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21,320 results for “Transcription”
A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution
<p>The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kölliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled ~1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed many neuronal subtypes' unique marker genes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this study's translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard (<a href="https://urldefense.com/v3/__http:/harvard.heavy.ai:6273/__;!!AIv8Mrc!-cGoARlBC8C7UnXDMr7w17gn4sMqIB-ToOkvqcj__zFp6n1lf2JlwGy84WzhhDRhY_jiL2y3k7ficQnBaHYX8TPF473zKP64$">http://harvard.heavy.ai:6273/</a>) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data. </p>
Data for paper "Detection of new pioneer transcription factors as cell-type specific nucleosome binders"
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An integrated single-cell RNA-seq atlas of the mouse hypothalamic paraventricular nucleus links transcriptional and function types
<p>The hypothalamic paraventricular nucleus (PVN) is a highly complex brain region that is crucial for homeostatic<br> regulation through neuroendocrine signalling, outflow of the autonomic nervous system (ANS), and projections<br> to other brain areas. The past years, single-cell datasets of the hypothalamus have contributed immensely<br> to the current understanding of the diverse hypothalamic cellular composition. While the PVN has been<br> adequately classified functionally, its molecular classification is currently still insufficient.</p> <p>To address this, we created a detailed atlas of PVN transcriptional cell types by integrating various PVN<br> single-cell datasets into a recently published hypothalamus single-cell transcriptome atlas. Furthermore, we<br> functionally profiled transcriptional cell types, based on relevant literature, existing retrograde tracing data<br> and existing single-cell data of a PVN-projection target region.</p>
Transcriptional response to long-term thermal acclimation in an ecologically relevant marine cyanobacterium of the ubiquitous Synechococcus clade II
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Transcripts for the "What is Token Engineering? A stakeholder study" publication
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Data Transcripts RBF Analysis of social investment in health systems reform
<p>Data transcripts</p>
Domain scanning results for a selected set of high-quality-annotation protein isoforms produced by human transcription factor genes
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Transcription data
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Interview Transcript - EA
<p>Transcript for IA in Global Politics</p>
Supplementary material 1 from: Calderón-Rosete G, González-Barrios JA, Piña-Leyva C, Moreno-Sandoval HN, Lara-Lozano M, Rodríguez-Sosa L (2021) Transcriptional identification of genes light-interacting in the extraretinal photoreceptors of the crayfish Procambarus clarkii. ZooKeys 1072: 107-127. https://doi.org/10.3897/zookeys.1072.73075
Appendix S1, S2
Figure 1 from: Calderón-Rosete G, González-Barrios JA, Piña-Leyva C, Moreno-Sandoval HN, Lara-Lozano M, Rodríguez-Sosa L (2021) Transcriptional identification of genes light-interacting in the extraretinal photoreceptors of the crayfish Procambarus clarkii. ZooKeys 1072: 107-127. https://doi.org/10.3897/zookeys.1072.73075
Figure 1 Comparative alignments of opsins reported in the crayfish Procambarus clarkiiA long-wavelength-sensitive opsin (UniProtKB/Swiss-Prot: P35356.1; Hariyama et al. 1993); (GenBank: ALJ26467 1; Kingston and Cronin 2015); (Procl_ES_23_0; Manfrin et al. 2015) B short-wavelength-sensitive opsin (GenBank: ALJ26468.1; Kingston and Cronin 2015); (Procl_ES_11143_0; Manfrin et al. 2015)
Inference of molecular mechanisms of transcriptional regulation from co-expression data
<p>The accompanying data for the article "Inference of molecular mechanisms of transcriptional regulation from co-expression data". The article is now live on Research Square <a href="https://doi.org/10.21203/rs.3.rs-1262163/v1">10.21203/rs.3.rs-1262163/v1</a></p>
transcription of a male with Wernicke's aphasia
<p>A transcription of a re-telling of a story of a cartoon "Shaun the Sheep" within exploring the relationship between speech and gestures in persons with aphasia: Evidence from the Czech perspective.</p>
Data from: Physiological and transcriptional immune responses of a non-model arthropod to infection with different entomopathogenic groups
