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1,604 results for “omics”

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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

Glycomics measurements of the retrospective study of the Pain-Omics project

This deposit contains the QCed glycomics data for the patients in the retrospective study of the Pain-Omics FP7 project.

opencc-by-4.0Dec 2017View 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

Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics

The original data used in the article: Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics Delineating the spatial multiomics landscape will pave the way to understanding the molecular basis of physiology and pathology. However, current spatial omics technology development is still in its infancy. Here, we developed a high-throughput targeted in situ sequencing strategy, multiomics in situ pairwise sequencing (MiP-Seq), to efficiently decipher multiplexed DNAs, RNAs, proteins, and small biomolecules at subcellular resolution. MiP-Seq simultaneously sequenced the dual barcode base of padlock probes, dramatically increasing the detection capacity to 10N by N rounds of sequencing. We delineated spatial gene profiles in the hypothalamus using MiP-Seq. Moreover, MiP-Seq was unitized to detect tumor gene mutations and allele-specific expression of parental genes and to differentiate sites with and without the m6A RNA modification at specific sites. MiP-Seq was combined with in vivo Ca2+ imaging and Raman imaging to obtain a spatial multiomics atlas correlated to neuronal activity and cellular biochemical fingerprints. Importantly, we proposed a “signal dilution strategy” to resolve the crowded signals that challenge the applicability of in situ sequencing. Together, our method improves spatial multiomics and precision diagnostics, and facilitates analyzing cell function in connection with gene profiles.

opencc-by-4.0Jan 2023View details →

zenodo44/100

scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data

This repository contains the training data and source code to reproduce the results of our paper: scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data More description can be also found in GitHub (https://github.com/LPH-BIG/scGraph2Vec).

opencc-zeroJun 2024View 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 →

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Demo code for "ESPRESSO: Spatiotemporal omics based on organelle phenotyping": https://doi.org/10.1101/2024.06.13.598932

The files and code provided are sufficient to run end-to-end the ESPRESSO analysis, from the microscope data to the feature analysis. In the following, we provide guidance for the installation and running of the program, the output for each of the scripts are also stored in the  "ESPRESSO - Demo\Output of scripts" folder and can be consulted directly.

opencc-by-4.0Jul 2024View details →

zenodo44/100

EukZoo, an aquatic protistan protein database for meta-omics studies.

This database contain protein sequences of aquatic microbial eukaryotes, or protists. The purpose of this is to make a database that is of reasonable quality to serve as resource for both taxonomy and functional interpretation of metagenomic and metatranscriptomic studies of protists. The source of the sequences were mainly from Marine Microbial Eukaryotes Transcriptome Sequencing Project (MMETSP), and supplemented with various genomes and transcriptomes of organisms that were not a part of MMETSP. To use this database, one has to understand the main function of the three files here. (1) The protein sequences are stored in .faa file. You can build an alignment/search database out of that and search your meta-omics sequences against it. Each sequence in the FASTA file has an ID which always consists of two parts like this: "MMETSP0004_1234567". The text before the first underscore is the source ID of that sequence. (2) Taxonomy information of each source ID are stored in "EukZoo_taxonomy_table_v_0.2.tsv". One can use the information within in conjunction with database search results to assign taxonomy to sequences. (3) KEGG annotation of each sequence are stored in "EukZoo_KEGG_annotation_v_0.2.tsv". One can use the information within in conjunction with database search results to assign KEGG functional annotation (KO ID) to sequences. I also provide scripts to assign taxonomy and KEGG annotation from database search results. You can also find the scripts and explanations on how to use them on the <a href="https://github.com/zxl124/EukZoo-database">EukZoo GitHub page</a>. You will find details on how the database was created and curated on there as well. Please contact me at zhenfeng.liu1@gmail.com if you have any questions or requests. Thank you for your interest in EukZoo.

opencc-by-4.0Oct 2018View details →

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Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

Baum_et_al_2019_Supplementary_Figures.pdf: Supplementary Figures S1-S4. Legends are included under each figure. sbm-for-correlation-based-networks-master.zip: Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.

opencc-by-4.0Mar 2019View details →

zenodo44/100

Characterizing cell-type spatial relationships across length scales in spatially resolved omics data: data repository

