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1,108 results for “Metabolomics”
mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis
<p>All the data for 'mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis'</p> <p>sim.zip is stimulated data for intensity cutoff 0.05. simxcms.csv is peak intensity profiles for their simulated peaks.</p> <p>sim3.zip are simulated data for normal/leading/tailing peaks with tailing factor of 1, 0.8, and 1.5, respectively.</p> <p>All the csv files begin with sim3 are extracted peaks list from the sim3.zip with corresponding data analysis software.</p> <p>csv.zip recorded the m/z, retention time, intensity, and compounds name for simulated compound for each condition (sim.zip and sim3.zip).</p> <p>sep1.mzML: simulation for 8 isomers with similar m/z while different retention times. 7 peaks are non baseline separation peaks. Peaks profile is saved in spe1.csv file.</p> <p>xcms.csv, mzmine.csv, openms.csv: peaks found in sep1.mzML by xcms, mzmine 4.5 and openms, respectively.</p> <p>R code: <a href="https://github.com/yufree/democode/blob/master/meta/simfin.R">https://github.com/yufree/democode/blob/master/meta/simfin.R</a></p> <p>Website of mzrtsim package: https://yufree.github.io/mzrtsim/</p>
Moderate associations between the use of levonorgestrel-releasing intrauterine device and metabolomics profile; Supplementary Figures
<p>Supplementary Material for the article "Moderate associations between the use of levonorgestrel-releasing intrauterine device and metabolomics profile", in the Journal of Clinical Endocrinology and Metabolism</p>
Datasets of "Differences in the stool metabolome between vegans and omnivores: analyzing the NIST stool reference material" publication
<p>To gain confidence in results of omic-data acquisitions, methods must be benchmarked by validated quality control materials. We here report data combining both untargeted and targeted metabolomics assays for the analysis of four new human fecal reference materials developed by the U.S. National Institute of Standards and Technologies (NIST) for metagenomics and metabolomics measurements. These reference grade test materials (RGTM) were established by NIST based on two different diets and two different samples treatments: homogenized fecal matter from subjects eating vegan diets, stored and submitted in either lyophilized (RGTM 10162) or aqueous form (RGTM 10171); secondly, homogenized fecal matter from subjects eating omnivore diets, stored and submitted in either lyophilized (RGTM 10172) or aqueous form (RGTM 10173). We used four untargeted metabolomics assays (lipidomics, primary metabolites, biogenic amines and polyphenols) and one targeted assay on bile acids.</p>
Untargeted metabolomics analysis of RPE cells during six month in culture
<p>Primary RPE cell cultures were established with human fetal RPE cells acquired from ScienCell (Cat. No, 6540) seeded at passage 3 (P3) in 12-well Transwell<sup>®</sup> inserts (Corning Inc., Cat. No. CLSS3460) coated with 2% v/v Geltrex<sup>®</sup> matrix (Thermo Fisher Scientific; Cat. No. A1413202). Cells were cultured for a total time of 6 months (25 weeks), following the same protocol as previous works (16,59). Samples were acquired for protein immunolocalization, transcriptomic and metabolomics analysis from independent cell cultures along the total 6 months culture time (specifically at 4, 12, 17 and 25 weeks in culture), collecting 3 biological replicates per type of analysis and time point.<em> </em>Cell metabolites were quenched and extracted using MeOH:H<sub>2</sub>O (80:20, -20ºC) containing a spiked solution of isotopically enriched low molecular mass internal standard<strong>. </strong>Quality control (QC) samples were prepared by creating a pool of equal volumes from each biological replicate and were analyzed after a blank solution every fifth sample to monitor the performance, stability, and reproducibility of the LC-MS run. A liquid chromatography (LC) system 1290 Infinity II (Agilent Technologies) was coupled to an Agilent 6560B Ion Mobility quadrupole-time-of-flight mass spectrometer (IM-QTOF-MS) equipped with an Agilent G1607A dual jetstream ESI source and the MassHunter WorkSation 11.0. A reference solution containing purine and hexakis(1H,1H,3H-tetrafluoropropoxy)phosphazene for mass correction was added using the second ESI source. Chromatographic, ion source and MS conditions were optimized using QCs and selected parameters are shown in Supporting Information. MS analysis was conducted with an untargeted approach, operating the instrument in both positive and negative ionization modes. All samples were analysed in a randomized order. Raw data was processed using the software Profinder B10.00 (Agilent) and refined data were exported as CEF files<strong>. </strong>Data were converted to mzml files using MS convert.</p>
