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”

Learn how ShareScore rates datasets ↗

zenodo56/100

EATRIS-Plus multi-omics data of a human reference cohort

In this reference study, blood samples of 127 healthy individuals were analyzed with a wide range of -omics technologies, resulting in the most comprehensive -omics  profiling data set that is publicly available. The molecular measurements that are available here, can be used as reference values for any future (multi-)omics studyies. Along with phenotypic information (Sex, Age, BMI etc. and measured cell types levels) on the healthy subjects, the following data types are included: <ul> <li>Targeted metabolomics (acylcarnitines, amino acids and very long chain fatty acids)</li> <li>Lipidomics (negative and positive ionization modes)</li> <li>Proteomics</li> <li>mRNA-seq</li> <li>miRNA-seq</li> <li>miRNA qRT-PCR</li> <li>Enzymation Methylation sequencing</li> </ul> The pre-processed mult-omics data can be accessed here in the shape of a MultiAssayExperiment object (<a href="https://doi.org/10.1158/0008-5472.can-17-0344">Ramos et al. 2017</a>). Instructions on how to read the object into R can be found here: <a href="https://github.com/EATRIS/Read_MultiAssayExperiment">Read_MultiAssayExperiment</a>. A similar object for Python (MuData) including the same data will be added later.    DATA AVAILABILITY STATEMENT: Full data related to the EATRIS-Plus multiomic cohort are available in the ClinData repository (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fclindata.imtm.cz%2F&data=05%7C02%7CCasper.deVisser%40radboudumc.nl%7C347853c763954a30b82208dc3ebe2571%7Cb208fe69471e48c48d87025e9b9a157f%7C0%7C0%7C638454233596610669%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=Jp7u%2BXblry9QNg4EQkJE4CKxMZZfMK9U84Ob7E6up90%3D&reserved=0">https://clindata.imtm.cz</a>) and include full phenotypic information, physical and laboratory examinations, multiomic data from white blood cells (whole genome sequencing, enzymatic methylation DNA sequencing, mRNA sequencing, miRNA sequencing) or plasma (miRNA qPCR profiling, proteomics, targeted metabolomics, untargeted lipidomics, Raman spectroscopy profiling). However, access is restricted due to legal, ethical, scientific and/or commercial reasons. Access to the data is subject to approval and a data sharing transfer agreement. For data access please contact <a href="mailto:data.access@imtm.upol.cz">data.access@imtm.cz</a>.

openmit-licenseMar 2024View details →

zenodo52/100

Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping

<h3>TCGA pan-cancer mRNA and DNA data augmented with artificial confounders utilised in "Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping" by Zuqi Li and Sonja Katz (manuscript in preparation).</h3> The following data curation steps were carried out:  <ul> <li>Step 1. Download data from TCGA <ul> <li>R package `TCGAbiolinks`</li> <li>2547 patients (after step 2) with 6 cancer types: <ul> <li>BRCA (731)</li> <li>THCA (408)</li> <li>BLCA (387)</li> <li>LUSC (297)</li> <li>HNSC (412)</li> <li>KIRC (312)</li> </ul> </li> <li>mRNA expression profiles</li> <li>DNAm expression profiles</li> <li>Clinical data: <ul> <li>tumor stage: i, ia, ib, ii, iia, iib, iii, iiia, iiib, iiic, iv, iva, ivb, ivc, x</li> <li>age at diagnosis</li> <li>race: 'white', 'black or african amarican', 'asian', 'american indian or alaska native'</li> <li>gender </li> </ul> </li> </ul> </li> <li>Step 2. Removal criteria <ul> <li>Patients with <ul> <li>NA or 'not reported' clinical data</li> <li>race 'american indian or alaska native'</li> <li>tumor stage x</li> </ul> </li> <li>mRNA and DNAm probes with <ul> <li>0 variance across all included patients</li> <li>not shared across all cancer types</li> <li>with missing values </li> </ul> </li> </ul> </li> <li> Step 3. Encode clinical vairables and save datasets <ul> <li>mRNA dataset: 2547 patients x 58,456 mRNAs</li> <li>DNAm dataset: 2547 patients x 232,088 DNAm</li> <li>clinic dataset: 2547 patients x 6 variables     1. patient ID     2. tumor stage: 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4     3. age at diagnosis     4. race: asian(1), black or african amarican(2), white(3)     5. gender: female(0), male(1)     6. cancer type: BRCA(1), THCA(2), BLCA(3), LUSC(4), HNSC(5), KIRC(6)     </li> </ul> </li> <li> Step 4. Pre-process the datasets <ul> <li>mRNA dataset: 'TCGA_mRNAs_processed.csv' <ul> <li>Take the 2000 mRNAs with highest variance</li> <li>Rescale every feature to [0,1]</li> <li>--> 2547 patients x 2000 mRNAs</li> </ul> </li> <li>DNAm dataset: 'TCGA_DNAm_processed.csv' <ul> <li>Take the 2000 DNAm with highest variance</li> <li>Rescale every feature to [0,1]</li> <li>--> 2547 patients x 2000 DNAm</li> </ul> </li> <li>clinic dataset: 'TCGA_clinic.csv' </li> </ul> </li> <li>Step 5. Simulate confounders (instructions can be found in Methods section of manuscript) <ul> <li>Linear confounder: <ul> <li>'TCGA_confounder_linear.csv' - linear confounding classes </li> <li>'TCGA_DNAm_confounded_linear.csv' - linearly confounded DNAm data </li> <li>'TCGA_mRNA2_confounded_linear.csv'  - linearly confounded mRNA data </li> </ul> </li> <li>Squared confounder <ul> <li>'TCGA_confounder.csv' - squared confounding classes </li> <li>'TCGA_DNAm_confounded.csv' - squared confounded DNAm data </li> <li>'TCGA_mRNA2_confounded.csv'  - squared confounded mRNA data</li> </ul> </li> <li>Categorical confounder  <ul> <li>'TCGA_confounder_categ2.csv' - categorical confounding classes </li> <li>'TCGA_DNAm_confounded_categ2.csv' - categorically confounded DNAm data </li> <li>'TCGA_mRNA2_confounded_categ2.csv'  - categorically  confounded mRNA data</li> </ul> </li> <li>Multiple confounders - combined effect (linear + squared + categorical) <ul> <li>'TCGA_confounder_multi.csv' - confounding classes for combined effect </li> <li>'TCGA_DNAm_confounded_multi.csv' - DNAm data with combined effect </li> <li>'TCGA_mRNA2_confounded_multi.csv'  - mRNA data with combined effect</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Dec 2023View details →

