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1,168 results for “metabolite”
S71 | CECSCREEN | HBM4EU CECscreen: Screening List for Chemicals of Emerging Concern Plus Metadata and Predicted Phase 1 Metabolites
<p>This is the collection associated with list S71 CECSCREEN HBM4EU CECscreen: Screening List for Chemicals of Emerging Concern Plus Metadata and Predicted Phase 1 Metabolites<strong> </strong>on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>CECScreen is part of the HBM4EU project (coord. UBA) > WP16 "emerging chemicals" (lead INRA, JP Antignac/L Debrauwer) > Task 16.1 (lead IRAS, J Vlanderen / R Vermeulen) > Main contributor (J Meijer) > Involved Partners (M Lamoree, T Hamers, S Hutinet, A, Covaci, C Huber, M Krauss, DI Walker, EL Schymanski). Further details in Meijer et al (2021) DOI: <a href="https://doi.org/10.1016/j.envint.2021.106511">10.1016/j.envint.2021.106511</a>. Dataset DOI: <a href="https://doi.org/10.5281/zenodo.3956586">10.5281/zenodo.3956586</a>.</p> <p>Update 23/7/2020 (v0.1.1): updated MetFrag files to remove elements causing errors (Os, Pd, Ag, Be). Update 8 Nov 2022 (v0.1.2) removed new lines in several synonyms as detected at BioHackEU22.</p>
Software and suspect database for: "A large scale multi-laboratory suspect screening of pesticide metabolites in human biomonitoring: From tentative annotations to verified occurrences"
<p>This upload contains the pesticide suspect list aggregated among the laboratories of work package 16 of the HBM4EU (https://www.hbm4eu.eu) project for a large-scale pesticide suspect screening and the resolving search templates for each pesticide. Additionally, we provide the used software version of MetAlign applied in this screening.</p>
Alteromonas macleodii MIT1002 growth on and uptake of Prochlorococcus-derived metabolites
This data package contains the results from a series of experiments designed to test the response of a heterotrophic, copiotrophic, gammaproteobacterium, Alteromonas macleodii strain MIT1002, to a range of metabolites released by the phytoplankton Prochlorococcus. A. macleodii MIT1002 was isolated from co-culture with Prochlorococcus, and so we hypothesized that A. macleodii MIT1002 would be able to grow on the full range of substrates tested. Instead, we found that A. macleodii MIT1002 could only grow on a narrow range of substrates, and data suggest that this substrate specificity may be related to transporter specificity. We performed two types of experiments: growth experiments and uptake experiments. Data from growth experiments are labeled with the name of the substrate being tested (e.g., “Leu,” for leucine, or “3m2ob”, for 3-methyl-2-oxobutanoic acid). For these experiments, we grew A. macleodii MIT1002 on either pyruvate (as a positive control), a selected Prochlorococcus-related substrate, or a mix of pyruvate and the metabolite. We measured growth in 96-well plates by OD600 using a plate reader which took a measurement every 0.5h for 48h. Uptake experiments are labeled with either “KHU7” (an experiment which tested A. macleodii MIT1002 growth on and uptake of 3-methyl-2-oxobutanoic acid) or “KHU8” (an experiment which tested the A macleodii MIT1002 growth on and uptake of 3-methyl-2-oxopentanoic acid -or lack thereof). For these experiments, we measured growth by flow cytometry. We measured the change in dissolved (i.e., extracellular) metabolite concentration by filtering samples, extracting organic carbon from the filtrate by solid phase extraction, and quantifying selected metabolites from the filtrate by targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS). This data package includes the peak areas for targeted metabolites generated by LC-MS/MS, the peak areas for our standard curves used for quantification, and the dissolved metabolite
Variant, Metabolite and Source Data for: Population genomics uncover loci for trait improvement in the indigenous African cereal tef (Eragrostis tef)
