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1,108 results for “Metabolomics”
Two metabolomics data sets (mouse kidney, mouse plasma), generated for the publication Bignon et al., 2023: "Multiomics reveals multilevel control of renal and systemic metabolism by the renal tubular circadian clock".
<p><strong>Publication: </strong>Bignon Y, Wigger L, Ansermet C, Weger BD, Lagarrigue S, Centeno G, Durussel F, Götz L, Ibberson M, Pradervand S, Quadroni M, Weger M, Amati F, Gachon F, Firsov D. Multiomics reveals multilevel control of renal and systemic metabolism by the renal tubular circadian clock. J Clin Invest. 2023 Mar 2:e167133. doi: 10.1172/JCI167133. Epub ahead of print. PMID: 36862511.</p> <p> </p> <p><strong>Abstract: </strong> Circadian rhythmicity in renal function suggests rhythmic adaptations in renal metabolism. To decipher the role of the circadian clock in renal metabolism, we studied diurnal changes in renal metabolic pathways using integrated transcriptomic, proteomic, and metabolomic analysis performed on control mice and mice with inducible deletion of the circadian clock regulator Bmal1 in the renal tubule (cKOt). With this unique resource, we demonstrated that ~30% RNAs, ~20% proteins and ~20% metabolites are rhythmic in kidneys of control mice. Several key metabolic pathways including NAD+ biosynthesis, fatty acid transport, carnitine shuttle,and b-oxidation displayed impairments in kidneys of cKOt, resulting in a perturbed mitochondrial activity. Carnitine reabsorption from the primary urine was one of the most impacted processes with a ~50% reduction in plasma carnitine levels and a parallel systemic decrease in tissues carnitine content. This suggests that the circadian clock in the renal tubule controls both kidney and systemic physiology.</p> <p> </p> <p><strong>This record contains two separate mass-spectrometry metabolomics data sets associated with this study:</strong></p> <ol> <li>Metabolic profile of renal tubules, MS/MS data, Metabolon, Morrisville, NC (N=60)</li> <li>Metabolic profile of blood plasma, MS/MS data, Biocrates, Innsbruck, Austria (N=60)</li> </ol> <p>For each data set, original data as received from the platforms and processed data as used in the data analysis are provided. Preprocessing of kidney data included removal of metabolites with more than 80% missing data values, median normalization, imputation and glog2 transformation. Preprocessing of plasma data included filtering of metabolites with any missing data and log2 transformation. Details of data processing are available in the STAR*methods of the publication.</p> <p> </p> <p><strong>Data sets in other repositories associated with the same study:</strong></p> <p>Additional data sets (transcriptomics, proteomics) pertaining to the same study have been deposited in public repositories:</p> <ul> <li>Gene Expression Omnibus (NCBI GEO), GSE216252</li> <li>PRIDE Archive (EMBL-EBI), PXD036803</li> </ul> <p> </p>
Processed metabolomic data from the EXPOsOMICS Personal Exposure Monitoring study
<p>Metabolomic data from the 'Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study' paper <a href="https://doi.org/10.1021/acs.est.3c03233">DOI: 10.1021/acs.est.3c03233</a> . </p> <p>The data was originally collected and generated by the multicenter EXPOsOMICS Personal Exposure Monitoring study. Details on data collection and processing are described in the aforementioned paper. The statistical analysis from that paper is available at <a href="https://github.com/moosterwegel/variability-metabolites-paper">https://github.com/moosterwegel/variability-metabolites-paper</a> and may contain useful information/code to work with this data.