zenodo48/100

Multi-omic Insights into Molecular Mechanism and Therapeutic Targets in Spinocerebellar Ataxia type 7

The molecular mechanism in spinocerebellar ataxia type 7 is currently poorly understood. To provide understandings, a multi-omic study was performed using SCA7266Q/5Q mice. At week 12, entire brain tissue samples were collected and RNA sequencing, methylation analysis, and proteomic analysis were performed. Results were integrated to identify genes with identical trends in expression. Data was also compared with SCA patient serum proteomic analysis, and based on common differentially expressed proteins, a Naïve Bayesian network model was constructed to predict nilotinib treatment response. Data from RNA sequencing and methylation analysis revealed 58 significantly hypomethylated-upregulated genes and 62 hypermethylated-downregulated genes, mostly enriched in GO terms of regulation of axonogenesis, channel activity, and monoamine signaling. In the proteomic analysis, 211 upregulated and 281 downregulated DEPs associated mostly with immune response and cellular mobility were identified. Two genes, Fam107b and Tph2, showed differential expression in both transcriptomic and proteomic analysis. Forty-two overlapping proteins were identified compared with SCA patient serum, and Bayesian network analysis revealed that nilotinib treatment response was associated with the protein expression of CLU, CA2, GLUL, PRDX6, C1QA, PLXNB1, and age. These findings will serve as an important reference for future studies on the pathogenesis and discovery of druggable targets.

opencc-by-4.0Aug 2024View details →

zenodo48/100

Multi-omics analysis reveals the link between Treg distribution and therapy efficacy in Hepatocellular Carcinoma patients treated with tremelimumab plus durvalumab

Introduction Hepatocellular carcinoma (HCC) remains a significant contributor to cancer-related deaths. Immunotherapy, either alone or in combination, has emerged as the standard treatment for advanced HCC. Notably, the combination of durvalumab (dur) and tremelimumab (trem) has received FDA approval based on findings from the HIMALAYA trial. However, comprehensive studies elucidating immune responses are lacking. We conducted a thorough analysis utilizing clinical samples from tumor biopsies to understand the mechanism of response. Methods Multiplexed immunofluorescence microscopy was used to analyze immune cell infiltration in primary human liver cancer samples. We developed and validated a comprehensive 37-plex antibody panel for immunofluorescence imaging of human FFPE samples. We applied highly multiplexed co-detection by indexing (CODEX) technology to simultaneously profile in situ expression of 37 proteins at sub-cellular resolution in 20 HCC patient samples using whole slide scanning. We established an image analysis pipeline to quantify all major cell populations in the human liver using supervised manual gating and unsupervised clustering algorithms using the exported matrix of the marker expression and spatial information. Clinical metadata including sex, gender, ethnicity, pretreatment, and histopathological reports are available for all patient samples. Results Using high-dimensional spatially resolved quantitative analysis of multiplexed immunofluorescence microscopy images, we generated a unique dataset and profiled the single-cell pathology landscape for human HCC treated with immunotherapy. In situ phenotyping of 400,000 single cells (including 130,000 CD45+ immune cells) allowed for the quantification of cell phenotype clusters, differential analysis of activation markers, and spatial features of each individual cell. This analysis revealed the comprehensive profile of the cell composition and spatial interactions of different cells in the TiME of patients treated with immunotherapy. Further details on the study can be obtained in our paper once it’s published. Conclusion We developed the CODEX panel for FFPE biopsy samples of HCC patients.

opencc-by-4.0Mar 2025View details →

zenodo48/100

Multi-omics identify LRRC15 as a COVID-19 severity predictor and persistent pro-thrombotic signals in convalescence