Effect of sticky rice germ oil droplet spraying on chrysanthemum thrips resistance and metabolome
<p>This dataset contains experimental results from full plant assays with Chrysamthemum plants that were conducted to test the effectiveness of sprayng solutions containing sticky rice oil droplets for trapping of small arthropods on plants. The experiments were conducted at the Institute of Biology Leiden, Leiden University the Netherlands.</p> <p>The first dataset contains the results of the full plant assays with thrips.</p> <p>The second dataset contains the results of 1H NMR and GC-MS signals of leaf samples of sprayed chrysanthemum plants.</p> <p> </p> <p>Version history:</p> <p>Version 2: Included the RAW data on % coverage of plants for the two plant assays that had been left out during earlier submission</p> <p>Updated the metadatasheets within the excel files to be more complete.</p> <p>Version 3: Included a new excel sheet in the GC-MS and NMR data file in which a subset of the RAW HS-GC-MS and 1H NMR data, namely those peaks and delta signals that were identified and matchedd to compound id after untargeted analysis, are presented together with the name of the compounds or classes of compounds as mentioned in the manuscript.</p> <p>No changes were made to the plant assay data file</p> <p> </p> <p>In the "Dataset_TBierman_RGO_thrips_1HNMR_GC-MS_V3" excel file:</p> <p>Sheets: "Processed 1H NMR data" and "Processed HS-GC-MS data"</p> <p>contain processed 1H NMR and GC-MS data of chrysanthemum leaves, harvested after 10 or 25 days, of plants that were sprayed with water or vegetable-oil derived adhesives and infested with thrips or not.</p> <p>Sheet: "Quantitative data selected comp" contains a subset of the data where signals were found significant in the untargeted analysis have been annotated to their compound identity.</p> <p>In the "Dataset_TBierman_RGO_thrips_plantassay1_and_2_V3" excel file:</p> <p>Sheets "Plant_assay_1_RGO_thrips_d10_25" and "Plant_assay_2_RGO_thrips_d25" contain the raw plant assay data</p> <p>Sheets "Plant_assay_1_RGO_coverage" and "Plant_assay_2_RGO_coverage" contain the summary values of the estimated coverage with adhesive oil droplets of each respective experiment on the left side while on the right side the raw data is presented </p> <p> </p>
Strawberry volatile organic compounds metabolomic data and QTL study
<p>This dataset contains the supplementary materials of the publication "Multivariate QTL approach reveals a major regulator of terpenoid production and other volatiles in strawberry" of the same authors. In this study we extracted volatile organic compounds from several strawberry samples and analysed their identity and abundance. We used this volatile data to perform an extensive multivariate QTL study, the results of which can be found in this dataset.</p> <p>All analysis, results and figures can be reproduced using the folder included in the <strong>supplementary data 1</strong>. If you want to reproduce part or all of our analysis, only download sup data 1. </p> <p>If you only need one of our results or data table you can download them individually:</p> <ul> <li>Sup data 2: abundances of volatile organic compounds from a biparental and diverse panel (GWAS) population.</li> <li>Sup data 3 and 4: p-value tables for all metabolites as well as multivariate traits (see publication for more information).</li> <li>Sup table 1: Summary of metabolite abundances and heritabilities across both populations.</li> <li>Sup tables 2 and 3: significant QTL signals for each trait individually and summarised per QTL locus.</li> <li>Sup table 4: previously reported VOC QTLs in strawberry, with positions imputed in the Royal Royce genome.</li> <li>Sup table 5: metadata about all the identified compounds on this and previous studies.</li> <li>Sup table 6: SNP array positions imputed in the "Royal Royce" genome assembly.</li> <li>Sup table 7: Number of markers per chromosome in this analysis.</li> </ul> <h3>Update 2025</h3> <p>We updated the underlying code and datasets to reflect several revisions made to this work. Most notably, the QTL results have been reworked. They now do not include Blink or FarmCPU results (only mixed model results, obtained through statgenGWAS). Additionally, heritability estimations, QQ-plots and other figures have been added to the reproducible results code.</p>
