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650 results for “multiomics”
Computational models of immunity for pertussis boost (CMI-PB) multiomics datasets used for the first challenge
<p><strong>Systems vaccinology is increasingly generating massive datasets to characterize the immune state of individuals before and after vaccination, which require data science to interpret them. These studies are used to draw conclusions on what drives different individuals to have different vaccine responses. </strong></p>
Multiomic single-cell analysis of the stem and progenitor cell compartment in chronic myeloid leukemia
<p>This repository contains CITE-seq data generated from healthy and CML stem and progenitor cells using the BD Rhapsody Single-Cell Analysis System. <br> </p> <p><strong>File descriptions</strong></p> <p>RSEC-adjusted UMI count files generated using the BD Rhapsody Targeted Analysis Pipeline (v. 1.10.1):</p> <ul> <li>CartridgeS1_RSEC_MolsPerCell.csv</li> <li>CartridgeS2_RSEC_MolsPerCell.csv</li> <li>CartridgeS3_RSEC_MolsPerCell.csv</li> <li>CartridgeS4_RSEC_MolsPerCell.csv</li> <li>CartridgeS5_RSEC_MolsPerCell.csv</li> </ul> <p>RSEC-adjusted UMI counts for cells remaining after cell quality filtering using SeqGeq software (genes expressed vs library size):</p> <ul> <li>CartridgeS1_RSEC_MolsPerCell_postQC.csv</li> <li>CartridgeS2_RSEC_MolsPerCell_postQC.csv</li> <li>CartridgeS3_RSEC_MolsPerCell_postQC.csv</li> <li>CartridgeS4_RSEC_MolsPerCell_postQC.csv</li> <li>CartridgeS5_RSEC_MolsPerCell_postQC.csv</li> </ul> <p>Sample tag (sample of origin) calls for each putative cell, output by the BD Rhapsody Targeted Analysis Pipeline (v. 1.10.1). The files also contain information on <em>BCR-ABL1</em> positivity for each individual cell, generated using our in-house optimized <em>BCR-ABL1 </em>expression detection method.</p> <ul> <li>CartridgeS1_Sample_Tag_Calls_with_BCR-ABL1.csv</li> <li>CartridgeS2_Sample_Tag_Calls_with_BCR-ABL1.csv</li> <li>CartridgeS3_Sample_Tag_Calls_with_BCR-ABL1.csv</li> <li>CartridgeS4_Sample_Tag_Calls_with_BCR-ABL1.csv</li> <li>CartridgeS5_Sample_Tag_Calls_with_BCR-ABL1.csv</li> </ul> <p>RDS file containing mRNA (RNA assay) and protein (Abseq assay) counts for all cells included in the analysis (from the RSEC-adjusted post QC UMI count files above). The sample of origin of each cell and information on <em>BCR-ABL1</em> positivity (from the sample tag call files above) are specified in the meta data.</p> <ul> <li>CITEseq_BCRABL1_postQC.rds</li> </ul>
Multiomic profiling of medulloblastoma reveals subtype-specific targetable alterations at the proteome and N-glycan level [RNA-Seq]
GEO Series GSE243795. Homo sapiens. 21 samples. Type: Expression profiling by high throughput sequencing.