<h1>CRAWDAD</h1> Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we develop CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package with source code and additional documentation at https://jef.works/CRAWDAD/. During CRAWDAD's development, we generated simulated datasets and new cell-type annotations for human spleen data, provided here. The external datasets such as the mouse cerebellum, mouse embryo, mouse brain, and human breast cancer data used in the paper can be found in their original publication. See more information in CRAWDAD's data availability statement. <h2>Simulated Datasets</h2> <ul> <li>sim.csv: the simulated data. Used in Figure 1 b-g, Supplementary Figure 1 a-c, and Supplementary Figure 9 a-b.</li> <li>ext_sim.csv: the extended simulated data. Used in Supplementary Figure 1 d-f.</li> <li>null_sim_visualization.csv: the null simulated data. Used to generate the plots Supplementary Figure 2 a-d.</li> <li>null_sim_1.csv - null_sim_10.csv: the 10 null simulated datasets. Used to quantitatively compare CRAWDAD, Squidpy’s co-occurrence implementation, and Ripley’s K Cross.</li> </ul> <h2>HuBMAP Datasets</h2> <ul> <li>pkhl.csv: annotated cell types and positions of sample HBM389.PKHL.936 from donor HBM966.VNKN.965. Used in Figure 5 a-h, Supplementary Figure 5 a, Supplementary Figure 7 a-c, and Supplementary Figure 8 c. doi:10.35079/HBM389.PKHL.936</li> <li>xxcd.csv: annotated cell types and positions of sample HBM772.XXCD.697 from donor HBM966.VNKN.965. Used in Figure 5 d-h, Supplementary Figure 5 a-c, and Supplementary Figure 7 a-c. doi:10.35079/HBM772.XXCD.697</li> <li>fsld.csv: annotated cell types and positions of sample HBM342.FSLD.938 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM342.FSLD.938</li> <li>pbvn.csv: annotated cell types and positions of sample HBM825.PBVN.284 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM825.PBVN.284</li> <li>ksfb.csv: annotated cell types and positions of sample HBM556.KSFB.592 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM556.KSFB.592</li> <li>ngpl.csv: annotated cell types and positions of sample HBM568.NGPL.345 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM568.NGPL.345</li> </ul> <h2>External Datasets</h2> <ul> <li>Mouse cerebellum: Used in Figure 2 a-e, Supplementary Figure 3 a-b, Supplementary Figure 4 a-d, and Supplementary Figure 8 a.</li> <li>Mouse embryo: Used in Figure 2 f-j, Supplementary Figure 3 c-d, Supplementary Figure 4 e-h, and Supplementary Figure 8 b.</li> <li>Human breast cancer: Used in Figure 3 a-c.</li> <li>Mouse brains: Used in Figure 4 a-e.</li> </ul>

opengpl-3.0-or-laterOct 2024View details →

zenodo44/100

Supplementary data for the paper "Visual integration of omics data to improve 3D models of fungal chromosomes"

<ul> <li>13 parameter files (*.YML) used by the 3DGB workflow to produce models of 3D genomes.</li> <li>13 3D genomes structures (*.PDB).</li> <li>4 animated GIF of representative structures.</li> <li>1 XLSX file that lists raw (Hi-C and ChIP-seq) data used in this study and the associated analysis.</li> </ul>

opencc-by-4.0Mar 2023View details →

zenodo44/100

CLImate for Maize OMICS: CLIM4OMICS Analytics and Database (v2.0)

CLIM4OMICS Analytics and Database is Improved database of G2F data repository that contains OMICs (genetic and phenotypic) and environmental data for maize yield predictability across 84 experimental fields in the U.S. and province of ON in Canada between 2014-2021. The goal of this pipeline is to aggregate, improve, and synthesize multi-dimensional G2F data including Geno-type, Phenotype and Environmental data for GxE modeling. This dataset contains 79,122 phenotype measurements, 378 genotypes of maize lines, environmental data of 178 locations and Python Scripts for Quality control (QC), Consistency control (CC) steps and ML models for GxE interactions. The Environmental data is extracted from NWS, DayMet and NSRDB databases and processed for QC and CC. The environmental dataset contains the minimum temperature (Tmin), average temperature (Tmean), maximum temperature (Tmax), minimum dew point (DPmin), average dew point (DPmean), maximum dew point (DPmax), minimum relative humidity (RHmin), average relative humidity (RHmean), maximum relative humidity (RHmax), minimum solar radiation (SRmin), average solar radiation (SRmean), maximum solar radiation (SRmax), accumulative rainfall (Racc), average wind speed (WSmean), and average wind direction (WDmean). This package also contains the raw G2F data and preprocessing pipeline.

opencc-by-4.0Jun 2023View details →

zenodo44/100

CLImate for Maize OMICS: CLIM4OMICS Analytics and Database

opencc-by-4.0Jan 2023View details →

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

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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