon infection of human cells. ARPE-19, A549, and 911 cells (5.0×10<sup>5 </sup>cells/well) were infected in a 24-well format 4 h post-seeding with MCMV-wt or Q4-LRBAs-GLuc, a high capacity replication competent vector based on MCMV, at an MOI of 3. As control, we used infection of mouse embryonic fibroblast, which is the natural host of the wild type MCMV, treated similarly. Harvesting occurred at 8 hpi and 31 hpi through centrifugation at 1.000 g for 5 min. Cell pellets were washed with PBS, re-suspended in 350 µL of RLT buffer, and processed using the RNeasy Mini kit as per the manufacturer’s instructions in independent triplicates. At least 1.500 ng of RNA in a 30 µL volume was isolated and sent for Illumina next-generation sequencing, resulting in paired-end sequencing with a read length of 2×100 bp and a depth of 20 million reads. This submission contains the reads, we obtained analyzing the infections of 911 cells. The data sheet for the samples and the reference genomes (.gb), which we used in the analysis published in Riedl et al. Vaccines 2024 can be found in the REFERENCES_911.zip folder. Please, find the control reads for uninfected cells in a separate upload entitled: Mock infected controls for transcriptional profiling of infections with high capacity MCMV vector (DOI 10.5281/zenodo.1250410).</p>
Transcripts Demonstrating the Application of ChatGPT in the Composition of the Manuscript "Deciphering Cancer Genomes with GenomeSpy: A Grammar-Based Visualization Toolkit" by Lavikka, et al.
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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.