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2,788 results for “remodeling”
Ligand binding remodels protein side chain conformational heterogeneity
<p>While protein conformational heterogeneity plays an important role in many aspects of biological function, including ligand binding, its impact has been difficult to quantify. Macromolecular X-ray diffraction is commonly interpreted with a static structure, but it can provide information on both the anharmonic and harmonic contributions to conformational heterogeneity. Here, through multiconformer modeling of time- and space-averaged electron density, we measure conformational heterogeneity of 743 stringently matched pairs of crystallographic datasets that reflect unbound/apo and ligand-bound/holo states. When comparing the conformational heterogeneity of side chains, we observe that when binding site residues become more rigid upon ligand binding, distant residues tend to become more flexible, especially in non-solvent exposed regions. Among ligand properties, we observe increased protein flexibility as the number of hydrogen bonds decrease and relative hydrophobicity increases. Across a series of 13 inhibitor bound structures of CDK2, we find that conformational heterogeneity is correlated with inhibitor features and identify how conformational changes propagate differences in conformational heterogeneity away from the binding site. Collectively, our findings agree with models emerging from NMR studies suggesting that residual side chain entropy can modulate affinity and point to the need to integrate both static conformational changes and conformational heterogeneity in models of ligand binding.</p>
Backpain exercise therapy remodels human epigenetic profiles in buccal and human peripheral blood mononuclear cells: An exploratory study in young male participants
<pre><strong>###### Files description #####</strong><br> <strong>Notes</strong>. 1) "BT" refers to before therapy and "AT" to after therapy. 2) 0 refers to FALSE and 1 to TRUE for binary variables. The provided files have tab-separated columns except the .RDS which is and R output of the mixOmics DIABLO integration analysis. <strong># Questionnaire</strong> > participants_categories.tsv: per participant (rows), output of the clustering with the participant ("ID") category ("category") per class<br> ("class") > questionnaire_agility_metrics.tsv: questionnaire and agility metrics per participant (rows) for the participants ("ID") with at least one paired AT+BT data in one type of biological sample (indicated in the columns "swab", "PBMC", and "plasma") <strong># PTMs</strong> Samples´ names are encoded as PBMC_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch (we removed _batch column suffix for the <br>processed files). NA indicates an undetected intensity. > raw_PBMC_light_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_light_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavy_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_heavy_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavynormalized_intensities.tsv: raw intensity of light peptides normalized by heavy peptides intensity (row) by precursor per <br>sample (column) > raw_swab_heavynormalized_labelled_intensities.tsv: idem from buccal cells > processed_cleaned_PBMC_log2intensities.tsv: processed (heavy normalized, imputed, batch-corrected) intensity of peptides aggregated by modification (PTM, row) by precursor per sample (column) after log2-transformation. The relative abundances are computed from this file. Rows without me/ac suffix represents the amount of unmodified peptide for the considered site. > processed_cleaned_swab_log2intensities.tsv: idem from buccal cells > rel_abundance_PTM_PBMC.tsv: relative abundance computed per precursor, e.g. for a given sample, the H3_K4+H3_K4me1+H3_K4me2+H3_K4me3 <br>relative abundance values must sum to 100, with the relative abundance of H3_K4 representing the absence of modified K4. > rel_abundance_PTM_swab.tsv: idem from buccal cells > tests_from_rel_abundance_PTM_swab_PBMC.tsv: per type of samples ("Sample.origin", i.e.swab of PBMC) and per PTM (rows, "PTM"), report <br>the output of classic (p-values, adjusted with Benjamini-Hochberg (BH), or Benjamini-Yekutieli procedure (BY), from raw and arcsin square <br>root transformed percentage) and PLS-DA tests (VIP - Variable Importance score - and its 95% confidence interval). The percentage of change<br>of each PTM after therapy relative tobefore therapy is reported in "perc_change.AT.over.BT" column. The "is_candidate" indicates if the PTM has been considered as a hit in the swab or PBMC. <strong># Plasma</strong> Samples´ names are encoded as PLASMA_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch. NA indicates an undetected intensity. > raw_plasma_maxquant_log2ibaq_intensities.tsv: raw data from protein group MaxQuant file. The iBAQ columns are used in later steps. > processed_cleaned_plasma_log2intensities.tsv: processed (imputed, batch-corrected) intensity of protein groups after log2-transformation. > tests_from_intens_plasma.tsv: per protein group ("Proteins.ID"), report the output of classic (p-values, adjusted Benjamini-Hochberg (BH),<br>or Benjamini-Yekutieli procedure (BY), from log2-transformed intensities) and PLS-DA tests (VIP and its 95% confidence interval). The log2 <br>fold change after therapy relative to before therapy is reported in "log2FC.AT.over.BT" column. The "is_candidate" indicates if the protein group has been considered as a hit. <strong># Integration</strong> > circos_input: output of DIABLO analysis with correlation threshold set to 0.7. Use the readRDS R function to open.</pre> <p> </p>
Xiao et al, Oligodendrocyte Precursor Cells Sculpt the Visual System by Regulating Axonal Remodeling [Dataset]
<p>Raw data from the behavior and imaging experiments of Xiao et al., Nature Neuroscience 2022. For additional details about the acquisition of each part of the dataset, refer to the methods section of the paper. From this dataset, using the published code, all the figures relative to the imaging in the optic tectum can be generated and all the cumulative statistics for the behavioural assays run with Stytra recomputed.</p> <p> </p> <p><strong>Organisation of the dataset</strong></p> <p>This dataset is organised in the following subdirectories:<br> - <em>freely_swimming</em>: contains the data for the freely swimming experiments quantifying motor activity in the various ablated groups. It contains subfolders of groups, each of which contains the Stytra raw data directories for all fish of that group. Refer to `Stytra` and `bouter` documentation for further details about the files.<br> - <em>OMR</em>: contains the data for the quantification of OMR reflex across different spatial frequencies. It contains subfolders for the control and ablated group, each of which contains the Stytra raw data directories for all fish of that group.<br> - <em>receptive_field_imaging</em>: contains the imaging data for the receptive field estimation. Subfolders contains, for each individual fish (both ablated and controls are pooled in the same directory):<br> - stytra raw output from the experiment<br> - data_from_suite2p_unfiltered.h5: `flammkuchen`-loadable `.h5` file that contains the raw fluorescent trace<br> - anatomy.mask: `flammkuchen`-loadable mask file saved by the `pypra` tool that was used for segmenting the tectum, delimiting the region of the tectum<br> The folder contains an additional file, `manual_alignment_offsets.h5`, where the offsets of the manual morphing across fish were saved.</p>
Escape from NK cell tumor surveillance by NGFR-induced lipid remodeling in melanoma
<p>Metabolomics Data used in the publication</p> <p>1) raw files obtained by the FGCZ (<a href="https://fgcz.ch/">Functional Genomics Center Zurich</a>):</p> <p>QCpools (technical replicate of all sample pooled: p2947_o5292_SOP3_DDA5_pos_QCpool_cells</p> <p>Sample of M010817 CMVTOEV cells not induced: p2947_o5292_SOP3_DDA5_pos_EV_cells_noninduced_sample</p> <p>Sample of M010817 CMVTOEV cells induced: p2947_o5292_SOP3_DDA5_pos_EV_cells_induced_sample</p> <p>Sample of M010817 CMVTONGFR cells not induced: p2947_o5292_SOP3_DDA5_pos_p75_cells_noninduced_sample</p> <p>Sample of M010817 CMVTONGFR cells induced: p2947_o5292_SOP3_DDA5_pos_p75_cells_induced_sample</p> <p>2) method description LC-MS</p> <p>LC-MS-based lipidomic analysis was performed with M010817 CMVTOEV and CMVTONGFR cells pretreated with 1 μg/ml doxycycline for 24 h. Cells were detached with PBS 2 mM EDTA, washed and resuspended in 1-butanol/methanol (1:1). Cells were vortexed for 15 sec and subsequently sonicated on ice with a pulse of 3x 10 sec and 1x 30 sec at an amplitude of 15% using a SONOPULS HD 2070 Ultrasonic Homogenizer (Bandelin). Cell extracts were centrifuged at 16’000 g, 20°C for 10 min to remove macromolecules and subsequently diluted (1:3) with water/methanol (1:2) solution. The dilution was vortexed and centrifuged (16,000 x g, 20 °C, 10 min). 