</p> <p>`processed_covariate_data.csv`:<br> ```<br> Rows: 298<br> Columns: 7<br> $ subjectid: hashed identifier subject<br> $ sample_code: indicates if it's the first (A) or second (B) blood sample<br> $ centre: indicates in which centre the data was collected<br> $ age_cat: indicates age category at the time of a PEM session<br> $ sq_sex: indicates the sex of the participant (male, female) as filled in during the screening questionaire<br> $ traf: indicates the exposure to traffic (PM2.5 and UFP) as measured during the PEM sessions. <br> $ bmi_cat: indicates BMI category at the time of a PEM session<br> ```</p> <p>`processed_lcms_data data.csv` contains the processed LCMS data:<br> ```<br> Rows: 298<br> Columns: 4297<br> $ subjectid: hashed identifier subject<br> $ sample_code: indicates if it's the first (A) or second (B) blood sample<br> $ centre: indicates in which centre the data was collected<br> $ compounds: measured features (compounds) are prefixed by the letter X. The name contains information on the measured monoisotopicmass_retentiontime.<br> Non-detects (below limit of detection (LOD) are coded as 1 for the compounds.<br> ....<br> ```<br> In the datasets each row indicates a measurement on a day (`sample_code`) and person (`subjectid`). The datasets can be joined on these variables.</p> <p>The other data files (`annotations.xslx`, `ancestors_annotations.xlsx`, `annotations_plus_kegg_pathways.csv`) contain the annotations, ancestors of the annotations (to assign a class to a compound based on ChEBI ontology, see our paper for details), annotations plus KEGG pathways respectively. </p>
Freshwater mussel metabolomics of Yahara Lakes, Madison, WI USA
Metabolomic profiles of unionids (Lampsilis siliquoidea) under varying loads of zebra mussels (Dreissena polymorpha) in a eutrophic lake chain in Wisconsin, USA. Metabolites were sourced from hemolymph.
Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning
<p>COVID-19 plasma samples spectrometry datasets for machine learning input. Used in the work of article Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning, currently under submittion.</p> <p>Abstract:</p> <p>COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study with 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) was enrolled from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules that are related to the disease’s pathophysiology and several discriminating features to patient’s health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings the disease’s pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using Machine Learning algorithms, transforming this screening approach in a tool with great potential for real-world application. </p>
HERMES: a molecular formula-oriented method to target the metabolome - Dataset
<p>This dataset contains all raw LC-MS1 and LC-MS2 data from river <em>s</em>urface water, <em>Escherichia coli, </em> and human plasma used in the paper <em>HERMES: a molecular formula-oriented method to target the metabolome, </em>as well as the RMarkdown vignettes generating the results and base figures.</p> <p>Please refer to the README file for more information about the data organization and script reproducibility.</p> <p>A collection of ready-to-use molecular formula databases can be found <a href="https://zenodo.org/record/5025560">in this Zenodo dataset.</a></p>
Circadian ontogenetic metabolomics atlas: an interactive resource with insights from rat plasma, tissues, and feces
<p>LC–MS instrumental files in mzXML format for metabolomics (HILICp, HILICn, HSST3p, HSST3n) and lipidomics platforms (LIPp, LIPn), including metadata for study samples, method blanks, quality control samples, and serial dilution samples. The instrumental files were acquired for each LC–MS platform as part of a study focused on creating a circadian ontogenetic metabolomics atlas of rat plasma, tissues, and feces. The original paper is accessible at http://doi.org/10.1007/s00018-025-05783-w</p>
