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21,320 results for “Transcription”
Transcript of FGD and Interview of participant and expert in survey and health PR
<p>Transcript in bahasa Indonesia about FGD expert panel and interview of survei participant on AI and health PR</p>
Annexe 21- Texte de la transcription des entrevues semi-dirigée réalisée auprès de 23 usagers réels ou potentiels de la traduction ou de l'interprétation
<p>Annexe 21 - Texte de la transcription des entrevues semi-dirigée réalisée auprès de 23 usagers réels ou potentiels de la traduction ou de l’interprétation</p>
Annexe 22 Texte de la transcription des entrevues semi-dirigées réalisées auprès de 23 usagers réels ou potentiels de la traduction ou de l'interprétation
<p>Annexe 22 - Texte de la transcription des entrevues semi-dirigées réalisées auprès de 23 usagers réels ou potentiels de la traduction ou de l’interprétation</p>
Dissociation rate compensation mechanism for budding yeast pioneer transcription factors
<p>Single molecule data sets related to "Dissociation rate compensation mechanism for budding yeast pioneer transcription factors."</p>
Processed data - DegNorm: Normalization of generalized transcript degradation improves accuracy in RNA-seq analysis
<p>Processed data from DegNorm:</p> <ul> <li>"_raw.txt": raw read counts matrix;</li> <li>"_DI.txt": Degradation index score matrix;</li> <li>"_DegNorm.txt": normalized read counts matrix from DegNorm output;</li> <li>"_coverage.Rdata": list of coverage matrix for the sample;</li> <li>"_countsTIN.txt": TIN normalized counts.</li> </ul> <p> </p>
Datasets for : High-resolution detection and differential expression analysis of transcription start sites using MAPCap
<p>This dataset corresponds to the study: High-resolution detection and differential expression analysis of transcription start sites using MAPCap (Bhardwaj et. al. 2018)</p> <p>It includes:</p> <p> - TSS identified using MAPCap in stage 15 embryos and larvae.</p> <p> - Differentially expressed TSS using MAPCap (FDR < 0.05) in larvae.</p> <p> - Common and stage-specific enhancer TSS identified in this study</p>
Benchmark and integration of resources for the estimation of human transcription factor activities
<p>Data used to benchmark human TF-target datasets via TF activities in 3 benchmark datasets. Described in <a href="https://www.biorxiv.org/content/biorxiv/early/2018/06/18/337915.full.pdf">Garcia-Alonso et al 2019</a></p> <p>Check <a href="https://github.com/saezlab/TFbenchmark">https://github.com/saezlab/TFbenchmark</a> to access the corresponding code.</p> <p> </p> <p> </p> <p><strong>Study abstract</strong></p> <p>Prediction of transcription factor (TF) activities from the gene expression of their targets (i.e. TF regulon) is becoming a widely-used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and datasets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are: (i) manually curated repositories, (ii) interactions derived from ChIP-seq binding data, (iii) <em>in silico</em> prediction of TF binding on gene promoters, and (iv) reverse-engineered regulons from large gene expression datasets. However, it is not known how these different sources of regulons affect the TF activity estimations, and thereby downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark datasets. We assembled a collection of TF-target interactions among 1,541 TFs and evaluated how the different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities or mode of interaction with the chromatin, affect the predictions of TF activity changes. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies with a confidence score, as a resource for enhanced prediction of TF activities.</p>
Transkripte von drei TV-Debatten mit Schweizer PolitikerInnen vor eidgenössischen Abstimmungen. Transcripts of three TV debates with Swiss politicians before popular votes.
<p>Der Datensatz enthält Transkripte (doc, htm, PDF) von drei 'Arena'-Abstimmungssendungen mit Schweizer PolitikerInnen vor eidgenössischen Abstimmungen. Die TV-Sendungen wurden nach GAT 2 transkribiert / The dataset contains transcripts (doc, htm, PDF) of three 'Arena' TV-debates with Swiss politicians before popular votes. The debates were transcribed according to GAT 2.</p>
Chlamydomonas pacifica Lipid Transcription Factors
<p>Contains the plasmid sequence for <span>pJPCHx1_CpaDpWRI1, pJPCHx1_CpaAtWRI1, pJPCHx1_CpaMYB6, pJPCHx1_CpabZIP1, pJPCHx1_CpaSPL12, pJPCHx1_CpaPSR1, pJPCHx1_CpaCHT7, pJPCHx1_CpaNRR1, and pJPCHx1_CpaLRL1.</span></p>
Identification of Mitoxantrone as a Potent Inhibitor of Transcriptional Cyclin-Dependent Kinase 7 via Structure-based Virtual Screening
<p>Data associated with the manuscript - Identification of Mitoxantrone as a Potent Inhibitor of Transcriptional Cyclin-Dependent Kinase 7 via Structure-based Virtual Screening </p>
Participant Data and Code for "Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video"
<p>All code produced to analyze the participant response data and the participant response data itself are included in this repository. </p>
Benchmarking Illumina RNA-seq fusion transcript detection methods - simulated 101 base PE reads
