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1,192 results for “multi-omics”
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>
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>
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>
Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data
<p>We proposed DCCA for accurately dissecting the cellular heterogeneity on joint-profiling multi-omics data from the same individual cell by transferring representation between each other.</p>
Raw Data - Part 4 : Spatial multi-omic map of human myocardial infarction ---- Raw image
<p>We provide here the raw image for the visium data for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022</p>
Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma
<p><span>Glioblastomas are malignant tumors of the central nervous system hallmarked by subclonal diversity and dynamic adaptation amid developmental hierarchies </span><span>(Couturier et al., 2020; Neftel et al., 2019; Richards et al., 2021)</span><span>. The source of the dynamic reorganization within the spatial context of these tumors remains elusive. Here, we characterized glioblastomas in-depth by spatially resolved transcriptomics, metabolomics, and proteomics. By </span><span>deciphering regionally shared transcriptional programs across patients, </span><span>we infer that glioblastoma is organized by spatial segregation of lineage states and adapt to inflammatory and/or metabolic stimuli, </span><span>reminiscent </span><span>of the reactive transformation in</span> <span>mature astrocytes. Integration of metabolic imaging and imaging mass cytometry uncovered locoregional tumor-host interdependence, resulting in spatially exclusive adaptive transcriptional programs. Inferring copy-number alterations emphasizes a spatially cohesive organization of subclones associated with reactive transcriptional programs, confirming that environmental stress gives rise to selection pressure. A model of glioblastoma stem cells implanted into human and rodent neocortical tissue mimicking various environments confirmed that transcriptional states originate from dynamic adaptation to various environments.</span></p>
Multi-omic brain and behavioral correlates of cell-free fetal DNA methylation in macaque maternal obesity models (NMR datasets, maternal plasma and infant brain)
<p>Maternal obesity during pregnancy is associated with neurodevelopmental disorder (NDD) risk. We utilized integrative multi-omics to examine maternal obesity effects on offspring neurodevelopment in rhesus macaques by comparison to lean controls and two interventions. Differentially methylated regions (DMRs) from longitudinal maternal blood-derived cell-free fetal DNA (cffDNA) significantly overlapped with DMRs from infant brain. The DMRs were enriched for neurodevelopmental functions, methylation-sensitive developmental transcription factor motifs, and human NDD DMRs identified from brain and placenta. Brain and cffDNA methylation levels from a large region overlapping mir-663 correlated with maternal obesity, metabolic and immune markers, and infant behavior. A DUX4 hippocampal co-methylation network correlated with maternal obesity, infant behavior, infant hippocampal lipidomic and metabolomic profiles, and maternal blood measurements of DUX4 cffDNA methylation, cytokines, and metabolites. Ultimately, maternal obesity altered infant brain and behavior, and these differences were detectable in pregnancy through integrative analyses of cffDNA methylation with immune and metabolic factors. </p>
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
<p>This repository (and several sub-repositories) contains the data for the manuscript "His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models"</p> <p>The current repository contains the de-identified metadata of WSIs. Additionally, it contains Supplementary Data S1-S7.</p> <p>Due to the large size of WSIs, the archives have been divided into several parts to satisfy the size limit of Zenodo.</p> <p>HMU-C dataset:</p> <p>part a: <a href="../doi/10.5281/zenodo.12636965">https://zenodo.org/doi/10.5281/zenodo.12636965</a></p> <p>part b: <a href="../doi/10.5281/zenodo.12705912">https://zenodo.org/doi/10.5281/zenodo.12705912</a></p> <p>HMU-1st dataset:</p> <p>part a: <a href="../doi/10.5281/zenodo.12710399">https://zenodo.org/doi/10.5281/zenodo.12710399</a></p> <p>part b: <a href="../doi/10.5281/zenodo.12723825">https://zenodo.org/doi/10.5281/zenodo.12723825</a></p> <p>part c: <a href="../doi/10.5281/zenodo.12724507">https://zenodo.org/doi/10.5281/zenodo.12724507</a></p> <p>part d: <a href="../doi/10.5281/zenodo.12726785">https://zenodo.org/doi/10.5281/zenodo.12726785</a></p> <p>Please fully download all the parts and concatenate them before extraction.</p> <p>Supplementary Data S1-S7:</p> <p><a href="https://zenodo.org/doi/10.5281/zenodo.16763510">https://zenodo.org/doi/10.5281/zenodo.16763510</a></p>
Multi-omics analysis reveals regime shifts in the gastrointestinal ecosystem in chickens following anticoccidial vaccination and Eimeria tenella challenge
<p>A multi-omics study integrating gut microbiota and host metabolome to investigate the gastrointestinal health markers in broiler chickens (Cobb500) from an anti-coccidiosis vaccine trial.</p>
