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.
1,604
datasets available to search
ShareScore release 0.9.0
Dataset results
1,604 results for “omics”
Multi-omics analysis reveals the glycolipid metabolism response mechanism in the liver of Genetically Improved Farmed Tilapia (GIFT, Oreochromis niloticus) under hypoxia stress
<p><span><b>Background: </b>Dissolved oxygen (DO) in the water is a vital abiotic factor in aquatic animal farming. A hypoxic environment affects the growth, metabolism, and immune system of fish. Glycolipid metabolism is a vital energy pathway under acute hypoxic stress, and it plays a significant role in the adaptation of fish to stressful environments. In this study, we used multi-omics integrative analyses to explore the mechanisms of hypoxia adaptation in Genetically Improved Farmed Tilapia (GIFT, <i>Oreochromis niloticus</i>). </span></p> <p><span><b>Results:</b><b> </b>The 96 h median lethal hypoxia (96h-LH50) for GIFT was determined by linear interpolation. We established control (DO: 5 mg/L) groups (CG) and hypoxic stress (96h-LH50) groups (HG) and extracted liver tissues for high-throughput transcriptome and metabolome sequencing. A total of 581 differentially expressed (DE) genes and 1250 DE metabolites were detected between CG and HG, and were annotated using tools at the KEGG database. We verified the transcript levels of eight DE genes by quantitative real-time PCR.</span></p> <p><span><b>Conclusions: </b>Analyses of essential glycolipid metabolism pathways of GIFT under hypoxia stress showed that, after 96 h of hypoxia stress, lipid metabolism became the primary metabolic pathway in GIFT. Our findings reveal the changes in metabolites and gene expression that occur under hypoxia stress, and shed light on the regulatory pathways that function under such conditions. Ultimately, this information will be useful to devise strategies to decrease the damage caused by hypoxia stress in farmed fish.</span></p>
Data from: Multi-omics analyses on rheumatoid arthritis in CD4+ T cells
<p><strong>Objective</strong>: CD4+ T cells have been suggested as the most disease-relevant cell type in rheumatoid arthritis (RA) in which RA-risk non-coding variants exhibit allele-specific effects on regulation of RA-driving genes. This study aimed to understand RA-specific signatures in CD4+ T cells using multi-omics data, interpreting inter-omics relationships in shaping the RA transcriptomic landscape.</p> <p><span><span><span><b>Methods</b>: We profiled genome-wide variants, gene expression, and DNA methylation in CD4<sup>+</sup> T cells from 82 RA patients and 40 healthy controls using high-throughput technologies. We investigated differentially expressed genes (DEGs) and differentially methylated regions (DMRs) in RA and localized quantitative trait loci (QTLs) for expression and methylation. We then integrated these based on individual-level correlations to inspect DEG-regulating sources and investigated the potential regulatory roles of RA-risk variants by a partitioned-heritability enrichment analysis with RA genome-wide association summary statistics.</span></span></span></p> <p><span><span><span><b>Results</b>: A large number of RA-specific DEGs were identified (n=2,575), highlighting T-cell differentiation and activation pathways. RA-specific DMRs, preferentially located in T-cell regulatory regions, were correlated with the expression levels of 548 DEGs mostly in the same topologically associating domains. In addition, expressional variances in 771 and 83 DEGs were partially explained by expression QTLs for DEGs and methylation QTLs for DEG-correlated DMRs, respectively. A large number of RA variants were moderately to strongly correlated with meQTLs. DEG-correlated DMRs, enriched with meQTLs, had strongly enriched heritability of RA.</span></span></span></p> <p><span><span><span><b>Conclusion</b>: Our findings revealed that the methylomic changes, driven by RA heritability-explaining variants, shape the differential expression of a substantial fraction of DEGs in CD4<sup>+</sup> T cells in RA patients, reinforcing the importance of a multi-dimensional approach in disease-relevant tissues.</span></span></span></p>
Reproducible in-silico omics analyses - GSE37703: Differential analysis of HOXA1 in adult cells dataset
<p>GSE37703: Differential analysis of HOXA1 in adult cells at isoform resolution by RNA-Seq’ for quantification by Kallisto and differential abundance with Sleuth dataset used for the "Reproducible in-silico omics analyses across clouds and clusters" paper.</p>