<p>These files contain the variant and metabolome for a collection of 220 tef (<em>Eragrsotis tef)</em> accessions from an ethiopian diversity panel. The accessions were assembled and managed by the Ethiopian Institute of Agricultural Research (EIAR, Ethiopia). The variant data was produced at the John Innes Centre (UK). The metabolome data was produced at Aberystwyth University (UK). These dataset are described in Jones et al. (2024), <em>bioRxiv</em>, https://doi.org/10.1101/2024.09.30.615331. The source data for main figures in the publication are also included.</p> <p>The submission contains</p> <ol> <li>EIAR_filtered.vcf.gz: This is the variant data obtained from alignment of Illumina reads from all 220 teff accessions to the reference assembly of tef (Dabbi). Low quality variants were filtered out. This variant data was used for constructing the phylogenetic relationship between the accessions. The samples names corresponds to the DNA code in Supplementary Table S10 (Jones et al, 2024).</li> <li>pooled_EIAR_filtered.vcf.gz: After the phylogentic analysis described above, reads from accessions that were found to be genetically redundant were pooled before variant calling. This file was used for the SNP GWAS analysis. The samples names corresponds to the DNA code in Supplementary Table S10 (Jones et al, 2024).</li> <li> Metabolite_Profile.xlxs (source data for Figure 5): This file contains m/z feature intensities from untargeted metabolite fingerprinting using Flow Infusion Electrospray High-resolution Mass Spectrometry (FIE-HRMS). The sample names contains a combination of Location code and Plot number in Supplementary Table S10 e.g AT plot 1, CD plot 1, DZ plot 1, where AT, CD and DZ represent Alem Tena, Chefe Donsa and Debre Zeit, respectively. The data was used for the partial least squares discriminant analysis and differentially accumulated metabolites analysis presented in Figure 5.</li> <li>Source data: Numerical source data for graphs and charts in Figures 3 - 7.</li> <li>Tsedey TT2 Sequence from Improved Assembly: The 4A and 4B sequences around the TT2 orthologue in tef from the improved PacBio-based chromosome-scale assembly of tef. These sequences were used for plotting the LTR Copia alignments presented in Supplementary Figure 9. We thank Corteva for pre-publication access to this improved Tsedey genome assembly.</li> </ol>
S60 | SWISSPEST19 | Swiss Pesticides and Metabolites from Kiefer et al 2019
<p>This is the collection associated with list S60 SWISSPEST19 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>Swiss pesticides (plant protection products) and metabolites from Kiefer et al 2019 (Eawag), Tables SI-B 1 and 2, DOI: <a href="https://doi.org/10.1016/j.watres.2019.114972">10.1016/j.watres.2019.114972</a></p> <p>Update 25 April 2020: fixed many naming issues in xlsx and csv file. No structural information changed. 25 Mar 2023: fixed date CAS. 25 May 2023: fixed non-live CIDs to <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/commit/56abd8e2bacbbb6969c961101df792189d605bfd">live CIDs</a>. 6 Jul 2023: added <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/commit/3ff26d704ad9ac831a5d276256c1c89020fb2e02">NOA 413161</a> structures and transformations file. 8 April 2025: fixed three CIDs that went non-live to match CIDs from NORMAN deposition. Mismatch of InChIKeys due to difference in treatment of the C=N-O groups between PubChem and Open Babel; files now match PubChem's output. 2 Jun 2025: updated TFA synonym. 31 Aug 2025: fixed triazole alanine structure for one entry (removing incorrect CID <span><a href="https://pubchem.ncbi.nlm.nih.gov/compound/139597001"><span>139597001</span></a></span>).</p>
Zebrafish Pathway Metabolite MetFrag Local CSV
<p>This is a local CSV file of Zebrafish metabolites for MetFrag (https://msbi.ipb-halle.de/MetFrag/) extracted from PubChem, based partially on previous data extracted from Wikipathways, KEGG and literature (DOI: <a href="https://doi.org/10.1371/journal.pone.0213661">10.1371/journal.pone.0213661</a>), combined in previous versions of this record (DOI: <a href="https://doi.org/10.5281/zenodo.3541624">10.5281/zenodo.3541624</a>).</p> <p>This file was created as documented on the <a href="https://gitlab.com/uniluxembourg/lcsb/eci/pubchem-docs/-/tree/main/taxonomy/Danio_rerio">ECI GitLab</a>. </p> <p>This file is designed for identification using MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p> <p> </p>
Metabomatching: Using Genetic Association to Identify Metabolites in Proton NMR Spectroscopy. CoLaus Pseudospectra.
<p>Summary statistics between urine NMR metabolome features and genotypes in the CoLaus cohort. Used as test pseudospectra for metabomatching, a method for metabolite identification using genetic spiking.</p>
Metabomatching: Using Genetic Association to Identify Metabolites in Proton NMR Spectroscopy. SHIP Pseudospectra.