100 μl of the supernatant was transferred to a glass vial with narrowed bottom (Total Recovery Vials, Waters) for LC-MS injection. <br> Lipids were separated on a nanoAcquity UPLC (Waters) equipped with a HSS T3 capillary column (150 μm x30mm, 1.8 μm particle size, Waters), applying a gradient of 5 mM ammonium acetate in water/acetonitrile 95:5 (A) and 5 mM ammonium acetate in isopropanol/acetonitrile 90:10 (B) from 5% B to 100% B over 10 min. The following 5 min conditions were kept at 100% B, followed by 5 min reequilibration to 5% B. The injection volume was 1 μL. The flow rate was constant at 2.5 μl/min. The UPLC was coupled to QExactive mass spectrometer (Thermo) by a nanoESI source. MS data was acquired using positive polarization and data-dependent acquisition (DDA). Full scan MS spectra were acquired in profile mode from 80-1200 m/z with an automatic gain control target of 1e6, an Orbitrap resolution of 70`000, and a maximum injection time of 200 ms. The 5 most intense charged (z = +1 or +2) precursor ions from each full scan were selected for collision induced dissociation fragmentation. Precursor was accumulated with an isolation window of 0.4 Da, an automatic gain control value of 5e4, a resolution of 17`500, a maximum injection time of 50 ms and fragmented with a normalized collision energy of 20, 30 and 40 (arbitrary unit). Generated fragment ions were scanned in the linear trap. Minimal signal intensity for MS2 selection was set to 500.</p> <p> </p> <p>3) csv result files were generated by the FGCZ (<a href="https://fgcz.ch/">Functional Genomics Center Zurich</a>)</p> <p>adducts found: adducts_p2947_o5292_new_10K_20210910</p> <p>identifications found: identifications_p2947_o5292_new_10K_20210910</p> <p>abundances of features: measurements_p2947_o5292_new_10K_20210910</p> <p>Metaboanalyst file statistics: Metabo_p2947_o5292_10k_cells_CVcleaned_20210916</p> <p>Metaboanalyst file enrichment: Metabo_Enrichment_p2947_o5292_10k_cells_20210910 _p75</p> <p>4) method description data analysis</p> <p>Data sets were evaluated with Progenesis QI software (Nonlinear Dynamics), which aligns the ion intensity maps based on a reference data set, followed by a peak picking on an aggregated ion intensity map. Detected ions were identified based on accurate mass, detected adduct patterns and isotope patterns by comparing with entries in the LipidMaps Data Base (LM) and KEGG database. Considered adducts were M+H, M+NH4, 2M+H M+H-H2O. A mass accuracy tolerance of 5 ppm was set for the searches. Fragmentation patterns were considered for 600 the identifications of metabolites. Putative identifications were further ranked based on Mass error (observed mass – exact mass), isotope similarity (observed versus theoretical).</p> <p> </p>
raw data of Gut microbiota remodeling and intestinal adaptation to lipid malabsorption after enteroendocrine cell loss in adult mice
<p>Microbiome dataset for "Gut microbiota remodeling and intestinal adaptation to lipid malabsorption after enteroendocrine cell loss in adult mice" publication</p> <p>https://doi.org/10.1016/j.jcmgh.2023.02.013</p> <p> </p>
A cross-study transcriptional patient map of heart failure defines conserved multicellular coordination in cardiac remodeling
<p>Collection of auxiliary data to reproduce the results from "<strong>A cross-study transcriptional patient map of heart failure defines conserved multicellular coordination in cardiac remodeling</strong>". Source code is available at: https://github.com/saezlab/reheat2_pub<br><br>We provide processed data to facilitate access to the results, for the original count data, please see the associated manuscript for references to the original datasets. </p> <p> </p> <p> </p>
Dynamic 1D search and processive nucleosome translocations by RSC and ISW2 chromatin remodelers