RNA sequencing, SomaLogic proteomics and flow cytometry data were generated for two cohorts of end-stage kidney disease patients with COVID-19. The Wave 1 cohort consists of samples collected from patients during the first wave of COVID-19 in early 2020, while samples were collected for the Wave 2 cohort in the following year. This data deposition includes the RNA-seq counts, SomaScan proteomics, flow cytometry and clinical metadata associated with the study. For further information about the study and data, see the associated GitHub repository (https://github.com/jackgisby/covid-longitudinal-multi-omics) or our pre-print (https://doi.org/10.1101/2022.04.29.22274267). The repository also contains code to replicate our analysis of the data. The raw RNA-seq reads were processed using the nf-core RNA-seq v3.2 pipeline before htseq-count was used to generate a raw counts matrix, which is included in this deposition (<code>htseq_counts.csv</code>). Three files make up the proteomics data: <code>sample_technical_meta.csv</code>, <code>feature_meta.csv</code> and <code>soma_abundance.csv</code>. The first two files contain metadata columns for the samples and protein features, respectively. The final file includes the unprocessed protein abundance data. The files <code>general_panel.csv</code> and <code>t_cell_panel.csv</code> contain the flow cytometry data, split into the general and T-cell panels, respectively. Finally, clinical metadata is available for the two cohorts described in this study (<code>w1_metadata.csv</code>, <code>w2_metadata.csv</code>). The features in the clinical metadata include: <table> <thead> <tr> <th>Column Name</th> <th>Data Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>sample_id</td> <td>Character</td> <td>Unique identifier for samples</td> </tr> <tr> <td>individual_id</td> <td>Character</td> <td>Unique identifier for individuals</td> </tr> <tr> <td>ethnicity</td> <td>Character</td> <td>The individual's ethnicity (asian, white, black or other)</td> </tr> <tr> <td>sex</td> <td>Character</td> <td>The individual's sex (M or F)</td> </tr> <tr> <td>calc_age</td> <td>Integer</td> <td>Age in years</td> </tr> <tr> <td>ihd</td> <td>Character</td> <td>Information on coronary heart disease</td> </tr> <tr> <td>previous_vte</td> <td>Character</td> <td>Whether individuals have had venous thromboembolism</td> </tr> <tr> <td>copd</td> <td>Character</td> <td>Whether individuals have chronic obstructive pulmonary disease</td> </tr> <tr> <td>diabetes</td> <td>Character</td> <td>Whether individuals have diabetes, and, if so, the type of diabetes</td> </tr> <tr> <td>smoking</td> <td>Character</td> <td>Smoking status</td> </tr> <tr> <td>cause_eskd</td> <td>Character</td> <td>Cause of ESKD</td> </tr> <tr> <td>WHO_severity</td> <td>Character</td> <td>The peak (WHO) severity for the patient over the disease course</td> </tr> <tr> <td>WHO_temp_severity</td> <td>Character</td> <td>The (WHO) severity at time of sampling</td> </tr> <tr> <td>fatal_disease</td> <td>Logical</td> <td>Whether the disease was fatal</td> </tr> <tr> <td>case_control</td> <td>Character</td> <td>Whether the individual was COVID-19 <code>POSITIVE</code> or <code>NEGATIVE</code> at time of sampling. Convalescent patients are denoted by the label <code>RECOVERY</code></td> </tr> <tr> <td>radiology_evidence_covid</td> <td>Character</td> <td>Evidence of COVID-19 from radiology</td> </tr> <tr> <td>time_from_first_symptoms</td> <td>Integer</td> <td>The number of days since the individual first experienced COVID symptoms at time of sampling</td> </tr> <tr> <td>time_from_first_positive_swab</td> <td>Integer</td> <td>The number of days since the individual's first positive swab was taken at time of sampling</td> </tr> </tbody> </table>

opencc-by-4.0Apr 2022View details →

zenodo44/100

Multi-omic approach to identify phenotypic modifiers underlying cerebral demyelination in X-linked adrenoleukodystrophy

These are the data tables used to produce results in the publication: "Multi-omic approach to identify phenotypic modifiers underlying cerebral demyelination in X-linked adrenoleukodystrophy." Phillip A. Richmond & Frans van der Kloet et al. Submitting to Frontiers in Cellular and Developmental Biology, 2020, Peroxisomal Special Issue.  These tables include normalized measurements from four omics technologies, with no identifying information included. For details on processing, see the manuscript or contact: prichmond (at) cmmt (dot) ubc (dot) ca.  Description of Files <ul> <li>Sample mapping <ul> <li>20180314_sib_pairs.xlsx <ul> <li>Excel sheet describing family numbering, etc. used as a mapping table within the sheets below. </li> </ul> </li> </ul> </li> <li>Methylation: <ul> <li>DMRs_5_Families_ALL_0.10DB_Dec2019.csv <ul> <li>Significant methylated regions with delta beta at least 10 percent when a single family is left out</li> </ul> </li> <li>ALD_Deconvoluted_Betas_Dec2019.csv <ul> <li>All fitted betas for every subject (single CpG)</li> </ul> </li> <li>ALD_Limma_Final_Dec2019_CHR.csv <ul> <li>All fitted effects using limma modeling per CpG </li> </ul> </li> </ul> </li> <li>RNA: <ul> <li>Count_data.txt <ul> <li>The raw count table summed at the gene level using featureCounts.</li> </ul> </li> <li>Pvalues_all_23_01_2019.csv <ul> <li>All pvalues and log fold changes for the genes included in the modeling process (also with family left out)</li> </ul> </li> <li>Tmm_norm_counts_5_2_2020.csv <ul> <li>Tmm normalized RNA count data</li> </ul> </li> </ul> </li> <li>Proteomic <ul> <li>Report_Precursor_Peptides.xls <ul> <li>The proteomic data as an excel spreadsheet</li> </ul> </li> </ul> </li> <li>Pvalues_prot_13_3_2019.xlsx <ul> <li>The pvalues and log fold changes (also with family left out)</li> </ul> </li> <li>Lipids: <ul> <li>Lipid_data.csv <ul> <li>The lipid data (metabolites with missings are removed)</li> </ul> </li> <li>Pvalues_lipids.csv <ul> <li>Pvalues for the lipid data (also with family left out)</li> </ul> </li> </ul> </li> </ul> NOTE: For use of these data files for processing and reproducing results of the manuscript, please see https://github.com/Phillip-a-richmond/ALD_Modifier_Project.