MangroveDB: A comprehensive online database for mangroves based on multi-omics data
<p><span>Mangroves are dominant flora of intertidal zones along tropical and subtropical coastline around the world that offer important ecological and economic value. Recently, the genomes of mangroves have been decoded, and massive omics data were generated and deposited in the public databases. Reanalysis of multi-omics data can provide new biological insights excluded in the original studies. However, the requirements for computational resource and lack of bioinformatics skill for experimental researchers limit the effective use of the original data. To fill this gap, we uniformly processed 942 transcriptome data, 386 whole-genome sequencing data, and provided 13 reference genomes and 40 reference transcriptomes for 53 mangroves. Finally, we built an interactive web-based database platform MangroveDB (https://github.com/Jasonxu0109/MangroveDB), which was designed to provide comprehensive gene expression datasets to </span><span>facilitate their exploration</span><span> and equipped with several online analysis tools, including principal components analysis, differential gene expression analysis, tissue-specific gene expression analysis, GO and KEGG enrichment analysis. MangroveDB not only provides query functions about genes annotation, but also supports some useful visualization functions for analysis results, such as volcano plot, heatmap, dotplot, PCA plot, bubble plot, population structure <em>etc</em>. In conclusion, MangroveDB is a valuable resource for the mangroves research community to efficiently use the massive public omics datasets.</span></p>
Pre-Processed Cancer Multi-Omic Data from TCGA and Synthetic Data
<p><strong>ABSTRACT </strong></p> <p>It contains the data of four omic profiles (CNV, mRNA, miRNA, and protein) obtained for BRCA, LGG, and LUAD obtained from the TCGA project. </p> <p>In addition, we provide synthetic data for a mixture of isotropic distributions.</p> <p><strong>Instructions: </strong></p> <p>Cancer data are identified by cancer type (LGG: low-grade glioma, BRCA: breast cancer, and LUAD: lung cancer). The data are scaled by using the minima and maxima of each column so that the values are between 0 and 1. In these files, the columns are the features and the rows correspond to the patients.</p> <p>The summary data contains only the numerical values. The columns are the features and the rows are the observations.</p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON"</p> <p>https://cancer.ubrite.org/hackathon-2021/</p> <p><strong>Acknowledgements</strong></p> <p>Diego Salazar, June 20, 2021, "Pre-processed Cancer multi-omic data from TCGA and synthetic data", IEEE Dataport, doi: https://dx.doi.org/10.21227/pjb8-d090.</p> <p>https://ieee-dataport.org/documents/pre-processed-cancer-multi-omic-data-tcga-and-synthetic-data</p> <p><strong>U-BRITE last update date:</strong> 07/21/2021</p>
Data deposition of the article 'Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence'
<p>Data deposition of the article 'Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence'</p>
Multi-Omic Approach Associates Blood Methylome with Bronchodilator Drug Response in Pediatric Asthma: Summary statistics
<p>We conducted an epigenome-wide association study of bronchodilator drug response (BDR) in a discovery and validation design. The discovery phase was focused on 221 African American children with asthma. The association between DNA methylation and BDR was conducted using the limma package correcting for age, sex, ancestry, and tissue heterogeneity. Summary statistics include the output from toptable limma function and CpG annotation (based on Illumina EPIC Manifest file v 1.0 B4) organized in the following columns:</p> <ul> <li>Probe: Probe ID</li> <li>Chr: chromosome</li> <li>Pos: genomic position based on GRCh37/hg19</li> <li>Gene: Gene annotation based on Illumina EPIC Manifest file v 1.0 B4</li> <li>logFC: estimate of the log2-fold-change corresponding to the effect or contrast</li> <li>SE: standard error</li> <li>AveExpr: average log2-expression for the probe over all arrays and channels</li> <li>t: moderated t-statistic</li> <li>P.value: raw p-value</li> <li>FDR: adjusted p-value by false discovery rate</li> <li>B: log-odds that the gene is differentially expressed</li> <li>Problem: Flagged potentially problematic probes</li> </ul>
Montipora capitata multi-omics supplemental tables
<p>Supplemental tables for <em>Montipora capitata</em> multi-omics study.</p> <p><strong>Table S1.</strong> Read statistics for the microbiome 16S rRNA data from the four <em>M. capitata</em> colonies (n=3 per treatment/time point/colony).</p> <p><strong>Table S2.</strong> Protein and transcript FC values for genes identified in proteomic data. The 138 stress-response related genes are also highlighted.</p> <p><strong>Table S3.</strong> Major KEGG pathways used to filter proteins identified in the proteomic data.</p> <p><strong>Table S4.</strong> Results from the permutational MANOVA (PERMANOVA) tests run on the transcriptomic, proteomic, and metabolomic datasets.</p> <p><strong>Table S5.</strong> Results from the statistical tests run on the alpha- and beta-diversity metrics.</p>
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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.