<p>Insect immune responses to multiple pathogen groups including viruses, bacteria, fungi, and entomopathogenic nematodes have traditionally been documented in model insects such as <em>Drosophila melanogaster</em>, or medically important insects such as <em>Aedes aegypti</em>. Despite their potential importance in understanding the efficacy of pathogens as biological control agents, these responses are infrequently studied in agriculturally important pests. Additionally, studies often neglect to investigate responses against different pathogen groups, and typically focus on only a single time point during infection. As such, a robust understanding of immune system responses over the time of infection is often lacking. This study was conducted to understand how 3<sup>rd</sup> instar larvae of the major insect pest <em>Helicoverpa zea</em> responded over time to infection by four different pathogenic groups: viruses, bacteria, fungi, and entomopathogenic nematodes. Physiological immune responses were assessed at 4-, 24-, and 48-hours post-infection by measuring hemolymph phenoloxidase concentrations, hemolymph prophenoloxidase concentrations, hemocyte counts, and encapsulation ability. Transcriptional immune responses were measured at 24-, 48-, and 72-hours post-infection by quantifying the expression of <em>PPO2</em>, <em>Argonaute-2</em>, <em>JNK</em>, <em>Dorsal</em>, and <em>Relish</em>. This gene set covers the major known immune pathways: phenoloxidase cascade, siRNA, JNK pathway, Toll pathway, and IMD pathway. Our results indicate <em>H. zea</em> has an extreme immune response to <em>Bacillus thuringiensis</em> bacteria, a mild response to <em>Helicoverpa armigera</em> nucleopolyhedrovirus, and no detectable response to either the fungus <em>Beauveria bassiana</em> or <em>Steinernema carpocapsae </em>nematodes.</p>
Evolution of binding preferences among whole-genome duplicated transcription factors
<p>Throughout evolution, new transcription factors (TFs) emerge by gene duplication, promoting growth and rewiring of transcriptional networks. How TF duplicates diverge is known for only a few studied cases. To provide a genome-scale view, we considered the 35% of budding yeast TFs, classified as whole-genome duplication (WGD)-retained paralogs. Using high-resolution profiling, we find that ~60% of paralogs evolved differential binding preferences. We show that this divergence results primarily from variations outside the DNA binding domains (DBDs), while DBD preferences remain largely conserved. Analysis of non-WGD orthologs revealed that ancestral preferences are unevenly split between duplicates, while new targets are acquired preferentially by the least conserved paralog (biased sub/neo-functionalization). Dimer-forming paralogs evolved mostly one-sided dependency, while other paralogs interacted through low-magnitude DNA-binding competition that minimized paralog interference. We discuss the implications of our findings for the evolutionary design of transcriptional networks.</p>
Data from: Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast
Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.
The novel regulator HdrR controls the transcription of the heterodisulfide reductase operon hdrBCA in Methanosarcina barkeri
<p><span>RNA-seq raw data and processed data are accessible at Zenodo.</span></p>
Source Data for the study of Sen1-dependent transcription termination
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Transcription of focus group meetings
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Transcriptional atlas of kidney organoid development
<p>This repository contains curated AnnData objects for the kidney organoid atlas published in Wilson et al., 2024 (1) and further analysed in Wilson et al., 2024 (2). These objects can be opened in a cellxgene instance for instant visualisation and investigation.</p> <p>Wilson_kidney_organoid_atlas.h5ad; The entire dataset, all cells at all time points</p> <p>Wilson_KidneyOrganoid_Stage1_atlas.h5ad; The cells in Stage1, containing iPSC, day 3, day 4 and day 5 time-points</p> <p>Wilson_KidneyOrganoid_Stage2_atlas.h5ad; The cells in Stage2, all day 7, day 8 and day 9 time-points</p> <p>Wilson_KidneyOrganoid_Stage3_atlas.h5ad; The cells in Stage3, all day 12, day 13 and day 14 time-points</p> <p>Wilson_KidneyOrganoid_Stage4_atlas.h5ad; The cells in Stage4, all day 27 time-points, the final collection point and the most mature organoids.</p> <p> </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.