<p>Summary statistics between urine NMR metabolome features and genotypes in the SHIP cohort. Used as test pseudospectra for metabomatching, a method for metabolite identification using genetic spiking.</p>
BioDeep/metabolomics-report-standards: BioDeep LC-MS Metabolite Identification Demo Report
<p><em>A Metabolomics unknown feature identification report industry standards from <a href="http://www.bionovogene.com/">BioNovoGene</a> corporation.</em></p> <p>2019.08.16# at Suzhou, China</p> <p>There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, enables the reinterrogation and comparison of data by others, which is also could let us interpret the result in a more clearly way.</p> <p>This article is mainly address at the unknown metabolite identification in LC-MS experiment, and proposes the reporting standards related to the chemical analysis aspects of metabolomics experiments its metabolite identification.</p> <p>Some terms in this article that address to:</p> <ul> <li>feature, the term feature in this article is refer to a parent ion in LC-MS experiment result raw data. Where a parent ion feature is a peak in chromatography data, which is consist of mass to charge ratio in ms1 level and its retention time (with a range of lower bound and upper bound) in chromatography experiment result.</li> <li>annotation, the term annotation in this article is refer to the multidimensional information about the metabolite that assigned to a unknown feature, which such multidimensional information consist with the metabolite its cross reference id in different database, common name, basic chemical data like mass and formula composition and its molecule structure information, etc.</li> <li>alignment, the term alignment means a kind of operation that use to compare the similarity of the mass spectrum data between user sample and the reference standard library. Such similarity comparison result is the most important evidence that use for unknown feature its identification.</li> <li>score, the term score is a kind of numeric value that produced by the alignment comparison calculation. Literally, the higher score the alignment it produce, the better the result it is.</li> </ul> <p>Our metabolite identification report consist with two parts of data which present to our user:</p> <ol> <li>Report excel table that contains the raw sample information and the meta annotation information of the metabolite.</li> <li>Data visual plot for the mass spectrum alignment details.</li> </ol>
WormJam Metabolites Local CSV for MetFrag
<p>This is a local CSV file of WormJam (https://www.tandfonline.com/doi/full/10.1080/21624054.2017.1373939) for MetFrag (https://msbi.ipb-halle.de/MetFrag/).</p> <p>The text file provided by Michael (also part of this dataset) was modified into CSV by adding identifiers and adjusting headers for MetFrag import. </p> <p>This CSV file is for users wanting to integrate WormJam into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p> <p>Update 10 Sept 2019: curated truncated InChIKey, InChI entries, added missing SMILES, added DTXSIDs by InChIKey match.</p>
S75 | CyanoMetDB | Comprehensive database of secondary metabolites from cyanobacteria
<p>This is the collection associated with list S75 CyanoMetDB Comprehensive database of secondary metabolites from cyanobacteria on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>CyanoMetDB is a comprehensive database of secondary metabolites from cyanobacteria manually curated from primary references described in Jones et al (2021), DOI: <a href="https://doi.org/10.1016/j.watres.2021.117017">10.1016/j.watres.2021.117017</a> (preprint DOI: <a href="https://doi.org/10.1101/2020.04.16.038703">10.1101/2020.04.16.038703</a>). This upload contains the 2024 release. Please cite Jones et al (2021) DOI: <a href="https://doi.org/10.1016/j.watres.2021.117017">10.1016/j.watres.2021.117017</a> and this record Janssen et al (2024) DOI: <a href="https://doi.org/10.5281/zenodo.13854577">10.5281/zenodo.13854577</a> when using this CyanoMetDB Version 3!</p> <p><em><strong>Contents: </strong></em></p> <p><em><strong>CyanoMetDB XLSX database (2024 release): <a href="https://zenodo.org/records/13854577/files/CyanoMetDB_Version03.xlsx?download=1">CyanoMetDB_Version03.xlsx</a></strong></em></p> <p>Additional files for workflows:</p> <p>CSV format: <a href="https://zenodo.org/records/13854577/files/CyanoMetDB_V03_2024.csv?download=1">CyanoMetDB_V03_2024.csv</a><br>MetFrag local CSV file (original database abridged and reformatted for use in MetFrag): <a href="https://zenodo.org/records/13854577/files/CyanoMetDB_V03_2024_MetFrag.csv?download=1">CyanoMetDB_V03_2024_MetFrag.csv</a><a href="https://zenodo.org/api/files/7d71e4a9-e5f2-4ca3-ac55-6467a356ab9a/CyanoMetDB_MetFrag_Feb2021.csv"> </a><br>Additional files for matching InChIKeys (rapid suspect flagging): <a href="https://zenodo.org/records/13854577/files/CyanoMetDB_V03_2024_InChIKeys.txt?download=1">CyanoMetDB_V03_2024_InChIKeys.txt</a></p> <p>Corresponding author: Elisabeth Janssen (Eawag): <a href="mailto:Elisabeth.Janssen@eawag.ch">Elisabeth.Janssen@eawag.ch</a></p>
Fecal glucocorticoid metabolite levels of American pika (Ochotona princeps) and habitat characteristics of their associated territories found in rock glaciers adjacent to Niwot Ridge and within Rocky Mountain National Park, 2018 - 2019.
