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1,192 results for “multi-omics”
Data from: Multi-omics analyses on rheumatoid arthritis in CD4+ T cells
Open the record for dataset details and reuse information.
MOLI: multi-omics late integration with deep neural networks for drug response prediction
<p>Harmonized data used in "MOLI: multi-omics late integration with deep neural networks for drug response prediction", 2019, <em>Bioinformatics </em><a href="https://academic.oup.com/bioinformatics/article/35/14/i501/5529255">https://academic.oup.com/bioinformatics/article/35/14/i501/5529255</a>. <br> CNA.tar.gz contains CNA profiles with non-integer estimates of copy number, e.g. log-ratios. Please use binarized CNA profiles (CNA_binary.tar.gz) to replicate the results described in the paper. </p> <p><br> All raw data were obtained from open sources:<br> - https://www.cancerrxgene.org/<br> - ArrayExpress https://www.ebi.ac.uk/arrayexpress/<br> - Firehose Broad GDAC http://gdac.broadinstitute.org/runs/stddata__2016_01_28/data/<br> - Supplementary of Gao et al., 2015 https://www.nature.com/articles/nm.3954</p> <p>Gene symbols were mapped to Entrez Gene IDs. Data preprocessing is described in detail in supplementary materials. The code is available at <a href="https://github.com/hosseinshn/MOLI/tree/master/preprocessing_scr">https://github.com/hosseinshn/MOLI/tree/master/preprocessing_scr</a>.</p>
Data from: Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney
<p>The kidney is an organ of key relevance to blood pressure (BP) regulation, hypertension and antihypertensive treatment. However, genetically mediated renal mechanisms underlying susceptibility to hypertension remain poorly understood. We integrated genotype, gene expression, alternative splicing and DNA methylation profiles of up to 430 human kidneys to characterise the effects of BP index variants from genome-wide association studies (GWAS) on renal transcriptome and epigenome. We uncovered kidney targets for 479 (58.3%) BP-GWAS variants and paired 49 BP-GWAS kidney genes with 210 licensed drugs. Our colocalisation and Mendelian randomisation analyses identified 179 unique kidney genes with evidence of putatively causal effects on BP. Through Mendelian randomisation we uncovered effects of BP on renal outcomes commonly affecting hypertensive patients. Collectively, our studies identified genetic variants, kidney genes, molecular mechanisms and biological pathways of key relevance to the genetic regulation of BP and inherited susceptibility to hypertension.</p> <p> </p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
MOTL: enhancing multi-omics matrix factorization with transfer learning
<p>The <strong>Lrn_5000D_Fctrzn_100k_001TH.zip</strong> file contains the results of a MOFA factorization of the TGCA learning dataset, to be downloaded and used for transfer learning factorization of a target dataset with MOTL. The MOFA output is in the <strong>Model.hdf5</strong> file, and intercepts for the factorization are in the <strong>EstimatedIntercepts.rds</strong> file. The <strong>FctrMeta.json </strong>file contains metadata related to the MOFA factorization. The nohup.out file is the log of the factorization.</p> <p>The <strong>expdat_meta.rds</strong> file contains metadata from the preprocessing of the TCGA multi-omics learning dataset that was factorized. This should also to be downloaded as it is an input to MOTL</p>
Multi-Omic Single-Cell Dissection of Leukemic T-Cell Lymphoma Following CAR T-Cell Therapy
<p>This repository contains code used to produce the results in: Till Braun, Michael Rade and Maximilian Merz et al., Multi-Omic Single-Cell Dissection of Leukemic T-Cell Lymphoma Following CAR T-Cell Therapy</p> <p>Contents:<br>- Instructions for using the singularity image and R packages can be found here: <a href="https://github.com/fraunhofer-izi/Braun_et_al_2024/tree/main/singularity">https://github.com/fraunhofer-izi/Braun_et_al_2024/tree/main/singularity</a><br>- "seurat_harmony.Rds" and "seurat_harmony_t.Rds": These Seurat objects were used to produce Figure 2 in this publication. "seurat_tcell_obj.Rds" is a subset of "seurat_harmony.Rds" and contains only T-cells. In addition, the metadata was extended by the output of TCR-Seq (using the R package scRepertoire). The following script uses the objects: <a href="https://github.com/fraunhofer-izi/Braun_et_al_2024/blob/main/publication/figure_scripts/main/fig_02.R">https://github.com/fraunhofer-izi/Braun_et_al_2024/blob/main/publication/figure_scripts/main/fig_02.R</a></p> <p> </p>
Test dataset for "Spatial Integration of Multi-Omics Data from Serial Sections using the novel Multi-Omics Imaging Integration Toolset"