Comprehensive 16s rRNA sequencing and metabolomics to investigate the effect of anticancer bioactive peptides combined with oxaliplatin on gastric cancer
<p>背景: 胃癌的发生、发展与肠道菌群密切相关。既往研究发现抗癌生物活性肽(ACBP)与奥沙利铂(OXA)联合对胃癌有显着的治疗作用,但ACBP-OXA对肠道菌群的影响仍不清楚。</p><p><strong>Methods:</strong> We established a nude mouse model of ACBP-OXA combined therapy for gastric cancer, the diversity of gut microbiota and fecal metabolomics were studied, and the correlation between gut microbiota and metabolites was analyzed.</p><p><strong>Results: </strong>ACBP-OXA联合疗法对肠道菌群具有很强的调节作用。16s rRNA研究发现,在门中,ACBP-OXA处理后,厚壁菌门和拟杆菌门的相对丰度发生显着变化,厚壁菌门的相对丰度下降,拟杆菌门的相对丰度增加。属内,ACBP-OXA组中毛螺菌科NK4AB6组的相对丰度降低,odpribacter和拟杆菌属的相对丰度增加。ACBP组乳酸菌相对丰度增加,ACBP-OXA和OXA组葡萄球菌相对丰度下降。GO和KEGG研究发现联合治疗机制与代谢和免疫有关。通过代谢组学研究,本研究发现差异代谢物与Benzenoids、Ligans、neoligans、其中脂质和脂类大多参与酪氨酸代谢、不饱和脂肪酸生物合成、苯丙氨酸代谢α-生物过程。将代谢组学与16s rRNA长寿素相结合,发现氨基酸相关代谢物与Jetgalilicus、Staphylococcus、Proteiniphilum等细菌属相关。</p><p>结论: ACBP与ACBP-OXA联合治疗可能通过改变肠道菌群的分布多样性和菌群结构来改善和恢复胃癌裸鼠的肠道菌群,这可能是抑制胃癌发生、发展的关键。该研究为进一步研究ACBP-OXA在胃癌治疗中的应用提供了新的方向。</p>
Metabolomics Analysis for the Identification of Biomarkers in Small Cell Lung Cancer
<p>Small cell lung cancer (SCLC), a highly aggressive malignancy with a poor prognosis is usually detected at the extensive stage of the disease. The demand for early diagnostic methods and reliable biomarkers is increasing, although a number of tumor markers such as NSE and NCAM have already been utilized in clinics. Here, we conducted untargeted metabolomics in 54 plasma samples from 34 patients with SCLC and 20 healthy controls.</p>
Metabolomics Peak Tables of the publication "Screening of leaf extraction and storage conditions for eco-metabolomics studies"
<p>This dataset is a metabolomics study of maize extracts. The dataset consists of peak tables (.csv files) and MS/MS fragment patterns (.mgf files) both exported from MetaboScape. Additionally the results of a Principal Component Analysis are provided for the LLE optimisation part of the dataset.</p> <p>All further details are available in the publication in Plant Direct: https://doi.org/10.1002/pld3.578</p>
Metabolomics WorkBench Compound Dictionary
<p>mwTAB file for each study in the Metabolomics Workbench (https://www.metabolomicsworkbench.org/) was processed through an automated data merging pipeline. It yielded -</p> <p> </p> <ul> <li>~237,333 unique chemical names</li> <li>~13,810 PubChem CIDs</li> <li>~14,298 KEGG IDs</li> <li>~5,130 CAS Numbers</li> <li>- 147 unique species</li> <li>- 175 unique sample type</li> </ul> <p> </p> <p>Note: workbench_curated_compound_list.csv contains some of the curated compound names. Basic curation has happened to connect metabolites names to PubChem identifiers. </p> <p> </p>
Metabolomics Data Dictionary (Metabolon Inc. ) - from PMC articles (OA)
<p>A collection of metabolite and chemical names reported by Metabolon Inc. in the supplementary tables of open-access full-text articles from the PMC database.</p> <p><strong>Many entries can be duplicate because of chemical name variants reported in different articles. </strong></p>
Untargeted leaf metabolomes of Noccaea praecox and N. caerulescens
<p>The dataset represents untargeted leaf metabolomes of Noccaea caerulescens from the un-polluted site Lokovec (Slovenia), N. praecox from the same site, and N. praecox from metal-enriched site Zerjav (Slovenia).</p>
Differential impact of impaired steryl ester biosynthesis on the metabolome of tomato seeds and fruits