To understand whether stress-associated hormones vary with metrics of habitat quality, we measured fecal glucocorticoid metabolite (FGM) levels in the American pika (Ochotona princeps), a small mammal with well-defined habitat (talus), that can vary in quality depending on the presence of rock ice features (RIFs). In 2018, we sampled pika scat from two types of RIFs: “active” rock glaciers thought to harbor subsurface ice recently, and “fossil” rock glaciers considered long devoid of subsurface ice (as classified by Janke 2005, 2007). Specifically, fecal pellets were collected from pika territories located in rock glaciers within eight sites along the Front Range of Colorado: four in Rocky Mountain National Park (2 active, 2 fossil) and four adjacent to Niwot Ridge (2 active, 2 fossil) (pika_fecal_glu_rg.aw.csv). To account for possible seasonal variation in pika FGM, scat samples were collected in the alpine spring and fall. To understand other influences of habitat quality on FGMs, we also measured fine-scale habitat differences between rock glaciers in 2019, including talus depth, clast size, and land cover metrics related to forage (pika_fecal_habitat_rg.aw.csv).
Antihypertensive drug metabolite screening toy dataset for MS/MS application
<p><strong>Objectives :</strong></p> <p>Detect and visualize antihypertensive drug metabolites in untargeted metabolomics experiments</p> <p><strong>Cohort :</strong></p> <p>6/26 patients on antihypertensive therapy</p> <p><strong>Mass spectrometer :</strong></p> <p>Thermo Q-Exactive coupled to pHILIC chromatography using data dependent analysis (DDA) MS/MS gas-phase experiments</p>
Plant metabolites modulate animal social networks and lifespan
<p><span>Social interactions influence disease spread, information flow, and resource allocation across species, yet heterogeneity in social interaction frequency and its fitness consequences remain poorly understood. Additionally, animals can utilize plant metabolites for purposes beyond nutrition, but whether that shapes social networks is unclear. Here, we investigated how non-nutritive plant metabolites impact social interactions and the lifespan of the turnip sawfly, <em>Athalia rosae</em>. Adult sawflies acquire neo-clerodane diterpenoids ('clerodanoids') from non-food plants, showing intraspecific variation in natural populations and laboratory-reared individuals. Clerodanoids can also be transferred between conspecifics, leading to increased agonistic social interactions. Network analysis indicated increased social interactions <span>in sawfly groups where some or all individuals had prior access to clerodanoids</span>. Social interaction frequency varied with clerodanoid status, with fitness costs including reduced lifespan resulting from increased interactions. Our findings highlight the role of intraspecific variation in the acquisition of non-nutritional plant metabolites in shaping social networks, with fitness implications on individual social niches.</span></p>
Metabolites_ CSF
<p>This dataset was obtained using CSF samples of the Harvard Biomarker Study (HBS). HBS is a case-control study including 3,000 patients with variousneurodegenerative diseases as well as healthy controls (HC).</p> <p>Human CSF was analyzed with BASF Metabolome Solutions GmbH. </p> <p>Data were used in the preprint: Genetic screening and metabolomics identify glial adenosine metabolism as a therapeutic target in Parkinson’s disease</p> <p><span>doi:</span> https://doi.org/10.1101/2024.05.15.594309</p> <h1></h1>
Exploring the Exclusive Isolation of Pseudomonas syringae in Peltigera Lichens via metabolite analysis and growth assays - Appendix
<p>Lichen samples from Iceland were collected from the genera Peltigera, Cladonia, and Stereocaulon in March 2023 at Heidmork forest, Oskjuhlid hill, and the shores of Ellidaa in Arbaejarstifla. All specimens underwent morphological analysis, and corresponding vouchers have been deposited at the Icelandic Institute of Natural History.</p>
Data for "Detection of metabolite-protein interactions in complex biological samples by high-resolution relaxometry: towards interactomics by NMR"