Reproducible in-silico omics analyses - Supplementary Figure 3
<p>Supplementary Figure 3. Interleaved output of two RAxML Phylogenetic Trees of the same sequences estimated on Mac OSX (blue) and Amazon Linux (red). Differences in the branch lengths of resulting trees are shown in color. No such differences were observed when running a Nextflow dockerized version of the same command.</p>
Reproducible in-silico omics analyses - Main figure
<p>Figure 1: Nextflow produces stable analysis across different platforms. (a) Leishmania infantum clone JPCM5 genome annotation was predicted using a native and a dockerized (Debian Linux) version of the Companion eukaryotic annotation pipeline. The native and dockerized versions were run on Mac OSX and Amazon Linux platforms. The Venn diagram shows the existence of small, but significant discrepancies when comparing the genomic coordinates of predicted coding genes and non-coding RNAs, with some of these disparities including entire genes. (b) Results were deterministic on each platform, and totally identical readouts were measured when deploying the dockerized version. (c) A similar comparison carried out on a Kallisto/Sleuth pipeline when looking for differentially expressed genes (q-value <0.01) in an RNA-seq experiment collected from human lung fibroblasts reveals a comparable fluctuation between the Mac OSX and the Amazon Linux platform. Similarly, in this case both platforms produce identical readouts when deploying the dockerized version of the pipeline.<br> </p>
Reproducible in-silico omics analyses - Supplementary Figure 2
<p>Supplementary Figure 2. Kallisto Nextflow pipeline. The native Kallisto pipeline is converted to Nextflow and composed of three processes. The first two processes call Kallisto to index the transcriptome and then pseudo-map for RNA-seq quantification, and the third one for Sleuth to perform differential expression analysis.</p>
Multi-omics reveals the attenuation of metabolic cardiomyopathy in mice by extracts from Clausena0 lansium (Lour.) by transiting gastrointestinal microbiota to an alternative homeostasis
<p>The raw data for MS "<strong>Multi-omics reveals the attenuation of metabolic cardiomyopathy in mice by extracts from </strong><i><strong>Clausena0 lansium</strong></i><strong> (Lour.) by transiting gastrointestinal microbiota to an alternative homeostasis".</strong></p>
Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data: intermediate results
<p>This directory contains all intermediate results generated in the "Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data" for reproducibility purpose. Associated scripts are available at https://github.com/JiayuSuPKU/Smoother_paper. Raw and processed data can be downloaded at https://zenodo.org/records/10223862.</p><p>Note that some intermediate results and figures in the notebooks may not be exactly the same as those in the paper due to the randomness in some analysis steps, but should be very close.</p>
Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data: raw and processed data files
<p>This directory contains all spatial and single-cell omics datasets analyzed in the "Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data". Associated scripts are available at https://github.com/JiayuSuPKU/Smoother_paper. See https://github.com/JiayuSuPKU/Smoother_paper/blob/main/data/README.md for details on folder structure and data sources.</p>
Multi-omics analysis of innate and adaptive responses to BCG vaccination reveals epigenetic cell states that predict trained immunity
<p>This repository contains personal immune profiles of 323 healthy individuals (300BCG) subjected to Bacillus Calmette-Guérin (BCG) with blood samples collected immediately before (day 0), and 14 and 90 days after the vaccination. The personal immune profiles comprise:</p> <ul> <li>immune cell concentrations measured with flow cytometry and a hematology analyzer</li> <li>plasma concentrations of 73 circulating inflammatory markers</li> <li>30 measurements of cytokine and lactate production capacity of peripheral blood mononuclear cells (PBMCs) in response to four microbial stimuli (Candida albicans, Escherichia coli lipopolysaccharide [LPS], Staphylococcus aureus, Mycobacterium tuberculosis).</li> </ul> <p>Visit <a href="http://300BCG.bocklab.org/">http://300BCG.bocklab.org/</a> to learn more.</p>
Pathformer: a biological pathway informed Transformer for disease diagnosis and prognosis using multi-omics data