<p>Steryl esters (SE) are a storage pool of sterols that accumulates in cytoplasmic lipid droplets and helps to maintaining plasma membrane sterol homeostasis throughout plant growth and development. Ester formation of plant SE is catalyzed by phospholipid:sterol acyltransferase (PSAT) and acyl-CoA:sterol acyltransferase (ASAT), which transfer long-chain fatty acid groups to free sterols from phospholipids and acyl-CoA, respectively. Comparative mass spectrometry-based metabolomic analysis between ripe fruits and seeds of a tomato (Solanum lycopersicum cv Micro-Tom) mutant lacking functional PSAT and ASAT enzymes (slasat1xslpsat1) shows that disruption of SE biosynthesis has a differential impact on the metabolome of these organs, including changes in the relative proportions of free and glycosylated sterols. Significant perturbations were observed in the fruit lipidome in contrast to the mild effect detected in the lipidome of seeds. A contrasting response was also observed in phenylpropanoid metabolism, which is down-regulated in fruits and appears to be stimulated in seeds. Comparison of global metabolic changes using volcano plot analysis suggests that disruption of SE biosynthesis favors a general state of metabolic activation that is more evident in seeds than fruits. Interestingly, there is an induction of autophagy in both tissues, which may contribute along with other metabolic changes to the phenotypes of early seed germination and enhanced fruit resistance to Botrytis cinerea displayed by the slasat1xslpsat1 mutant. The results of this study reveal unreported connections between SE metabolism and the metabolic status of plant cells, and lay the basis for further studies aimed at elucidating the mechanisms underlying the observed effects.</p> <p> </p> <p>Data: </p> <p>W1-54; AxP LC polar.zip: raw files LC-polar</p> <p>W1-1 (1)-(54); AxP LC lipid.zip: raw files lipid LC</p> <p>GCtomato.zip: raw files GC polar</p> <p>spreadsheet (.csv) with sample IDs</p>
RECETOX Metabolome HR-[EI+]-MS library
<p>The RECETOX Mass Spectrum Reference Libraries is a collection of MS spectra collected from authentic compounds. Each library comprises the spectra in MSP format and an accompanying SDF database of compounds. The collection is shared under terms & conditions of <a href="https://creativecommons.org/licenses/by-nc/4.0/">CC-BY-NC</a>.<br> <br> The RECETOX Metabolome HR-[EI+]-MS library is a collection of mostly endogoenous compounds from MetaSci Human Metabolite Library. Analytes underwent methoximation/silylation prior to acquisition. Spectra were acquired at 70 eV on Thermo Fisher Q Exactive™ GC Orbitrap™ GC-MS/MS at 60000 resolving power.</p>
MetaPro: a web-based metabolomics application for MS data batch inspection and library curation
<p>MetaPro is a metabolomics web analysis platform built on the Aird data format with high performance and high compression. This platform includes a series of necessary functions for metabolomics analysis such as quality control, retention time(RT) alignment, target analysis, untarget analysis, manual integration, batch inspection, MS2 library establishment, and report export, providing efficient data analysis, management and visualization capabilities</p>
Datasets for "Comparison of multivariate ANOVA-based approaches for the determination of relevant variables in experimentally designed metabolomic studies"
<p><em><strong>Raw files of the LC-MS designed experiments.</strong></em></p> <p> </p> <p>* <strong>YEAST GROWTH DATASET</strong></p> <blockquote> <p><strong>A) Phospholipids extraction</strong></p> <p>- FosfoB1.raw</p> <p>- FosfoB3.raw</p> <p>- FosfoC2.raw</p> <p>- FosfoD1.raw</p> <p>- FosfoE1.raw</p> <p>- FosfoE2.raw</p> <p>- FosfoE3.raw</p> </blockquote> <p> </p> <blockquote> <p><strong>B) Sphingolipids extraction</strong></p> <p>- EsfingoB1.raw</p> <p>- EsfingoB2.raw</p> <p>- EsfingoC1.raw</p> <p>- EsfingoC2.raw</p> <p>- EsfingoC3.raw</p> <p>- EsfingoD1.raw</p> <p>- EsfingoD2.raw</p> <p>- EsfingoD3.raw</p> </blockquote> <p> </p> <p><strong>* ZEBRAFISH DATASET</strong></p> <blockquote> <p><strong>A) BPA study</strong></p> <p>Low BPA