<p>Simulated 101 base paired-end reads used for benchmarking Illumina-based fusion detection methods as used in:</p> <p>Haas, B.J., Dobin, A., Li, B. <em>et al.</em> Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. <em>Genome Biol</em> <strong>20</strong>, 213 (2019). https://doi.org/10.1186/s13059-019-1842-9</p> <p> </p>
Supplementary documents for Decitabine Increases the Transcription of RIG-I Gene to Sup-press the Replication of Feline Calicivirus and Canine Influen-za Virus
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Correspondence-Transcription-Rashleigh-Forster-Heuland (Cornwall Archives)
<p>Source: Transcription of the letters exchanged between H. Heuland, E. Forster and P. Rashleigh (Cornwall Archives).</p>
Transcription factor clusters in fruit fly embryos
<p>These files represent the dataset used in generating plots shown in <a href="https://arxiv.org/abs/2403.02943">[2403.02943] Transcription factor clusters as information transfer agents (arxiv.org)</a></p> <p>The MATLAB programs to generate the plots using this data can be found in:</p> <p>https://github.com/ancientman/clusters-plots</p> <p>Please refer to the instructions there.</p> <p> </p> <p> </p>
Development of compact transcriptional effectors using high-throughput measurements in diverse contexts
<p>HT-recruit and CRISPR HT-recruit processed datasets </p> <p>Abstract: <span>Transcriptional effectors are protein domains known to activate or repress gene expression, however, a systematic understanding of which effector domains regulate transcription robustly across genomic, cell-type, and DNA-binding domain (DBD) contexts is lacking. Here, we develop dCas9-mediated high-throughput recruitment (HT-recruit), a pooled screening method for quantifying effector function at endogenous target genes, and test effector function for a library containing 5,092 nuclear protein Pfam domains across varied contexts. We also map context dependencies of effectors drawn from unannotated protein regions using a larger library containing 114,288 sequences tiling chromatin regulators and transcription factors. We find that many effectors depend on target and DBD contexts, such as HLH domains that can act as either activators or repressors. To enable efficient perturbations, we select context-robust domains, including ZNF705 KRAB, that improve CRISPRi tools to silence promoters and enhancers. We engineer a compact human activator NFZ by combining several domains, which enables efficient CRISPRa with better viral delivery, and inducible control of CAR T-cells. Together, this effector-by-context functional map reveals context-dependence across human effectors and guides effector selection for manipulating transcription.</span></p>
Oryctolagus cuniculus CF_000003625.3_OryCun2.0 extended transcript annotation
<p>Extended transcript annotation GTF files, based on the CF_000003625.3_OryCun2.0_genomic.gtf reference. Transcripts have been identified from an infection time course experiment of rabbit kidney epithelial cell line infected with equine alphaherpesvirus 1. </p> <p>Supplementary material to the publication "Temporal Transcriptional Profiling of Host Cells Infected by a Veterinary Alphaherpesvirus using Nanopore Sequencing" by Tombácz et al, 2024.</p>
Single-molecule analysis of transcription activation: dynamics of SAGA co-activator recruitment
<p>Source data for:</p> <p><strong>Single-molecule analysis of transcription activation: dynamics of SAGA co-activator recruitment</strong></p> <p><strong> </strong></p> <p>Jongcheol Jeon<sup>1</sup>, Larry J. Friedman<sup>2</sup>, Daniel H. Zhou<sup>2</sup>, Hogyu David Seo<sup>1</sup>, Oluwatobi A. Adeleke<sup>3</sup>, Bria Graham<sup>3</sup>, Emily F. Patteson<sup>3</sup>, Jeff Gelles<sup>2</sup>*, and Stephen Buratowski<sup>1,</sup>*</p> <p> </p> <p><sup>1</sup>Department of Biological Chemistry and Molecular Pharmacology,</p> <p>Harvard Medical School, Boston, MA 02115</p> <p><sup>2</sup> Department of Biochemistry, Brandeis University, Waltham, MA 02454</p> <p><sup>3</sup> EpiCypher Inc., Durham NC 27709</p> <p> </p> <p>*Corresponding authors</p> <p>Lead author contact information:</p> <p><a href="mailto:steveb@hms.harvard.edu">steveb@hms.harvard.edu</a></p> <p> </p> <p> </p> <p>The source data for the single molecule experiments are provided as "intervals" files and "CoSMoS parameters", which can be read and manipulated using the publicly available programs "Imscroll" and "Tapqir", respectively:</p> <p><a href="https://github.com/gelles-brandeis/CoSMoS_Analysis">https://github.com/gelles-brandeis/CoSMoS_Analysis</a></p> <p><a href="https://github.com/gelles-brandeis/tapqir">https://github.com/gelles-brandeis/tapqir</a></p>
Fig 5 in Docosahexaenoic Acid (DHA) Reduces LPS-Induced Inflammatory Response Via ATF3 Transcription Factor and Stimulates Src/Syk Signaling-Dependent Phagocytosis in Microglia
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Fig 3 in Docosahexaenoic Acid (DHA) Reduces LPSInduced Inflammatory Response Via ATF3 Transcription Factor and Stimulates Src/ Syk Signaling-Dependent Phagocytosis in Microglia
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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.