<p>Integrating multi-omics data offers a more comprehensive view of gene regulation, which would be helpful in achieving accurate diagnosis of diseases like cancer. To improve the accuracy of disease diagnosis and prognosis, we developed Pathformer, a multi-omics integration method for both tissue and liquid biopsy data. We implemented Pathformer's network architecture using the “PyTorch” package in Python v3.6.9, and our codes can be found in the GitHub repository (https://github.com/lulab/Pathformer). This repository contains preprocessed TCGA dataset data, preprocessedliquid biopsy dataset data, result of Pathformer and comparison_methods, mentioned in GitHub project and article.</p>
Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features - Validation Data Sets
<p>Two public validation cohorts were utilized in the MT-Pilot study, the Cancer Genome Atlas (TCGA) and cohort-1 Johns Hopkins University (JHU). These datasets included DNA, RNA, clinical data, and tissue protein analytes analyzed for survival outcome prediction using AI/Machine Learning modeling. </p>
A multi-omics systems vaccinology resource to develop and test computational models of immunity: 1st challenge dataset and submissions
<p>The goal of the CMI-PB prediction contest is to foster a collaborative research community that can collectively tackle challenges and accelerates scientific progress beyond the capabilities of individual researchers or groups. The CMI-PB consortium has curated multi-source data from multiple individuals, encompassing Ab titers (around four antibodies/features), cell frequency (approximately 20 cell types/features), gene expression (roughly 50,000 RNA transcripts/features), and plasma proteomics (around 50 proteins/features). The challenge requires integrating these diverse data sources to predict different immune responses or tasks. Specifically, you will utilize multi-source data from several individuals on day 0 (baseline) to predict specific immune responses at later time points (1, 3, 7, and 14 days post-booster vaccination).</p> <p>The first CMI-PB challenge, which is an internal challenge, was conducted using datasets from 2020 (train) and 2021 (test). In the following sections, we provide detailed information on the datasets, challenge tasks, submission format, descriptions, and access to the necessary data files for participants to develop their models and make predictions.</p> <p><br><strong>A) Multiomics CMI-PB dataset:</strong></p> <p>We propose a study design that enables a systems-level understanding of the immune responses through computational modeling. Our cohort comprises aP vs. wP infancy-primed subjects boosted with Tdap. We recruit individuals born before 1995 (wP) and after 1996 (aP), collect baseline plasma and blood samples, and then at 1, 3, 7 and 14 days post booster vaccination.</p> <p>With the obtained samples processed, we generated omics data by:</p> <ul> <li> <p>Bulk PBMCs transcriptomics,</p> </li> <li> <p>Plasma proteomics using Olink, which provides a quantitative readout of cytokines, chemokines, and other immune factors,</p> </li> <li> <p>Cell frequency in PBMCs using flow cytometry,</p> </li> <li> <p>Tdap-specific antibodies levels</p> </li> </ul> <p><strong>B) List of tasks can be accessed using the “List of tasks for challenge 1.docx” file, and submissions need to submit in provided format here: “submission template challenge 1.tsv”</strong></p> <p><strong>C) Datasets for model building and making predictions:</strong></p> <p> Data files are divided into two categories: 1) raw dataset and 2) computable matrices.</p> <ol> <li> <p><strong>Raw dataset: </strong>This raw-most dataset is divided into training and test datasets. </p> </li> <li> <p><strong>Computable matrices: </strong>There are three different types of computable matrices. a) Full: These files are generated by dividing raw files into sub-files specific to planned days specific to vaccination. b) harmonized: These are generated by preserving only overlapping features between train and test datasets. b) imputed: MICE imputation is performed to impute missing values in the dataset.</p> </li> </ol> <p><strong>D) Submission evaluation</strong></p> <p>This folder contains all submitted models with ranking files and code for evaluating these models.</p> <p><strong>To learn more about the CMI-PB prediction challenge, visit our website at www.cmi-pb.org.</strong></p>
Multi-omics for Understanding Climate Change (MUCC) database v2.0.0