opencc-by-4.0Mar 2020View details →

zenodo44/100

EnrichKit: a multi-omics tool for livestock research

This is the backend database for the web application EnrichKit. This <a href="../api/records/10257552/draft/files/EnrichKitDB.sqlite/content">EnrichKitDB.sqlite </a>object is created following this repo - https://github.com/liulihe954/EnrichKitDB The main EnrichKit repo can be found there - https://github.com/liulihe954/EnrichKitWeb

opencc-by-4.0Dec 2023View details →

zenodo44/100

Multi-omic analysis of the Arabidopsis clock activator mutant rve 4 6 8 reveals connections to carbohydrate metabolism and proteasome regulation

Plants are able to sense changes in their light environments, such as the onset of day and night, as well as anticipate these changes in order to adapt and survive. Central to this ability is the plant circadian clock, a molecular circuit that precisely orchestrates plant cell processes over the course of a day. REVEILLE proteins (RVEs) are recently discovered members of the plant circadian circuitry that activate the evening complex and PRR genes to maintain regular circadian oscillation. The RVE 8 protein and its two homologs, RVE 4 and 6, have been shown to limit the length of the circadian period, with rve 4 6 8 triple-knockout plants possessing an elongated period along with increased leaf surface area, biomass, cell size and delayed flowering relative to wild-type Col-0 plants. Here, using a multi-omics approach consisting of phenomics, transcriptomics, proteomics, and metabolomics we draw novel connections between RVE8-like proteins and a number of core plant cell processes. In particular, we reveal that loss of RVE8-like proteins results in altered carbohydrate, organic acid and lipid metabolism, including a starch excess phenotype at dawn. We further demonstrate that rve 4 6 8 plants have lower levels of 20S proteasome subunits and possess significantly reduced proteasome activity, potentially explaining the increase in cell-size observed in RVE8-like mutants. Overall, this robust, multi-omic dataset, provides substantial new insights into the far reaching impact RVE8-like proteins have on the diel plant cell environment. This dataset has the raw search outputs for the mass-spec analysis for this manuscript.

opencc-by-4.0May 2022View details →

zenodo44/100

Multi-omic machine learning predictor of breast cancer therapy response

H&E slides used in the training dataset described in "Multi-omic machine learning predictor of breast cancer therapy response" published in Nature: <a href="https://www.nature.com/articles/s41586-021-04278-5">https://www.nature.com/articles/s41586-021-04278-5</a> Metadata associated with these images also included in file Slide metadata.xlsx

opencc-by-4.0Jan 2022View details →

zenodo44/100

Multi-omics data for pro-inflammatory and anti-inflammatory exposure to THP-1 macrophages

This data characterizes gene expression levels in THP-1 macrophages. The data was generated using RNA sequencing and analyzed with DeSeq2 (version 1.24.0). The analysis included raw count data and normalized count matrices obtained from DESeq2's dds_deseq objects. This data describes the methylation levels of individual CpG sites in THP-1 macrophages. The data was obtained using the Infinium MethylationEPIC v2.0 Kit (Illumina) and analyzed with the minfi package (version 1.46). Specifically, the data underwent quantile normalization using the preprocessQuantile function within minfi. Only CpG sites with a detection p-value less than 0.05 were included to obtain MatrixProcessedGEO.txt file. The beta values (bValues.xlsx) were obtained using the function “getBeta” from the same package, considering each time point individually. The macrophages were exposed to phorbol 12-myristate 13-acetate (PMA) for 48 hours, followed by treatment with either a combination of LPS (10 pg/ml) and interferon-gamma (IFNγ) (20 ng/ml) or a combination of interleukins 13 (IL-13) (20 ng/ml) and 4 (IL-4) (20 ng/ml) for 24, 48, and 72 hours.