<p>The uploaded tar file contains anonymized and reduced test data for the paper "Spatial Integration of Multi-Omics Data from Serial Sections using the novel Multi-Omics Imaging Integration Toolset". (doi: https://doi.org/10.1101/2024.06.11.598306; https://github.com/mwess/miit)</p> <p>Dataset description:<br>- 9 serial histology sections with the following stains: (HES, HE, HES, HES, HES, MTS, IHC, IHC, HES)<br>- Sections are indexed in the following way (due to some sections not being part of this project): 1,2,3,6,7,8,9,10,11<br>- Each serial section contains: <br> - landmarks with matching labels across all sections.<br> - semi-manually generated tissue masks <br>- Section 2 contain spatial transcriptomics data and one annotation file in geojson format.<br>- Sections 6 and 7 contain imzml data that were generated with MALDI-MSI in positive ion mode (section 6) and negative ion mode (section 7) and additional histology annotations.<br>- MALDI-MSI is reduced. The positive ion data contains only intensities and spectra for spermine. The negative ion mode data contains only intensities and spectra for citrate and zinc.<br>- ST data contains only locations of spots and scalefactors. (I.e. no count data is included.). Barcode ids are randomly generated. <br>- In addition, for each ST spot histopathological annotations and GSEA scores for the Citrate-Spermine Secretion gene signature are provided.</p> <p>Abbreviations:</p> <p>- HES = Hematoxylin-Erythrosine-Saffron<br>- HE = Hematoxylin-Eosin<br>- MTS = Masson's Trichrome Staining<br>- IHC = Immunohistochemistry<br>- ST = Spatial Transcriptomics, here refers to Visium10X arrays.<br>- MALDI-MSI = Matrix-Assisted Laser Desorption Ionization - Mass Spectrometry Imaging.</p> <p> </p>
Processed Data 1 : Spatial multi-omic map of human myocardial infarction
<p>We provide here the processed snRNA-seq, snATAC-seq, and visium data for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022</p>
Data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"
<p>Joint embedding data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"</p> <p>Code available at https://github.com/MTreppner/multiomics_dgms</p>
Simulated multi-omic dataset including DNA methylation, proteins and metabolites
<p>Dataset simulated for use in a short course in molecular epidemiology.</p> <p> </p>
Multi-omics data for ischemic stroke etiology biomarker discovery
<p>Multi-omics data for ischemic stroke etiology biomarker discovery</p>
Genome annotations for: Multi-omics approaches define novel aphid effector candidates associated with virulence and avirulence phenotypes
<div> <p><span><span>Peter Thorpe</span></span><span><span>1</span></span><span><span>, Simone Altmann</span></span><span><span>1</span></span><span><span>, Rosa Lopez-Cobollo</span></span><span><span>2</span></span><span><span>, Nadine Douglas</span></span><span><span>3</span></span><span><span>, Javaid Iqbal</span></span><span><span>2</span></span><span><span>, Sadia Kanvil</span></span><span><span>2</span></span><span><span>, </span><span>Jean-Christophe Simon</span></span><span><span>4</span></span><span><span>, </span><span>James </span><span>C. </span><span>Carolan</span></span><span><span>3</span></span><span><span>, Jorunn Bos</span></span><span><span>1</span><span>*</span></span><span><span>, Colin Turnbull</span></span><span><span>2</span><span>*</span></span><span> </span></p> </div> <div> <p><span><span>1</span></span><span><span>School of Life Sciences, University of Dundee, UK;</span> </span><span><span>2</span></span><span><span>Department of Life Sciences, Imperial College London, UK; </span></span><span><span>3</span></span><span><span>Department of Biology, Maynooth University, </span><span>Republic of Ireland</span><span>; </span></span><span><span>4</span></span> <span><span>INRAE , France</span><span>. *Authors for correspondenc</span><span>e: </span></span><a href="mailto:j.bos@dundee.ac.uk" target="_blank" rel="noreferrer noopener"><span><span>j.bos@dundee.ac.uk</span></span></a><span><span>, </span></span><a href="mailto:c.turnbull@imperial.ac.uk" target="_blank" rel="noreferrer noopener"><span><span>c.turnbull@imperial.ac.uk</span></span></a><span><span>. </span></span><span> </span></p> <p> </p> <p><span>This is a repository for the version3 gene predictions and annotation for the pea aphid used for the publication:</span></p> <p> </p> <p><strong><span><span><span>Multi-omics approaches define novel aphid effector candidates associated with </span><span>virulence and </span><span>avirulence</span> <span>phenotypes</span></span><span> </span></span></strong></p> <p> </p> <div> <p><span><span>ABSTRACT</span></span></p> </div> <div> <p><span><span>Background</span></span><span><span>. Compatibility between