<p>This is the Multi-omics for Understanding Climate Change (MUCC database) version 2.0.0. This current version is based on amplicon and metagenomic sequencing of Old Woman Creek (OWC), Prairie Pothole Region(PPR7 and PPR8), Jean Lafitte National Historical Park and Preserve (JLA), AmeriFlux site US-LA2 (LA2), Stordalen Mire (STM-fen and STM-bog), AmeriFlux site-ID US-Twt (TWI), and Peatland Responses Under Changing Environments (SPRUCE) and wetland soils. Additionally, this includes metatranscriptome sequencing from OWC. In the future, this will be expanded to include more data from these sites and from additional wetlands.</p> <p>OWC, PPR, JLA and LA2 data are deposited in NCBI Bioproject PRJNA1007388</p> <p>Stordalen Mire MAGs are deposited in BioProject PRJNA386538</p> <p>AmeriFlux site-ID US-Twt are deposited in SRA SRP003022, SRA SRP010671, SRP010730, SRP010738, SRP010741, SRP010747, SRP010748, SRP010751, SRP010862, SRP010870, and SRP011309. </p> <p>SPRUCE data are deposited in PRJNA638786 and PRJNA638601</p> <p> </p> <p>Files and datasets included here: </p> <ol> <li><strong>16S.zip </strong>16S amplicon sequencing data and site metadata for 1,112 samples (fastq files)</li> <li><strong>MQ_HQ_MAGs.zip </strong>Database of 4745 Medium and High Quality MAGs (fasta files)</li> <li><strong>MUCC_v2.0.0_HQMQ_genes.faa.zip </strong>MAG amino acid gene sequences derived from DRAM gene calls (fasta file)</li> <li><strong>MUCC_v2.0.0_HQMQ_annotations.tsv </strong>MAG DRAM ANNOTATIONS</li> <li><strong><strong>owc_metat_table_methanoregula_genes.csv </strong></strong>Metatranscriptomic expression per genes in <em>Methanoregula</em> across 133 metatranscriptomes (csv table)</li> <li><strong>gtdbtk.ar53.decorated.tree </strong>newick file for GTDB de novo work flow <em>Methanoregula</em> MAG tree</li> <li><strong>Newick_gene_trees.zip </strong>Trees used in blast identification of methylotrophic gene homologs to curate MR for methylotrophy</li> <li><strong>fasta_reference_genes.zip </strong>FASTA reference files of genes used as BLAST query to mine Methanoregula MAGs for genes involved in detoxification of reactive oxygen species (ROS) and methanogenic metabolism of methylated compounds</li> <li><strong>protpipeliner.py </strong>Python script is a modification of protpipeliner.rb for building RAXML trees</li> <li><strong>classification_w_outgroup.txt </strong>Taxonomy and corresponding MAG ID for <em>Methanregula </em>used in the tree (Figure 5B)</li> <li><strong>Methanoregula_metabolism_summary.xlsx </strong>The DRAM annotations of the <em>Methanoregula </em>MAGs from MUCC, GTDB, and JGI</li> <li><strong>Methanoregula_physiology.txt </strong>Curation of <em>Methanoregula</em> MAGS for physliogical functions of interest</li> <li><strong>Methanoregula_MAGs_list.txt </strong>Comprehensive list of all <em>Methanoregula </em>MAGs used and what database they were sourced from</li> <li><strong>Methanoregula_MAGs_DB.zip </strong>Database of 108 <em>Methanregula</em> MAGs</li> </ol>
Data for: Molecular mechanisms behind safranal's toxicity to liver cancer cells from dual omics
<p>The spice saffron (<em>Crocus sativus</em>) has anticancer activity in several human tissues, but the molecular mechanisms underlying potential therapeutic effects are poorly understood. We investigated the impact of safranal, a small molecule secondary metabolite from saffron, on the HCC cell line HEP-G2 using untargeted metabolomics (HPLC-MS) and transcriptomics (RNAseq). Increases in glutathione disulfide and other biomarkers for oxidative damage contrasted with lower levels of the antioxidants biliverdin IX (139-fold decrease, p=5.3E-5), the ubiquinol precursor 3-4-dihydroxy-5-all-trans-decaprenylbenzoate (3-fold decrease, p=1.9E-5), and resolvin E1 (-3,282-fold decrease, p=4E-5), which indicates sensitization to reactive oxygen species. We observed a significant increase in intracellular hypoxanthine (538-fold increase, p=7.7E-6) that may be primarily responsible for oxidative damage in HCC after safranal treatment. The accumulation of free fatty acids and other biomarkers, such as S-methyl-5'-thioadenosine, are consistent with safranal-induced mitochondrial de-uncoupling and explain the sharp increase in hypoxanthine we observed. Overall, the dual omics datasets describe routes to widespread protein destabilization and DNA damage from safranal-induced oxidative stress in HCC cells.</p>
Benchmark Multi-Omics Datasets for Methods Comparison