opencc-by-4.0Jun 2024View details →

zenodo40/100

Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation

Data accompanying the manuscript "Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation". Note: For ATAC fragment files, e,caQTL full cis scan summary files, clustering objects, please see the CMDGA portal (https://cmdga.org/search/?searchTerm=stephen-parker%3AVarshney2024) For raw data including fastq files, please see dbGaP repo phs001048.v3.p1 Data in this repository includes: Filename: Description 1. list of 8,666 genes for which exon-only counts were considered. See methods section "Adjusting RNA counts for overlapping gene annotations" in the manuscript. 2. nucleus_sample_cluster_map.tsv: nucleus-sample-cluster map with other QC info.  # index: nucleus identified syntax <modality>.<batch>.NM.<10X channel>.<barcode>  # UMAP_1, UMAP_2: UMAP coordinates for visualization # modality: rna or atac # batch: processing batch identifier # hqaa_umi: high quality autosomal alignments (HQAA) for atac nuclei, unique molecular identifier (UMI) for tna  # fraction_mitochondrial: fraction of reads mapping to the mitochondrial genome # cohort: sample cohort # tss_enrichment: TSS enrichment for atac nuclei # coarse_cluster_name: cluster name 3. peaks.tar.gz: snATAC peak features including: # consensus-summits.bed: consensus summits along with the cell type that the summits was highest in. # narrow peaks in clusters # consensus summit feature (summit +- 150bp) identified in each cluster - these were used in GWAS enrichments. 4. snrna-cell-type-specific-genes.tsv: Normalized expression scores for genes in each cell-type cluster 5. eqtl_permute.tar.gz: Permutation scan eQTL in each cell-type cluster. Columns:  # variant: syntax <chrom>:<hg38 pos>:<ref>:<alt> # effect_allele: effect allele (was the alt allele) # other_allele: non-effect allele # feature: gene name # featureCoordinates_tss: gene TSS # p-value: nominal p value # beta: slope/beta of the linear regression. Keyed on the alt allele # se: standard error of the slope # snp: SNP ID # strand: gene strand # n_variants_tested: number of variants tested for the gene # distance_var_pheno: distance of the variant with the gene TSS # n_effective_tests: number of effective tests # p_beta: beta distribution adjusted p value # qvalue: qvalue (Storey) 6. caqtl_permute.tar.gz: # Permutation scan caQTL in each cell-type cluster. Columns:  # variant: syntax <chrom>:<hg38 pos>:<ref>:<alt> # effect_allele: effect allele (was the alt allele) # other_allele: non-effect allele # feature: peak feature coordinates # p-value: nominal p value # beta: slope/beta of the linear regression. Keyed on the alt allele # se: standard error of the slope # snp: SNP ID # n_variants_tested: number of variants tested for the gene # distance_var_pheno: distance of the variant with the gene TSS # n_effective_tests: number of effective tests # p_beta: beta distribution adjusted p value # qvalue: qvalue (Storey) 7. eqtl_credible_sets.tar.gz: # eQTL credible set. The file name denotes the egene and the signal hit id. Bed file columns:  # 1: snp chromosome # 2: snp start # 3: snp end # 4: snp chrom_pos_ref_alt # 5: Bayes Factor  # 6: PIP # 7: SNP rsid 8. caqtl_credible_sets.tar.gz: # caqtl credible set. The file name denotes the capeak and the signal hit id. Bed file columns:  # 1: snp chromosome # 2: snp start # 3: snp end # 4: snp chrom_pos_ref_alt # 5: Bayes Factor  # 6: PIP # 7: SNP rsid 9. cicero_all.tar.gz # Cicero coaccessibility results. Columns # Peak 1: Macs2 narrowpeak coordinate for peak 1 # Peak 2: Macs2 narrowpeak coordinate for peak 2 # coaccess: Cicero coaccessibility score 10. cicero_gene_tss.tar.gz: Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name. Columns # Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name.Columns # Peak 1: Macs2 narrowpeak coordinate for peak 1 # gene_name: Assigned gene # Peak 2: Macs2 narrowpeak coordinate for peak 2 # coaccess: Cicero coaccessibility score ##  Peak1 is the narrowpeak in the TSS region, peak2 is the distal peak 11. mash.tar.gz Mashr results for e/caQTL - lfsr, posterior means and posterior SD for each tested eSNP-eGene, caSNP-caPeak pair.  12. cellregmap.tar.gz: Cellregmap results for endothelial nucleus-level eQTL scans. ## Persistent genetic effect beta_g was calculated in a simple association model.  ## An interaction model was fit to test for GxC effect. columns: # rho1, g2, e1, and eps2 are variance component measures outputs from CellRegMap corresponding to interaction, genetic, environment and residual variance components.  # p_nominal: nominal p from cellRegMap # kind: model kind in CellRegMap - simple association or interaction # beta_g:  Persistent genetic effect # gene_name: gene name for eQTL or peak feature name for caQTL # context: context used either factors (continuous) or subclusters (discrete) # snp: index snp for which model is fit. This is the most significant identified snp from our standard e,caQTL scans. chrom-hg38pos-rsid 13. coloc-eqtl-caqtl.tsv: # Summary of eQTL-caQTL coloc in each cluster. Columns: # nsnps: Number of SNPs in the region # eqtl_hit: SNP with the highest Bayes factor in the SuSiE eQTL credible set # caqtl_hit: SNP with the highest Bayes factor in the SuSiE caQTL credible set # PP.H0.abf: Coloc posterior probability for no signal # PP.H1.abf: Coloc posterior probability for signal in dataset 1 # PP.H2.abf: Coloc posterior probability for signal in dataset 2 # PP.H3.abf: Coloc posterior probability for different signals in datasets 1 and 2 # PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2 # idx1: Index of the SuSiE credible set for dataset 1 # idx2: Index of the SuSiE credible set for dataset 2 # cluster: cluster name # egene: eGene name # capeak: caPeak coordinates 14. cit-mrs-summary.tsv:  Summary from CIT and MR Steiger directionality tests. Columns: # cluster: cluster name # egene: eGene name # capeak: caPeak coordinates # eqhit: SNP with the highest Bayes factor in the SuSiE eQTL credible set # cahit: SNP with the highest Bayes factor in the SuSiE caQTL credible set # p.cit_c_c-e: P value for CIT causal cahit-ca-to-e model # q.cit_c_c-e: q value for CIT causal cahit-ca-to-e model # p.cit_rc_c-e: P value for CIT reverse-causal eqhit-ca-to-e model  # q.cit_rc_c-e: value for CIT reverse-causal eqhit-ca-to-e model  # p.cit_c_e-c: P value for CIT causal eqhit-e-to-ca model # q.cit_c_e-c: q value for CIT causal eqhit-e-to-ca model # p.cit_rc_e-c: P value for CIT reverse-causal cahit-e-to-ca model  # q.cit_rc_e-c: q value for CIT reverse-causal cahit-e-to-ca model  # cit_direction: Direction inferred from CIT   # correct_causal_direction--ca-to-e: MR Steiger directionality test - is ca-to-e direction correct? # correct_causal_direction--e-to-ca: MR Steiger directionality test - is e-to-ca direction correct? # sensitivity_ratio--ca-to-e: MR Steiger Sensitivity ratio for ca-to-e model  # sensitivity_ratio--e-to-ca:  MR Steiger Sensitivity ratio for e-to-ca model # steiger_test--ca-to-e: MR Steiger directionality test P value for ca-to-e model # steiger_test--e-to-ca: MR Steiger directionality test P value for e-to-ca model # steiger_q--ca-to-e: MR Steiger directionality test q value for ca-to-e model # steiger_q--e-to-ca: MR Steiger directionality test q value for e-to-ca model # mrs_direction: Direction inferred from MR Steiger # direction: Direction inferred requiring consistent results between CIT and MR Steiger directionality test 15. coloc-gwas-eqtl.tsv and 16. coloc-gwas-caqtl.tsv # Summary of e/caQTL coloc with GWAS in each cluster. Columns: # nsnps: Number of SNPs in the region # gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set # eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set # caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set # PP.H0.abf: Coloc posterior probability for no signal # PP.H1.abf: Coloc posterior probability for signal in dataset 1 # PP.H2.abf: Coloc posterior probability for signal in dataset 2 # PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2 # PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2 # idx1: Index of the SuSiE credible set for dataset 1 # idx2: Index of the SuSiE credible set for dataset 2 # cluster: cluster name # egene: eGene name # capeak: caPeak coordinates # p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization # trait: GWAS trait name # gwas_locus: GWAS locus name for the coloc test - a 250kb left and right flanking genomic window on this SNP was considered for testing coloc between all pairs of GWAS/QTL signals identified in this region   # traitname: Expanded GWAS trait name # variable_type: GWAS type  # source: Source of GWAS - either UKBB or other study 17. supplementary_tables.xlsx: Supplementary tables from the manuscript. Information included in sheets: 1. "marker_genes": Marker genes known from literature used to annotate clusters 2. "n_nuclei": n pass-QC nuclei per modality-sample-cluster 2. "snrna_GO_enrichment": GO term enrichment: matrix of cluster vs top 2 GO terms 3. "qtl_scan_info":  e/caQTL scan info cluster: cluster ntested_eqtl: N genes tested for eQTL nsig_eqtl: N significant (5% FDR) eGenes n_pheno_pcs_eqtl: N phenotype PCs considered for eQTL ratio_eqtl: Ratio of N eGenes/N genes tested nsig_caqtl:  N peaks tested for caQTL ntested_caqtl: N significant (5% FDR) caPeaks n_pheno_pcs_caqtl: N phenotype PCs considered for caQTL ratio_caqtl: Ratio of N caPeaks/N peaks tested nsamples_eqtl: N samples for eQTL nsamples_caqtl: N samples for caQTL 4. "gwas_trait_list": GWAS trait info trait: GWAS trait ID traitname: GWAS trait description variable_type: GWAS type. case/control (cc), continuous_irnt=continuous inverse-normal transformed source: GWAS source doi: GWAS study DOI 5. "traits_in_ldsc_baseline" - list of annotations included in the baseline model for LDSC 6. "gwas_enrichment_in_peaks" GWAS enrichment in cluster peaks (S-LDSC) 7. "gwas_enrichment_in_qtl_peaks" GWAS enrichment in QTL peaks (fGWAS) # fGWAS results comparing GWAS enrichment in type 1 annotations CI_lower_ln, estimate_ln, CI_upper_ln: natural log of lower confidence interval, estimate, and upper confidence interval trait: trait id traitname: trait name annotation: annotation sig: 1 if CIs don't overlap 0, otherwise 0 8. t2d_gwas_caqtl_coloc and 9. t2d_gwas_eqtl_coloc: Summary of e,caQTL coloc with T2D GWAS in each cluster, along with target gene nominations. Columns: nsnps: Number of SNPs in the region gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set PP.H0.abf: Coloc posterior probability for no signal PP.H1.abf: Coloc posterior probability for signal in dataset 1 PP.H2.abf: Coloc posterior probability for signal in dataset 2 PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2 PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2 idx1: Index of the SuSiE credible set for dataset 1 idx2: Index of the SuSiE credible set for dataset 2 cluster: cluster name egene: eGene name capeak: caPeak coordinates p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization trait: GWAS trait id diamante_gwas_locus: GWAS signal from the DIAMANTE 2018 study. Some signals that our SuSiE runs identified were not present in the original study in which case this column is NA traitname: Expanded GWAS trait name capeak_in_tss: caPeak in TSS + 1kb upstream region of a gene gene_target_standard_cicero: caPeak coaccessible with TSS peak of a gene considering nuclei from all samples for co-accessibility gene_target_allelic_cicero:  caPeak coaccessible with TSS peak of a gene considering nuclei from samples homozygous for the caSNP allele associated with increased accessibility gwashit_nominal_egene: gwas_hit nominally associated with these genes nominated in the columns capeak_in_tss, gene_target_standard_cicero, and  gene_target_allelic_cicero 10. MPRA results for the C2CD4A locus