SpatialMETA: A Novel Framework for Integrating Spatial Transcriptomics and Metabolomics Data
<p>Multimodal analysis of spatial transcriptomics (ST) and spatial metabolomics (SM) has rapidly advanced for characterizing tissue microenvironments. However, integrating ST and SM data remains challenging due to differing morphologies, resolutions, and batch effects. We developed SpatialMETA (Spatial Metabolomics and Transcriptomics Analysis), a novel method for integrating spatial multi-omics data, which aligns ST and SM to a unified resolution, enables both cross-modal and cross-sample integration to identify ST-SM associated spatial patterns, and provides extensive visualization and analysis capabilities. The datasets for SpatialMETA is avaiable. </p>
MetabolACE-Metabolomics & Lipidomics Dataset 1
<p>Analysis of intact lipids in Mock, Scramble and NAA40-KD cells using high-resolution mass spectrometry</p>
Spectral Libraries for Metabolome Annotation Workflow (MAW)
<p>MassBank saved at 2022-09-12 10:28:52 with release version 2022.06 as mbankNIST.rda (MsBackendMsp)<br> GNPS saved at 2022-09-12 13:37:42 as gnps.rda (MsBackendMsp)<br> HMDB saved with the release version 4 as hmdb.rda (MsBackendHmdb)</p> <p>All .rda files can be reloaded into R session using the respective Backends. These databases were created for MAW version 1.</p> <p>hmdb_dframe_str.csv is downloaded from HMDB Downloads for structural information on HMDB IDs present in the HMDB version 4 spectral data.</p>
Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline
<p>This upload contains the HZV029 Plasma and HZV029 Two-Phase dataset for reviewers of the "Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline" submission. </p> <p>Both datasets will be uploaded to metabolomics workbench and the upload completed before final publication of the manuscript. For the he HZV029 Plasma datasets only the final run is included for any sample (i.e., failed injections or other samples with data quality issues that were reran during acquisition were omitted).</p> <p>Also included in the upload is the source code for the MetDataModel and the pcpfm at the time of manuscript re-submission and the pcpfm itself. If you find this upload in the future, please check out the github repos for more updated versions:</p> <p>https://github.com/shuzhao-li-lab/PythonCentricPipelineForMetabolomics</p> <p>https://github.com/shuzhao-li-lab/metDataModel</p> <p>The github repo does not store the input the data for space reasons, they only have the notebooks. However, the .zip here has both the notebooks by themselves in the notebook subdirectory and a separate directory with the notebooks and the data used to generate all the figures and results in the manuscript.</p> <p><strong>Some information that is needed to rerun this analysis:</strong></p> <p>Sequence files are critical to the functioning of the pipeline. The sequence files for all analyses are provided under sequence_files.zip. These can be used to recapitulate the analysis by eitehr changing the filepath to each acquisition to where you put it on your sytem or by placing the sequence file in the same directory as the mzml or raw. In the latter case, the pipeline will search for filenames matching the sample names. The sequence files also store some sample metadata such as the type of sample a given acquisition is (unknown, pooled, qc, etc...)</p> <p>.raw to .mzML conversion works well on MacOS but may not work well on other systems. You will need to use the ability to specify your own conversion command or convert files outside of the pipeline. </p> <p>To replicate the results, you do need to have the annotation sources downloaded which can be done using the pipeline. MS2 annotation requires the files in the AcquireX directory which is MS2 acquisitions on pooled HZV029 plasma samples.</p> <p>For the comparison between MetaboAnalystR and the pcpfm, subsets of the datasets were used. These subsets and the sequence files are in Subsets_for_performance_testing.zip. The sequences are also in the sequence_files directory as well</p> <p>The notebooks reference data in the analysis folders. Copies of these files are located with the notebooks to ease reproduction of the exact results in the paper; however, to do so, you will need to change paths to this data in the notebook. This lets the notebooks be ran during a rerun without copying intermediates back and forth and it keeps the github repo clean.</p> <p><strong>Version History:</strong></p> <p>This version is after reviewer comments and is for resubmission.</p> <p> </p> <p><strong>Contributions:</strong></p> <p>Joshua M Mitchell implemented the pipeline and was first author on the manuscript. Shuzhao Li is the corresponding author on the manuscript. </p> <p>Maheshwor Thapa performed the experiments to collect the HZV029 data. Yuanye Chi helped with testing and documenting the pipeline. </p> <p>Jiangou (Jeff) Xia and Zhiqiang Pang provided the R portion of the analysis. </p>