<p><strong>Pathway Multi-Omics Simulated Data</strong></p> <p>These are synthetic variations of the TCGA COADREAD data set (original data available at <a href="http://linkedomics.org/data_download/TCGA-COADREAD/">http://linkedomics.org/data_download/TCGA-COADREAD/</a>). This data set is used as a comprehensive benchmark data set to compare multi-omics tools in the manuscript "pathwayMultiomics: An R package for efficient integrative analysis of multi-omics datasets with matched or un-matched samples".</p> <p>There are 100 sets (stored as 100 sub-folders, the first 50 in "pt1" and the second 50 in "pt2") of random modifications to centred and scaled copy number, gene expression, and proteomics data saved as compressed data files for the R programming language. These data sets are stored in subfolders labelled "sim001", "sim002", ..., "sim100". Each folder contains the following contents: 1) "indicatorMatricesXXX_ls.RDS" is a list of simple triplet matrices showing which genes (in which pathways) and which samples received the synthetic treatment (where XXX is the simulation run label: 001, 002, ...), (2) "CNV_partitionA_deltaB.RDS" is the synthetically modified copy number variation data (where A represents the proportion of genes in each gene set to receive the synthetic treatment [partition 1 is 20%, 2 is 40%, 3 is 60% and 4 is 80%] and B is the signal strength in units of standard deviations), (3) "RNAseq_partitionA_deltaB.RDS" is the synthetically modified gene expression data (same parameter legend as CNV), and (4) "Prot_partitionA_deltaB.RDS" is the synthetically modified protein expression data (same parameter legend as CNV).</p> <p> </p> <p><strong>Supplemental Files</strong></p> <p>The file "cluster_pathway_collection_20201117.gmt" is the collection of gene sets used for the simulation study in Gene Matrix Transpose format. Scripts to create and analyze these data sets available at: <a href="https://github.com/TransBioInfoLab/pathwayMultiomics_manuscript_supplement">https://github.com/TransBioInfoLab/pathwayMultiomics_manuscript_supplement</a></p> <p> </p>
GeoMx DSP dataset of "Integrated multi-omics reveals cellular and molecular interactions governing the invasive niche of basal cell carcinoma"
<p>This data set is linked to the paper "Integrated multi-omics reveals cellular and molecular interactions governing the invasive niche of basal cell carcinoma". It contains datasets and images from GeoMx DSP analysis. </p>
Data and code for "Multi-omics Reveals Microbiome, Host Gene Expression, and Immune Landscape in Gastric Carcinogenesis" by Park et al., iScience 2022
<p>This repository is a part of the supplementary document in Park et al., "Multi-omics Reveals Microbiome, Host Gene Expression, and Immune Landscape in Gastric Carcinogenesis" published in iScience 2022.</p> <p>Abstract: To date, there has been no multi-omic analysis characterizing the intricate relationships between the intragastric microbiome and gastric mucosal gene expression in gastric carcinogenesis. Using multi-omic approaches, we provide a comprehensive view of the connections between the microbiome and host gene expression in distinct stages of gastric carcinogenesis (i.e., healthy, gastritis, cancer). We uncover associations specific to disease states. For example, uniquely in gastritis, Helicobacteraceae is highly correlated with the expression of <em>FAM3D</em>, which has been previously implicated in gastrointestinal inflammation. Additionally, in gastric cancer but not in adjacent gastritis, Lachnospiraceae is highly correlated with the expression of <em>UBD</em>, which regulates mitosis and cell cycle time. Furthermore, lower abundances of B cells in gastric cancer compared to gastritis may suggest a previously unidentified immune evasion process in gastric carcinogenesis. Our integrative analysis provides the most comprehensive description of microbial, host transcriptomic, and immune cell factors of the gastric carcinogenesis pathway.</p>
MIntO: a Modular and Scalable Pipeline for Microbiome Metagenomic and Metatranscriptomic Meta-omics Data Integration
<p>To illustrate the use of MIntO, a set of 91 human fecal metagenomes from the Inflammatory Bowel Disease Multi’omics Database was selected (IBDMDB). We selected six participants diagnosed as non-IBD (P6018 (nIBD1), M2072 (nIBD2)); Crohn’s disease (H4006 (CD1) and H4020 (CD2)); and ulcerative colitis (H4019 (UC1) and H4035 (UC2)) that were followed for one year each. </p> <p>Here, we present the results from the <em>genome-based assembly-dependent</em> mode, where we used 91 metagenomic high-quality reads<strong> </strong>to recover 163 high-quality MAGs, which constituted a set of non-redundant genomes.</p>
Raw Data - Part 2 : Spatial multi-omic map of human myocardial infarction
<p>We provide here the raw data of snATAC-seq and snRNA-seq for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022</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.