opencc-by-4.0Nov 2023View details →

zenodo40/100

Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and East Asian ancestries

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions, and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Cross-tissue analyses indicated that over a third of effector genes most likely act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis, and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies. The data submitted here are expression and methylation models with LD reference data for the transcriptome-wide (TWAS), methylome-wide (MWAS) and transcript isoform-wide association study (TIsWAS) as described in the manuscript "Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and East Asian ancestries". Details of the methods are presented in the method section and supplementary information file.  TWAS analysis  Gene expression models for the six in-house expression datasets were generated using the PredictDB v7 pipeline for a total of 1,077 participants. Elastic net model building with 10-fold cross-validation was performed independently for each dataset. The elastic net models for GTEx v8 Colon Transverse were obtained from the PredictDB data repository (<a href="http://predictdb.org/">http://predictdb.org/</a>) and had been generated using the same pipeline. Models were computed using HapMap2 SNPs ±1Mb from each gene, together with covariate factors estimated using PEER32, clinical covariates when appropriate (age, sex and, where appropriate, case-control status, type of polyp and anatomic location in the colorectum), and three PCs from the individual dataset’s SNP genotype data. Transcript-based TWAS analyses (TIsWAS) were likewise performed by using transcript-level data from the SOCCS, BarcUVa-Seq and GTEx Colon Transverse datasets. MWAS analysis  Methylation beta values were calculated based on the manufacturer’s standard, ranging from 0 to 1. Quality control and data normalization were performed in R using the ChAMP software pipeline for the EPIC and 450K arrays. Briefly, we filtered out failed probes with detection P > 0.02 in >5% of samples, probes with <3 reads in >5% of samples per probe and all non-CpG probes. Samples with failed probes >0.1 were also excluded from downstream analyses. We discarded all probes with SNPs within 10bp of the interrogated CpG (from 1,000 Genomes Project, CEU population)34, and probes that ambiguously mapped to multiple locations in the human genome with up to two mismatches33. We only considered probes mapping to autosomes and those overlapping between the EPIC and the 450K arrays. Normalization was achieved using the Beta MIxture Quantile (BMIQ) method. Per probe methylation models were created using the PredictDB pipeline on the normalized methylation matrix and the genotypes as per TWAS eQTL analysis. To optimize power, we restricted our analysis to 263,341-238,443 (for the 450K array) and 377,678 (for the EPIC array) probes annotated to Islands, Shores and Shelves, and discarded “Open Sea” regions.

opencc-by-4.0Dec 2021View details →

dryad40/100

Data for: Multi-omics analysis identifies symbionts and pathogens of blacklegged ticks (Ixodes scapularis) from a Lyme disease hotspot in southeastern Ontario, Canada

Ticks in the family Ixodidae are recognized as important vectors of zoonoses including Lyme disease (LD), which is caused by spirochete bacteria from the Borreliella (Borrelia) burgdorferi sensu lato (Bbsl) complex. The blacklegged tick (Ixodes scapulars) continues to expand across Canada, creating hotspots of elevated LD risk at the leading edge of its expansion range. Current efforts to understand the risk of pathogen transmission associated with I. scapularis in Canada focus primarily on targeted screens, while variation in the tick microbiome remains poorly understood. Using multi-omics consisting of 16S metabarcoding and ribosome-depleted, whole-shotgun RNA transcriptome sequencing, we examined the microbial communities associated with adult I. scapularis (N = 32), sampled from four tissue types (whole tick, salivary glands, midgut, and viscera) and three geographical locations within an LD hotspot near Kingston, Ontario. The communities consisted of both endosymbiotic and known or potentially pathogenic microbes, including RNA viruses, bacteria, and a Babesia sp. intracellular parasite. We show that β-diversity is significantly higher between individual tick salivary gland and midgut bacterial communities, compared to whole ticks; while linear discriminant analysis (LDA) effect size (LEfSe) determined that the three potentially pathogenic bacteria detected by V4 16S rDNA sequencing were also discriminatory for dissected tissues only, including a Borrelia from the Bbsl complex, Borrelia miyamotoi, and Anaplasma phagocytophilum. Importantly, we find co-infection of I. scapularis by multiple microbes, in contrast to diagnostic protocols for LD, which typically focus on infection from a single pathogen of interest (B. burgdorferi sensu stricto).