Soil pH, developmental stages and geographical origin differently influence the root metabolomic diversity and root-related microbial diversity of Echium vulgare from native habitats
<p>R Studio codes and ASV table used to analyze the microbiome data of our Echium vulgare microbial ecology experiment. </p>
Interstage single ventricle heart disease infants show dysregulation in multiple metabolic pathways: targeted metabolomics analysis - Data
<p>The data in this Zenodo entry corresponds to the data used to produce the results in <a href="https://www.jacc.org/doi/full/10.1016/j.jacadv.2022.100169">https://www.jacc.org/doi/full/10.1016/j.jacadv.2022.100169</a>. The zipped folder contains three files</p> <ul> <li>Metabolite Data.csv - The meatobilte measurements for all the samples</li> <li>Clinical Data.csv - Values for the clinical variables</li> <li>Clinical Data Descriptions.csv - More in depth explanation of clinical variables as well as possible values of the variables</li> </ul> <p><span>This study was supported by the American Heart Association (AHA</span><span>20CDA35310498 and AHA18IPA34170070) and the National Institutes </span><span>of Health (NIH/NCATS Colorado CTSA, No. UL1 TR001082 and NIH/</span><span>NHLBI K23HL12363</span></p>
Raw metabolomics data of the paper: New findings in the metabolism of the saffron apocarotenoids, crocins and crocetin, by the human gut microbiota
<p>Raw dataset of the metabolomicas data of the study : New findings in the metabolism of the saffron apocarotenoids, crocins and crocetin, by the human gut microbiota.</p> <p>The csv file contain the raw data matrix exported from MS-DIAL software after total ions aligment across all study samples.</p>
Metabolome and proteome dataset from yeast kinase knock-outs
<p>The dataset comprised of processed data produced in Zelezniak at al, Cell Systems 2018 study, please see README.txt for the detailed description of files. </p> <p> </p> <p> </p>
Compartment and Hub Definitions Tune Metabolic Networks for Metabolomic Interpretations
<p>This archive contains data for a report by the same title.<br> Data relate to software projects MetaboNet and DyMetaboNet.<br> MetaboNet: https://github.com/tcameronwaller/metabonet<br> DyMetaboNet: https://github.com/tcameronwaller/dymetabonet</p> <p>File descriptions</p> <p>dymetabonet_2019-08-29.mp4 ... raw screen capture video of DyMetaboNet<br> dock_metabonet_2019-08-18.zip ... complete MetaboNet export<br> model_* ... curation of human metabolic model by MetaboNet<br> model_dymetabonet.zip ... format for DyMetaboNet<br> model_compartments* ... compartments<br> model_processes* ... processes<br> model_reactions* ... reactions<br> model_metabolites* ... metabolites<br> measurement_* ... curation of metabolomic measurements by MetaboNet<br> measurement_study_*_report.tsv ... summary of match measurements to metabolites<br> measurement_study_*.tsv ... metabolites' fold changes and probabilities between groups<br> measurement_study_*_metaboanalyst.txt ... format for MetaboAnalyst<br> measurement_study_*_metaboanalyst_pair.txt ... format for MetaboAnalyst with sample pairs<br> network_* ... multiple definitions of metabolic networks<br> network_compartments-true_hubs-true.zip ... compartmental network with hubs<br> network_compartments-true_hubs-false.zip ... compartmental network without hubs<br> network_compartments-false_hubs-true.zip ... noncompartmental network with hubs<br> network_compartments-false_hubs-false.zip ... noncompartmental network without hubs<br> network_compartments-*_hubs_*/network_cytoscape.json ... format for Cytoscape<br> network_compartments-*_hubs_*/network_networkx.pickle ... format for NetworkX<br> network_compartments-*_hubs_*/nodes_reactions.pickle ... network's nodes for reactions<br> network_compartments-*_hubs_*/nodes_metabolites.pickle ... network's nodes for metabolites<br> network_compartments-*_hubs_*/links.pickle ... network's links<br> network_compartments-*_hubs_*/analysis/nodes_reactions.tsv ... nodes' metrics relative to reactions<br> network_compartments-*_hubs_*/analysis/nodes_metabolites.tsv ... nodes' metrics relative to metabolites<br> network_compartments-*_hubs_*/analysis/network_reactions.tsv ... network's metrics relative to reactions<br> network_compartments-*_hubs_*/analysis/network_metabolites.tsv ... network's metrics relative to metabolites<br> network_compartments-*_hubs_*/measurement/metabolites.tsv ... measurements on nodes for metabolites</p>