exposure</p> <p>- Low_BPA_A.d</p> <p>- Low_BPA_B.d</p> <p>- Low_BPA_C.d</p> <p>High_BPA_exposure</p> <p>- High_BPA_A.d</p> <p>- High_BPA_B.d</p> <p>- High_BPA_C.d</p> <p>Controls</p> <p>- Control_BPA_A.d</p> <p>-Control_BPA_B.d</p> <p>- Control_BPA_C.d</p> </blockquote> <p> </p> <blockquote> <p><strong>B) Estradiol study</strong></p> <p>Low E2 exposure</p> <p>- Low_E2_A.d</p> <p>- Low_E2_B.d</p> <p>- Low_E2_C.d</p> <p>High_E2_exposure</p> <p>- High_E2_A.d</p> <p>- High_E2_B.d</p> <p>- High_E2_C.d</p> <p>Controls</p> <p>- Control_E2_A.d</p> <p>-Control_E2_B.d</p> <p>- Control_E2_C.d</p> </blockquote> <p> </p> <p><strong>Funding:</strong> This research was funded by the Spanish Ministry of Science and Innovation (MCI, Grant CTQ2017-82598-P) and Severo Ochoa Project CEX2018-000794-S (funded by MCIN/AEI/ 10.13039/501100011033), and supported from the Catalan Agency for Management of University and Research Grants (AGAUR, Grant 2017SGR753). MPC was funded by a predoctoral FPU 16/02640 scholarship from the Spanish Ministry of Education and Vocational Training (MEFP). The 1290 LC system and 6545 XT QTOF instrumentation were provided to DS as gifts by Agilent Technologies through their Thought Leader program.</p>
Chemical shift imaging by NMR metabolomics
<p>This repository contains the point-wise binned NMR spectra as well as the data analysis R-code for the study "Chemical shift imaging by NMR metabolomics using in tube extraction and slice selection." by Silvio Waschina (Kiel University), Karsten Seeger (University of Lübeck).</p>
Growth and metabolome data of Saccharomyces uvarum grown in synthetic wine must with different nitrogen sources
<p><em>Raw data: Metabolome of S. uvarum (Su) and S. cerevisiae (Sc) in wine fermentations in 13 different nitrogen conditions. Concentrations of compounds expressed in mg/L at 60 g/L CO2 sampling point and at the end of fermentation. The volatile compounds are grouped according to the chemical functional group (ethyl esters, acetate esters, higher alcohols, medium chain fatty acids (MCFA) and branched-chain fatty acids (BCFA)). The central carbon metabolites (CCM) and sugars conform the last group. Each value is the mean of three biological replicates. The raw data is reported in a processed form in the manuscript entitled “The growth and metabolome of Saccharomyces uvarum in wine fermentations is strongly influenced by the route of nitrogen assimilation” </em></p>
Spanish melon landraces: revealing useful diversity by genomic, morphologic, and metabolomic analysis. Supplementary data.
<p>Original data linked to the publication Spanish melon landraces: revealing useful diversity by genomic, morphologic, and metabolomic analysis. It includes, Supp. Table 1: Genomic data; Supp. Table 2: Characterization data; Supp. Table 3: sugar and acids data; Supp Table Supp. Table 4: Voaltile organic compounds data; Supp Table 5: Germplasm details; Supp. table 6: Cromatographic parameters</p>
High-throughput metabolomics for the design and validation of a diauxic shift model
<p>Untargeted metabolomics on ten different regulatory strains in <em>Saccharomyces cerevisiae, </em>(BY4741). Samples were taken before and after the diauxic shift, to investigate regulatory consequences of gene deletions and their roles during the substantial metabolic reconfiguration that is the diauxic shift. The analysis of samples was performed on an Agilent UHPLC-qTOF-MS system which consisted of a 1290 II Infinity series UHPLC system with a 6550 UHD iFunnel accurate-mass qTOF spectrometer.</p> <p>Data-set used in: <a href="https://www.nature.com/articles/s41540-023-00274-9">High-throughput metabolomics for the design and validation of a diauxic shift model</a></p>
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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.