opencc-zeroNov 2022View details →

zenodo40/100

Extended data: Tissue-specific multi-omics analysis of atrial fibrillation

Summary statistics and result repository for the publication Tissue-specific multi-omics analysis of atrial fibrillation: Assum, I., Krause, J., Scheinhardt, M.O. et al. Tissue-specific multi-omics analysis of atrial fibrillation. Nat Commun 13, 441 (2022). https://doi.org/10.1038/s41467-022-27953-1 For the related source code, see https://doi.org/https://doi.org/10.5281/zenodo.5094276 or https://github.com/heiniglab/symatrial. Ines Assum1,2,&dagger;, Julia Krause3,4,&dagger;, Markus O. Scheinhardt5, Christian Müller3,4, Elke Hammer6,7, Christin S. Börschel4,8, Uwe Vöker6,7, Lenard Conradi9, Bastiaan Geelhoed4,8,10, Tanja Zeller3,4,*, Renate B. Schnabel4,8,*, Matthias Heinig1,2,11,* &dagger; ,* These authors contributed equally.  1 Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany.  2 Department of Informatics, Technical University Munich, München, Germany.  3 University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, Hamburg, Germany.  4 Partner site Hamburg/Kiel/Lübeck, DZHK (German Center for Cardiovascular Research), Hamburg, Germany.  5 Institute of Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany.  6 Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.  7 Partner site Greifswald, DZHK (German Center for Cardiovascular Research), Greifswald, Germany.  8 Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.  9 Department of Cardiovascular Surgery, University Heart and Vascular Center Hamburg, Hamburg, Germany. 10 Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. 11Partner site Munich, DZHK (German Center for Cardiovascular Research), Munich, Germany.   ABSTRACT: Genome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants. Their underlying molecular mechanisms, especially consequences for mRNA and protein expression remain largely elusive. Thus, refined multi-omics approaches are needed for deciphering the underlying molecular networks. Here, we integrate genomics, transcriptomics, and proteomics of human atrial tissue in a cross-sectional study to identify widespread effects of genetic variants on both transcript (cis-eQTL) and protein (cis-pQTL) abundance. We further establish a novel targeted transQTL approach based on polygenic risk scores to determine candidates for AF core genes. Using this approach, we identify two trans-eQTLs and five trans-pQTLs for AF GWAS hits, and elucidate the role of the transcription factor NKX2-5 as a link between the GWAS SNP rs9481842 and AF. Altogether, we present an integrative multi-omics method to uncover trans-acting networks in small datasets and provide a rich resource of atrial tissue-specific regulatory variants for transcript and protein levels for cardiovascular disease gene prioritization. This version contains a reference file identifying effect alleles for all QTL results and adds additional genotype and allele frequency information for all QTL SNPs.  TABLE OF CONTENTS: <ul> <li>Reference for effect alleles map_AFHRI_B_effect_alleles.txt</li> <li>Reference for genotype and allele frequencies (derived using PLINK) <ul> <li>genotype_allele_frequencies_eQTL_SNPs.txt</li> <li>genotype_allele_frequencies_pQTL_SNPs.txt</li> <li>genotype_allele_frequencies_resQTL_SNPs.txt</li> </ul> </li> <li>Single-omic cis-QTL results <ul> <li>cis-eQTLs (all pairs, incl. LD clump info) eQTL_right_atrial_appendage_allpairs_clump.txt</li> <li>cis-pQTLs (all pairs, incl. LD clump info) pQTL_right_atrial_appendage_allpairs_clump.txt</li> <li>cis-res eQTLs (all pairs, incl. LD clump info) res_eQTL_right_atrial_appendage_allpairs_clump.txt</li> <li>cis-res pQTLs (all pairs, incl. LD clump info) res_pQTL_right_atrial_appendage_allpairs_clump.txt</li> <li>cis-ratioQTLs (all pairs, incl. LD clump info) ratioQTL_right_atrial_appendage_allpairs_clump.txt</li> </ul> </li> <li>Functional cis-QTL categories and eQTL/pQTL overlap: <ul> <li>All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with a significant eQTL and pQTL (FDR<0.05) Fig2a_source_data_Shared_eQTL_pQTL_clump.txt</li> <li>All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with a significant eQTL but no pQTL (FDR<0.05) Fig2b_source_data_Independent_eQTL_clump.txt</li> <li>All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with no eQTL but a significant pQTL (FDR<0.05) Fig2c_source_data_Independent_pQTL_clump.txt </li> </ul> </li> <li>QTS rankings and enrichment results <ul> <li>eQTS rankings and enrichments TableS6_source_data_eQTS_ranking.txt TableS7_source_data_eQTS_GSEA_results.txt</li> <li>pQTS rankings and enrichments TableS8_source_data_pQTS_ranking.txt TableS9_source_data_pQTS_GSEA_results.txt</li> </ul> </li> <li>Trans-QTLs all tested pairs including trans-pQTLs for trans-eQTLs and trans-eQTLs for trans-pQTLs Table2_source_data_Trans-QTL_results.txt</li> </ul>

opencc-by-4.0Jul 2021View details →

zenodo40/100

Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease

This dataset repository corresponds to the project Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease.

opencc-by-4.0Mar 2022View details →

zenodo40/100

Multi-Omics Visible Drug Activity Prediction with a Biologically Informed Neural Network Model

Drug discovery is a challenging task, it takes several years for a drug to be introduced on the market, with most of the studied drugs not even passing the first phase. The understanding of the mechanisms influencing response to drugs can reduce failures and accelerate drug development. Virtual drug screening, based on Machine Learning models, is a promising field for the prediction of the outcome of a treatment. However, the complex relationships between the features learned by these models are still poorly understood and not easy to interpret. We have designed a Neural Network model for drug sensitivity prediction that leverages a Visible Neural Network, an easily interpretable model, due to its biologically informed nature. The trained model can be inspected to study which biological processes were fundamental for the prediction and to identify the drug properties that affect sensitivity. It combines multi-omics data from various types of tumor tissues and drug representations based on molecular descriptors. The mechanisms learned from the network can also be exploited to find candidate drugs for synergy to predict the effect of combined therapies. We consider the unbalanced nature of public drug screening datasets and show that our model outperforms state-of-the-art visible machine learning models.

opencc-by-4.0Dec 2022View details →

dryad40/100

Data for: Multi-omics analysis identifies symbionts and pathogens of blacklegged ticks (Ixodes scapularis) from a Lyme disease hotspot in southeastern Ontario, Canada

Open the record for dataset details and reuse information.

publicNov 2022View details →

dryad36/100

Multi-omics analysis reveals the glycolipid metabolism response mechanism in the liver of Genetically Improved Farmed Tilapia (GIFT, Oreochromis niloticus) under hypoxia stress

Background: 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, Oreochromis niloticus). Results: 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. Conclusions: 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.

opencc-zeroNov 2020View details →

dryad36/100

Data from: Multi-omics analyses on rheumatoid arthritis in CD4+ T cells

Objective: 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. Methods: We profiled genome-wide variants, gene expression, and DNA methylation in CD4+ 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. Results: 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. Conclusion: 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+ T cells in RA patients, reinforcing the importance of a multi-dimensional approach in disease-relevant tissues.

opencc-zeroJan 2021View details →

zenodo36/100

Multi-omics reveals the attenuation of metabolic cardiomyopathy in mice by extracts from Clausena0 lansium (Lour.) by transiting gastrointestinal microbiota to an alternative homeostasis

The raw data for MS "Multi-omics reveals the attenuation of metabolic cardiomyopathy in mice by extracts from Clausena0 lansium (Lour.) by transiting gastrointestinal microbiota to an alternative homeostasis".

opencc-by-4.0Oct 2023View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record