<p>Raw NMR data for relaxometry experiments, divided by donor sample. For every donor sample 2 or 3 different samples were used in order to record data at 19 different magnetic fields.</p> <p>Data from fast field-cycling relaxometry. All the data is in one xlsx file, divided by donor sample.</p> <p>Relaxometry results for alanine, lactate, creatinine and glutamine, obtained from the fitting of their relaxation decays recorded at 19 different fields, divided by donor sample.</p>
Amine metabolites in pigs fed a diet with spray dried plasma protein as functional protein source
<p><span>We evaluated the effects of diets formulated with either soybean meal (SBM) as a reference protein source or SDPP in pigs. Blood amine profiles were analysed to evaluate the effects of the diets at a systemic level. <span>Blood samples were collected via the ear-vein for plasma preparation at at dissection days (d28-29) after the morning meal ingestion. </span></span></p> <p><span>For plasma, blood samples were collected in sterile Vacuette tubes containing lithium-heparin and immediately centrifuged at 3,000x g for 10 min at 4°C and plasma was extracted. Plasma were stored at -80°C for further analysis on levels ofsystemic amine metabolite profiles. </span></p> <p>The protocol outlined in the following publication was used for detecting plasma amine levels:</p> <ul> <li>Noga MJ, Dane A, Shi S, Attali A, van Aken H, Suidgeest E, et al. Metabolomics of cerebrospinal fluid reveals changes in the central nervous system metabolism in a rat model of multiple sclerosis. <span><span><span>Metabolomics. 2012;8(2):253-63.</span></span></span></li> <li><span><span><span>van der Kloet FM, Bobeldijk I, Verheij ER, Jellema RH. </span></span></span>Analytical Error Reduction Using Single Point Calibration for Accurate and Precise Metabolomic Phenotyping. Journal of Proteome Research. 2009;8(11):5132-41.</li> </ul>
WormJam-DB Combined Metabolites
<p>This is a collection of the unique metabolites in the WormJam Consortium "<a href="https://github.com/wormjam-consortium/wormjam-db">wormjam-db</a>" repository, created by curating the "<a href="https://github.com/wormjam-consortium/wormjam-db/blob/master/literature_combined/unique_metabolites.txt">unique_metabolites</a>" file in the literature-combined subfolder and the "<a href="https://github.com/wormjam-consortium/wormjam-db/blob/master/prediction/WormJam_v0.1.0.txt">WormJam_v0.1.0.txt</a>" file in the prediction subfolder. The lipid file only contained formulas. Several issues were fixed with these files prior to merging, see curation notes. </p> <p>This file is shared with the same permissions as the <a href="https://github.com/wormjam-consortium/wormjam-db">wormjam-db</a> GitHub repository and was created primarily for upload and integration into PubChem as part of a separate <a href="https://gitlab.com/uniluxembourg/lcsb/eci/pubchem-docs/-/tree/main/taxonomy/Celegans"><em>C. elegans</em></a> effort, to fill in the gaps in their taxonomy pages.</p>
Data associated with "Microbiota-derived metabolites inhibit Salmonella virulent subpopulation development by acting on single-cell behaviors"
<p>Data used for the publication Microbiota-derived metabolites inihibit Salmonella virulent subpopulation development by acting on single-cell behaviors. </p> <p> </p> <p>all_hi_2307202.csv Single-cell quantifications of Salmonella SPI-1 reporter cells grown in the presence of SCFAs.</p> <p>all_no_2307202.csv Single-cell quantifications of Salmonella SPI-1 reporter cells grown in the absence of SCFAs.</p> <p>odmeasurements.csv OD measurements of plate-reader assays of Salmonella SPI-1 reporter cells and controls grown in a range of SCFA conditions. </p> <p>gfpmeasurements.csv GFP measurements of plate-reader assays of Salmonella SPI-1 reporter cells and controls grown in a range of SCFA conditions. </p>
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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.
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.