<p>Eukaryotic gene expression is linked to chromatin structure and nucleosome positioning by ATP-dependent chromatin remodelers that establish and maintain nucleosome-depleted regions (NDRs) near transcription start-sites. Conserved yeast RSC and ISW2 remodelers exert antagonistic effects on nucleosomes flanking NDRs, but the temporal dynamics of remodeler search, engagement and directional nucleosome mobilization for promoter accessibility are unknown. Using optical tweezers and 2-color single-particle imaging, we investigated the Brownian diffusion of RSC and ISW2 on free DNA and sparse nucleosome arrays. RSC and ISW2 rapidly scan DNA by one-dimensional hopping and sliding respectively, with dynamic collisions between remodelers followed by recoil or apparent co-diffusion. Static nucleosomes block remodeler diffusion resulting in remodeler recoil or sequestration. Remarkably, both RSC and ISW2 use ATP hydrolysis to translocate mono-nucleosomes processively at ~30 bp/sec for surprising distances on extended linear DNA. Processivity and opposing push-pull directionalities of nucleosome translocation shown by RSC and ISW2 shape the distinctive landscape of promoter chromatin.</p>
Erythropoietin directly remodels the clonal composition of murine hematopoietic multipotent progenitor cells
<p>## In version 1 of the repository the scRNAseq data was corrupted -- in version 2 this has been corrected and the bam files of both scRNAseq runs have been uploaded ##</p> <p>This dataset consists of the raw sequencing files of 9 independent barcoding experiments and of one 10X Genomics scRNAseq experiment. These are the source files for the main figures of the associated publication.</p> <p>HSPCs (C-Kit+ Sca1+ CD150+ Flt3-) or MPP2 (C-Kit+ Sca1+ Flt3- CD150+ CD48+) were isolated form mouse bone marrow through flushing, MACS-enrichment and sorting. The cells were barcoded by spin infection for 6h, and cultured in StemSpanMedium SFEM with 50 ng/ml mSCF (STEMCELL Technologies) for 16h with or without human recombinant EPO (Eprex, erythropoietin alpha, Janssen) at 1000 ng/ml or 160 ng/ml. At this stage scRNAseq was perfromed on the 10X Chromium platfom (10X Genomics), or cells were transplanted by tail vein injection into 6Gy irradiated recipient mice. When appropirate an additional injection of EPO 133ug/kg was given at the moment of transplantation. Different mature hematopoietic cells were isolated form the transplanted mice after 4 weeks or 16 weeks.</p> <p>For barcoding experiments, a three-step PCR was performed to amplify barcode sequences, to add Read1 and Read2 Illumina sequencing adapters, P5 and P3 flow cell attachment sites as well as plate and sample indices. Libraries were sequenced on an Illumina HiSeq SR65 with 10% of PhiX spike-in. For scRNAseq experiment, libraries were made using the Chromium SIngle Cell 3' v2 kit and sequencing was performed on a HiSeq PE26-98.</p> <p>The repository encompasses the following datasets:</p> <ul> <li>A1006.tar.gz -- AE05Low -- HSPCs EPO 160 ng/ml + injection 4 weeks -- main figure 2</li> <li>A1007.tar.gz -- AE05High -- HSPCs EPO 1000 ng/ml + injection 4 weeks -- main figure 2</li> <li>A984.tar.gz -- AE03Low -- HSPCs EPO 160 ng/ml 4 weeks -- main figure 2 and HSPC part of main figure 4</li> <li>A1008.tar.gz -- AE03High -- HSPCs EPO 1000 ng/ml 4 weeks -- main figure 1, 2 and DC part of main figure 3</li> <li>A1012.tar.gz -- AE04Low -- HSPCs EPO 160 ng/ml 4 months -- main figure 8</li> <li>A1013.tar.gz -- AE04High -- HSPCs EPO 1000 ng/ml 4 months -- main figure 8</li> <li>A1105.tar.gz -- AE07part1 -- HSPCs EPO 1000 ng/ml 4 weeks part 1 -- MkP part of main figure 3</li> <li>A1107.tar.gz -- AE07part2 -- HSPCs EPO 1000 ng/ml 4 weeks part 2 -- MkP part of main figure 3</li> <li>A1166.tar.gz -- AE13part1 -- HSPCs EPO 1000 ng/ml 4 weeks part 1 -- HSPC part of main figure 4</li> <li>A1166.tar.gz -- AE13part2 -- HSPCs EPO 1000 ng/ml 4 weeks part 2 -- HSPC part of main figure 4</li> <li>D757_3697.tar.gz -- LP26MPP2 -- MPP2 EPO 1000 ng/ml 4 weeks -- main figure 7</li> </ul> <p>In version two of the repository the scRNAseq data is changed into:</p> <ul> <li>possorted_genome_bam_T1.bam -- 10X of EPO exposed (T1) HSPCs -- main figure 5 and 6</li> <li>possorted_genome_bam_T2.bam -- 10X of control (T2) HSPCs -- main figure 5 and 6</li> </ul> <p>The folder for each barcode experiment encompasses fastq files for each sample index used in the experiment. These have to be further de-multiplexed by plate index and barcode reads have to be called. These, and all subsequent processing steps till the making of the main figures are described in the github folder accompanying the associated publication.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Harmonized remodeled energy system transformation strategies for Germany - additional data