Multiomics profiling of HIV-infected cells
GEO Series GSE184345. Homo sapiens. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Unveiling E2F4, TEAD1 and AP-1 as regulatory transcription factors of the replicative senescence program by multiomics analysis
GEO Series GSE185416. Mus musculus. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Single Cell Multiomics Across Nine Mammals Reveals Cell Type Specific Regulatory Conservation in the Brain [ATAC-seq]
GEO Series GSE303046. Felis catus; Equus caballus; Bos taurus; Oryctolagus cuniculus; Rattus norvegicus. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Dataset related to article "Single-Cell and Single-Nucleus Multiomics Reveal Cardiac Endothelial Cell Heterogeneity and Novel Pathological Processes in Pressure Overload-Induced Hypertrophy"
<p>This record contains raw data related to article “<em><span>Single-Cell and Single-Nucleus Multiomics Reveal Cardiac Endothelial Cell Heterogeneity and Novel Pathological Processes in Pressure Overload-Induced Hypertrophy</span></em>"</p> <p>Endothelial cells, the most prevalent cellular population in the heart, line the internal walls of coronary and lymphatic vessels and form the endocardium and cardiac valves. The heterogeneity of cardiac endothelium poses a challenge in fully understanding endothelial states. However, to better grasp the plasticity of endothelial cell phenotypes, it's crucial to distinguish the varied responses of endothelial populations to physiological and pathological stimuli. Endothelial dysfunction plays a significant role in the development and progression of heart failure, a condition impacting millions globally. For the heart to sustain pressure-overload-induced remodelling, endothelial cells must proliferate and generate new blood vessels. When cardiomyocyte hypertrophy and angiogenesis become uncoupled, it leads to decompensated heart failure. Despite this, the molecular mechanisms governing cardiac vascularization during pathological hypertrophy remain unclear.</p> <p>Transcriptional phenotyping methods have been widely used to capture endothelial cell plasticity and heterogeneity. However, only single-cell profiling has effectively resolved distinct phenotypes. While single-cell RNA sequencing can create detailed cellular maps and cell-to-cell communication networks, it does not fully elucidate how gene regulatory programs are established or how cell states, functions, and responses are specified. To address these gaps, multimodal omics approaches have been developed, allowing for simultaneous profiling of chromatin accessibility and gene expression within the same cell.</p> <p>In our study, we aimed to characterize the transcriptional and epigenetic profiles of cardiac endothelial cells in a mouse model of pressure-overload-induced hypertrophy using multiomics bioinformatic approaches at single-cell resolution. This innovative technology enabled us to uncover the regulatory mechanisms that drive endothelial cell sub-population specifications following banding. Our resulting atlas is intended to serve as a valuable reference and resource for future studies and the development of therapeutic strategies targeting endothelial cells in cardiovascular diseases.</p>
SYSTACT CyTOF dataset associated with Multiomics approaches disclose very-early molecular and cellular switches during insect-venom allergen-specific immunotherapy: an observational study
<p>This dataset contains all the raw mass cytometry (CyTOF) fcs files associated with Pogorelov et al. 'Multiomics approaches disclose very-early molecular and cellular switches during insect-venom allergen-specific immunotherapy: an observational study', <em>Nature Communications</em>, 2024, in Press. The dataset contains all the raw fcs files generated by CyTOF analysis of 199 time-series samples including ~200 million of deeply characterized immune cells, either from patients with insect-venom allergy or patients with pollen allergy following allergen-specific immunotherapy (AIT) or from healthy controls (HC), who did not receive any AIT or other immunotherapy. As specified in the manuscript, we sampled different individuals up to seven times (0h, 8h, 24h, Day 7, Week 2, Week 6 and Week 12) over a three-month period. All the samples from insect-venom allergy patients are stored in the zipper folder called "Venom" and all the samples from pollen allergy patients are in the zipper folder called "Pollen" while all the samples from HC are placed in the zipper folder called "Control". We also provide the following annotation files (metadata) to help the readers to better understand, validate and re-analyze our immunological dataset.</p> <p>1, In addition to the Supplementary Tables in our manuscript, we also attached one Word table file mapping the 36 specific metal isotypes and the specific Ab target markers.</p> <p>2, The Excel file with the list of 199 samples, showing the correct group and time point information (of note, the manually input sample information in the file name might be wrong for some samples). The correct time point information is in the column "Timepoint Detail" and "Timepoint Final".</p> <p>Associated with these raw fcs files, our processed results of all the CyTOF data are deposited in another webpage. Our large-scale interactive interlinked immunological Data resource (i3Dare) allows investigators to more effectively explore or reuse our immunological datasets of each immune subset from each participant at each time point (please refer to <a href="https://public.tableau.com/app/profile/lihpublicdata/viz/i3Dare_SYSTACT_Database/SYSTACTHome">https://public.tableau.com/app/profile/lihpublicdata/viz/i3Dare_SYSTACT_Database/SYSTACTHome</a>). OF note, to further protect privacy, the pseudoIDs within groups in i3Dare were re-labelled again. </p> <p><strong>Please read the accompanying information/metadata files, our main manuscript and Supplementary Information, our processed data on the i3Dare webpage and the published Source Data together with the raw fcs files in order to reproduce our published results.</strong></p>
MultiOMIC approaches for understanding physiopathology of SPG11
<p>Raw data from the research article "MultiOMIC approaches for understanding physiopathology of SPG11"</p>
MultiOMIC approaches for understanding physiopathology of SPG11
<p>Raw data from the research article "MultiOMIC approaches for understanding physiopathology of SPG11"</p>
ScienceDex guides
Understand access before you commit
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.