Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,192
datasets available to search
ShareScore release 0.9.0
Dataset results
1,192 results for “multi-omics”
Association Analysis of Cardiovascular and Nervous System Diseases and Intestinal Microbiome Based on Multi-omics Big Data and Related Applications
ClinicalTrials.gov study NCT06099496. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Deciphering the Factors of Longevity Through a Multi-Omics Study in Long-Lived Individuals in Hong Kong
ClinicalTrials.gov study NCT07356700. IPD Sharing: NO. Countries: 1. Publications: 5.
Data from: Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney
Open the record for dataset details and reuse information.
Data for: Single-cell multi-omics in the medicinal plant Catharanthus roseus
Open the record for dataset details and reuse information.
Datasets used for Multi-omics integration using Deep Learning and other state-of-the-art regression models
<p>This repository link contains the LIHC files that were downloaded using TCGA Assembler 2 and used in the publication for benchmarking DL and other state-of-the-art regression models.</p> <p>The contents are as follows.</p> <p>Gene level CNA , filename= "<a href="https://zenodo.org/api/files/15943ba8-5f3d-4397-8eb2-98ed85693b79/LIHC__genome_wide_snp_6__GeneLevelCNA.txt">LIHC__genome_wide_snp_6__GeneLevelCNA.txt</a>"</p> <p>DNA Methylation data around 1500 bp around TSS (450K) , filename= "<a href="https://zenodo.org/api/files/15943ba8-5f3d-4397-8eb2-98ed85693b79/LIHC_Methylation450__SingleValue__TSS1500__Both.txt">LIHC_Methylation450__SingleValue__TSS1500__Both.tx</a>t"</p> <p>RNASeq data, filename= "<a href="https://zenodo.org/api/files/15943ba8-5f3d-4397-8eb2-98ed85693b79/LIHC_RNASeq__illuminahiseq_rnaseqv2__GeneExp.txt">LIHC_RNASeq__illuminahiseq_rnaseqv2__GeneExp.txt</a>"</p>
Data from: Multi-omics investigation reveals benzalkonium chloride disinfectants alter sterol and lipid homeostasis in the mouse neonatal brain
Lipids are critical for neurodevelopment; therefore, disruption of lipid homeostasis by environmental chemicals is expected to have detrimental effects on this process. Previously, we demonstrated that the benzalkonium chlorides (BACs), a class of commonly used disinfectants, alter cholesterol biosynthesis and lipid homeostasis in neuronal cell cultures in a manner dependent on their alkyl chain length. However, the ability of BACs to reach the neonatal brain and alter sterol and lipid homeostasis during neurodevelopment in vivo has not been characterized. Therefore, the goal of this study was to use targeted and untargeted mass spectrometry and transcriptomics to investigate the effect of BACs on sterol and lipid homeostasis, and to predict the mechanism of toxicity of BACs on neurodevelopmental processes. After maternal dietary exposure to 120 mg BAC/kg body weight/day, we quantified BAC levels in the mouse neonatal brain, demonstrating for the first time that BACs can cross the blood-placental barrier and enter the developing brain. Transcriptomic analysis of neonatal brains using RNA sequencing revealed alterations in canonical pathways related to cholesterol biosynthesis, liver X receptor-retinoid X receptor (LXR/RXR) signaling, and glutamate receptor signaling. Mass spectrometry analysis revealed decreases in total sterol levels and downregulation of triglycerides and diglycerides, which were consistent with the upregulation of genes involved in sterol biosynthesis and uptake as well as inhibition of LXR signaling. In conclusion, these findings demonstrate that BACs target sterol and lipid homeostasis and provide new insights for the possible mechanisms of action of BACs as developmental neurotoxicants.
Datasets: Integrative Multi-omics Profiling in Human Decedents Receiving Pig Heart Xenografts
Open the record for dataset details and reuse information.
Single-cell multi-omics analysis identifies context specific gene regulatory gates and mechanisms
<p>There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. <br>In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g., AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. <br>We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omic data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.</p>
Montipora capitata multi-omics dataset
<p><strong>Transcriptomic, proteomic, metabolomic and prokaryote microbiome 16S-rRNA amplicon data from <em>Montipora capitata</em></strong></p> <p><strong>Data S1.</strong> Processed proteomic data for <em>M. capitata</em> colony MC289 (n=2 per treatment/time point). Normalized abundance values and peptide statistics are shown for each protein identified. </p> <p><strong>Data S2.</strong> Processed metabolite data from the four <em>M. capitata</em> colonies (n=3 per treatment/time point/colony). Accumulation values (unnormalized) and peak counts, and compounds IDs are shown for each metabolite identified. </p> <p><strong>Data S3.</strong> Processed meta-transcriptome data for <em>M. capitata</em> colony MC289 (n=3 per treatment/time point). Read counts (unnormalized) for each of the predicted proteins in the <em>M. capitata</em> genome is shown for each of the samples. </p> <p><strong>Data S4.</strong> Processed microbiome 16S rRNA data from the four <em>M. capitata</em> colonies (n=3 per treatment/time point/colony). The (unnormalized) number of reads assigned to each ASV are shown for each of the samples.</p> <p><strong>Data S5.</strong> The (unnormalized) number of reads assigned to each of the ASV that survived filtering are shown for each of the samples.</p>
Supplementary material 1 from: Niehues A, de Visser C, Hagenbeek FA, Karu N, Kindt ASD, Kulkarni P, Pool R, Boomsma DI, van Dongen J, van Gool AJ, `t Hoen PAC (2022) A Multi-omics Data Analysis Workflow Packaged as a FAIR Digital Object. Research Ideas and Outcomes 8: e94042. https://doi.org/10.3897/rio.8.e94042
Members of the ACTION Consortium
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
Open the record for dataset details and reuse information.
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
Open the record for dataset details and reuse information.
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
Open the record for dataset details and reuse information.
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
<p>part aa of HMU1st</p>
Development of a Multi-omics Prediction Model for Immunotherapy Response in Triple-Negative Breast Cancer Subtypes
ClinicalTrials.gov study NCT06833723. IPD Sharing: NO. Countries: 1. Publications: 0.
Lung Cancer Multi-omics Digital Human Avatars for Integrating Precision Medicine Into Clinical Practice
ClinicalTrials.gov study NCT05802771. IPD Sharing: NO. Countries: 0. Publications: 1.
Leveraging Artificial Intelligence and Multi-Omics Data to Predict Opioid Addiction
ClinicalTrials.gov study NCT06540105. IPD Sharing: YES. Countries: 1. Publications: 0.
Construction and Evaluation of Tumor Immunotherapy and Organ Damage Early Warning System Based on Multi-omics
ClinicalTrials.gov study NCT07131007. IPD Sharing: NO. Countries: 0. Publications: 0.
Vitamin D Status and Multi-Omics Profiles in Primary Osteoporosis
ClinicalTrials.gov study NCT07296471. IPD Sharing: YES. Countries: 1. Publications: 0.
Clinical Translation Research on a Multi-omics Breast Cancer Distant Metastasis Prediction Model Empowered by Artificial Intelligence
ClinicalTrials.gov study NCT07252986. IPD Sharing: NO. Countries: 1. Publications: 0.
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.