Metabolomics data associated with "Glial swip-10 controls systemic mitochondrial function, oxidative stress, and neuronal viability via copper ion homeostasis"
<p>Raw feature tables used for metabolomic analysis of the <em>Caenorhabditis elegans</em> mutant <em>swip-10</em>. The data were generated using liquid chromatography coupled high-resolution mass spectrometry. Two different columns were used: HILIC (+ ESI) and C18 (-ESI), coupled to a Thermo Q-Exactive Orbitrap mass spectrometer. The feature tables were generated using open-source peak peaking and alignment R packages: apLCMS and xMAanalyzer. See more details in the associated manuscript.</p>
Characterization of Metabolism Associated with Outcomes in Severe Acute Pancreatitis: Insights from Serum Metabolomic Analysis
<p>1H NMR spectra data of SAP patients (Survivors/ Non-survivors). The spectra were binned as 0.02 ppm spectral buckets. The chemical shift regions corresponding to the water region and TSP were excluded to avoid spectral interference. This dataset was used for the metabolomics related study to highlight the dysregulation of metabolites in the study group.</p> <p> </p>
Processed data used in transcriptome- metabolome-wide association study
<p>Processed_RNASeq_RPKM.txt contains RPKM levels for 45484 genes quantified in 555 individuals from RNA-Seq of lymphoblastoid cell lines (LCLs).</p> <p>Processed_NMRpeaks_baseline.txt contains binned, normalized and standardised (z-scored) NMR peak intensities for 1276 bins quantified in 555 individuals from urine samples taken at baseline. NMR spectra were acquired at 300 K on a Bruker 16.4 T Avance II 700 MHz NMR spectrometer (Bruker Biospin, Rheinstetten, Germany) using a standard 1H detection pulse sequence with water suppression. The spectra were referenced to the TSP signal and phase and baseline corrected.</p> <p>Processed_NMRpeaks_followup.txt contains binned, normalized and standardised (z-scored) NMR peak intensities for 1289 bins quantified in 315 individuals from urine samples taken during follow-up. NMR spectra were acquired with an Avance III HD 600 NMR spectrometer. Spectra were referenced to the TSP signal and phase and baseline corrected.</p> <p>More details on the data set can be found in Sönmez Flitman et al. (doi: https://doi.org/10.1101/2020.05.22.110197).</p>
Comparative metabolomics of fruits and leaves in a hyperdiverse lineage suggests fruits are a key incubator of phytochemical diversification
<p>Data files, chromatograms, and metadata for the Frontiers in Plant Science article "Comparative metabolomics of fruits and leaves in a hyperdiverse lineage suggests fruits are a key incubator of phytochemical diversification" . </p> <p>doi: 10.3389/fpls.2021.693739</p>
Anti-inflammatory compounds in probiotic yeast revealed by untargeted metabolomics
<p><strong>Abstract</strong></p> <p>The saccharomyces strain <em>Saccharomyces cerevisiae </em>var.<em> boulardii</em> has exhibited efficacy in ameliorating symptoms of gastrointestinal disorders, including inflammatory diseases. The molecular origin of the anti-inflammatory activity has remained largely elusive to this day. Earlier studies suggest a small, secreted, yet undefined, molecule as the active principle and thus an untargeted metabolomics approach towards its identification was adopted. We used LCMS-analysis to interrogate the secreted metabolome of <em>S. cerevisiae </em>var.<em> boulardii</em> in comparison to a <em>S. cerevisiae </em>reference strain. Statistical analysis of the data revealed several compounds unique to <em>S. cerevisiae </em>var.<em> boulardii</em>, that were partially annotated and confirmed by comparison with authentic standards. Furthermore, anti-inflammatory properties were experimentally assigned to several small molecules, indicating that this property of the yeast variant is due to several factors. Our data suggest that the anti-inflammatory properties of <em>S. cerevisiae </em>var.<em> boulardii</em> can be linked to the activity of small molecules in its secreted metabolome.</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.