<p>This dataset compares 10 different scenarios for the transformation of the German energy system by 2050. These scenarios were used in the <a href="https://www.innosys-projekt.de">InNOSys project</a> as a starting point for a multidimensional impact assessment and evaluation of different transformation strategies (see also <a href="https://www.mdpi.com/2071-1050/13/9/5217">https://www.mdpi.com/2071-1050/13/9/5217</a>).<br> As a source of inspiration for these scenarios, 10 different transformation strategies were used, as published for Germany in 2012-2018. However, for the present document, the original scenarios were re-modeled in a harmonized way.</p> <p>An additional documentation of the scenarios is also available on ZENODO. </p>
Gene expression plasticity, genetic variation and fatty acid remodelling in divergent populations of a tropical bivalve species: lipid profiles
<p><span>Ocean warming challenges marine organisms' resilience, especially for species experiencing temperatures close to their upper thermal limits. A potential increase in thermal tolerance might significantly reduce the risk of population decline, which is intrinsically linked to variability in local habitat temperatures.</span></p> <p><span>Our goal was to assess the plastic and genetic potential of response to elevated temperatures in a tropical bivalve model, <em>Pinctada margaritifera</em>. We benefit from two ecotypes for which local environmental conditions are characterized by either large diurnal variations in the tide-pools (Marquesas archipelago) or lower mean temperature with stable to moderate seasonal variations (Gambier archipelago).</span><br><br><span>We explored the physiological basis of individual responses to elevated temperature<em>, </em>genetic divergence as well as plasticity and acclimation by combining lipidomic and transcriptomic approaches.</span><br><br><span>We show that <em>P. margaritifera</em> has certain capacities to adjust to long-term elevated temperatures that was thus far largely underestimated. Genetic variation across populations overlaps with gene expression and involves the mitochondrial respiration machinery, a central physiological process that contributes to species thermal sensitivity and their distribution ranges.</span><br><br><span>Our results present evidence for acclimation potential in <em>P. margaritifera</em> and urge for longer term studies to assess populations resilience in face of climate change.</span></p>
TPE-MI reveals proteome remodelling in response to pharmacological stimuli
<p>Dataset contains raw and preprocessed data for fluorescence and proteomic studies respectively. In each case, protein foldedness was probed using thiol reactivity. The raw mass spectrometry proteomics data have also been deposited to the ProteomeXchange Consortium via the PRIDE partner repository, with the dataset identifiers PXD033152.</p>
ATP binding facilitates target search of SWR1 chromatin remodeler by promoting one-dimensional diffusion on DNA
<p>One-dimensional (1D) target search is a well-characterized phenomenon for many DNA-binding proteins but is poorly understood for chromatin remodelers. Herein, we characterize the 1D scanning properties of SWR1, a conserved yeast chromatin remodeler that performs histone exchange on +1 nucleosomes adjacent to a nucleosome-depleted region (NDR) at gene promoters. We demonstrate that SWR1 has a kinetic binding preference for DNA of NDR length as opposed to gene-body linker length DNA. Using single and dual color single-particle tracking on DNA stretched with optical tweezers, we directly observe SWR1 diffusion on DNA. We found that various factors impact SWR1 scanning, including ATP which promotes diffusion through nucleotide binding rather than ATP hydrolysis. A DNA-binding subunit, Swc2, plays an important role in the overall diffusive behavior of the complex, as the subunit in isolation retains similar, although faster, scanning properties as the whole remodeler. ATP-bound SWR1 slides until it encounters a protein roadblock, of which we tested dCas9 and nucleosomes. The median diffusion coefficient, 0.024 μm2/s, in the regime of helical sliding, would mediate rapid encounter of NDR-flanking nucleosomes at length scales found in cellular chromatin.</p>
REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5-2. Cable grasping. Data related to a paper published in MDPI Electronics 2021
<p>The datasets contain data for the normalization of tactile voltages, the training dataset for the diameter classifier, and data recorded during the experiment related to the publication:</p> <p>A. Cirillo, G. Laudante, and S. Pirozzi, “Tactile sensor data interpretation for estimation of wire features,” Electronics, vol. 10, no. 12, art. 1458, June 2021. (DOI: 10.3390/electronics10121458)</p>
REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5-2. Cable grasping. Data related to a paper for the conference ICPS2022
<p>The datasets contain data related to the experiments presented in the publication:</p> <p>A. Cirillo, G. Laudante, and S. Pirozzi, “Wire Grasping by Using Proximity and Tactile Data,” 5th IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2022. (DOI: 10.1109/ICPS51978.2022.9816936)</p>
REMODEL. WP6. Sensory Systems and Mechatronic Tools. T6-2. Development and optimization of sensory system components. Data related to a paper published in MDPI Machines 2021
<p>The datasets contain the data related to the experiment presented in the publication:</p> <p>A. Cirillo, G. Laudante, and S. Pirozzi, “Proximity sensor for thin wire recognition and manipulation,” Machines, vol. 9, no. 9, art. 188, Sept. 2021. (DOI: 10.3390/machines9090188)</p>
REMODEL. WP6. Sensory Systems and Mechatronic Tools. T6-2. Development and optimization of sensory system components. Data related to IMM2021 publication
<p>The datasets contain data related to the experiments presented in the publication:</p> <p>Costanzo, M., Pirozzi, S., “Optical Force/Tactile Sensors for Robotic Applications” (2021) IEEE Instrumentation and Measurement Magazine, 24 (5), art. no. 9491003, pp. 28-35. DOI: 10.1109/MIM.2021.9491003</p>
REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5-2. Cable grasping. Data related to a paper published in IEEE Access 2021
<p>The datasets contain images for the training and testing of algorithms presented in the publication:</p> <p>Cirillo, P., Laudante, G., Pirozzi, S. “Vision-Based Robotic Solution for Wire Insertion with an Assigned Label Orientation” (2021) IEEE Access, 9, art. no. 9490630, pp. 102278-102289. DOI: 10.1109/ACCESS.2021.3098472</p>
REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5-2. Cable grasping. Data related to a paper published on RA-L (2022)
<p>The dataset contains data related to the following publication:</p> <p>Costanzo, M., De Maria, G., Natale, C., Russo, A., “Stability and Convergence Analysis of 3D Feature-Based Visual Servoing”, (2022) IEEE Robotics and Automation Letters, pp. 1-8. (DOI: 10.1109/LRA.2022.3211154)</p>
Data from: Dissecting gene activation and chromatin remodeling dynamics in single human cells undergoing reprogramming
<p>During cell fate transitions, cells remodel their transcriptome, chromatin, and epigenome; however, it has been difficult to determine the temporal dynamics and cause-effect relationship between these changes at the single-cell level. Here, we employ the heterokaryon-mediated reprogramming system as a single-cell model to dissect key temporal events during early stages of pluripotency conversion using super-resolution imaging. We reveal that, following heterokaryon formation, the somatic nucleus undergoes global chromatin decompaction and removal of repressive histone modifications H3K9me3 and H3K27me3 without acquisition of active modifications H3K4me3 and H3K9ac. The pluripotency gene OCT4 (POU5F1) shows nascent and mature RNA transcription within the first 24 h after cell fusion without requiring an initial open chromatin configuration at its locus. NANOG, conversely, has significant nascent RNA transcription only at 48 h after cell fusion but, strikingly, exhibits genomic reopening early on. These findings suggest that the temporal relationship between chromatin compaction and gene activation during cellular reprogramming is gene context dependent. </p>
Histone tail dynamics in partially disassembled nucleosomes during chromatin remodeling: Simulation dataset
<p>Dataset of molecular dynamics simulations of partially disassembled nucleosomes.</p> <p>- Input: Parameters and initial structures</p> <p>- Output: Trajectories</p> <p>NAMD 2.12 (multi-core with CUDA) was used for the simulations.</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.