aphids and plant hosts is genetically </span><span>determined</span><span> by both interacting organisms. For example, plants may carry resistance (R) genes or deploy chemical defences. Aphid saliva </span><span>contains</span><span> many proteins that are secreted into host tissues. </span><span>S</span><span>ubset</span><span>s</span><span> of these proteins are predicted to act as effectors, either subverting or triggering host immunity. However, associating </span><span>particular effectors</span><span> with virulence or </span><span>avirulence</span><span> outcomes presents challenges due to the combinatorial complexity. Here we use defined aphid and host genetics to test for co-segregation of expressed aphid transcripts and proteins with virulent or avirulent phenotypes.</span></span><span> </span></p> </div> <div> <p><span><span>Results</span><span>. </span></span><span><span>We compared virulent and avirulent pea aphid parental genotypes, and their bulk segregant F</span></span><span><span>1</span></span><span><span> progeny on </span></span><span><span>Medicago </span><span>truncatula</span> </span><span><span>genotypes</span></span> <span><span>carrying or lacking the </span></span><span><span>RAP1 </span></span><span><span>resistance </span><span>quantitative trait locus</span><span>. </span><span>D</span><span>ifferential expression </span><span>analysis based on </span><span>RNA sequencing </span><span>of whole bod</span><span>y and head samples, </span><span>in combination with proteomics of saliva and salivary glands</span><span>,</span> <span>enabled us </span><span>to pinpoint proteins </span><span>associated</span><span> with virulence/</span><span>avirulence</span><span> phenotypes. </span></span><span><span><span>There was relatively </span><span>little impact</span><span> of </span></span></span><span><span><span>host genotype, </span></span></span><span><span><span>whereas</span><span> l</span></span></span><span><span>arge numbers of transcripts and proteins were differentially expressed between parental aphids, </span><span>likely a</span><span> reflection of their classification as divergent biotypes within the pea aphid species complex. Many fewer </span><span>transcripts</span> <span>intersected with the equivalent differential expression patterns in the bulked F</span></span><span><span>1</span></span><span><span> progeny, providing an effective filter for removing </span><span>genomic </span><span>background effects</span></span><span><span>. </span><span>Overall, t</span><span>here were more upregulated genes detected in the </span><span>F</span></span><span><span>1</span></span><span> <span>avirulent </span><span>dataset </span><span>compared with the virulent one. </span><span>Some of the</span><span> differentially expressed transcripts </span><span>were also found in the differentially expressed proteomes</span><span>, with a</span><span>minopeptidase N prot</span><span>eins </span><span>being </span><span>t</span><span>he most frequent</span><span> differentially expressed</span><span> family</span></span><span><span>. </span><span>In addition</span><span>, a </span><span>substantial</span> <span>proportion </span><span>(26%) </span><span>of salivary proteins lack annotations, suggesting that </span><span>many </span><span>novel functions </span><span>remain</span><span> to be discovered. </span></span><span> </span></p> </div> <div> <p><span><span>Conclusions.</span></span><span><span> Especially when combined with tightly controlled genetics of both insect and host, multi-</span><span>omic</span><span> approaches are powerful tools for revealing and filtering candidate lists down to plausible genes for further functional analysis as putative </span><span>aphid </span><span>effectors.</span></span><span> </span></p> </div> </div>
Tumor multi-omics profiles and clinical information employed in TMO-Net research
<p>This dataset includes tumor multi-omics profiles used in TMO-Net research. The preprocessed TCGA pan-cancer multi-omics were employed during pre-training stage of TMO-Net. The metabric multi-omics dataset, metastatistic tumor multi-omics dataset, PDX cell line multi-omics dataset, GDSC cell line multi-omice dataset, CPTAC cancer multi-omics dataset and metastatic dataset were used for downstream tasks and analysis.</p>
Seurat objects for "Single-cell multi-omic analysis of the vestibular schwannoma ecosystem uncovers a nerve injury-like state" (https://doi.org/10.1038/s41467-023-42762-w)
Open the record for dataset details and reuse information.
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
<p>partab of His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models</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.