Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,108
datasets available to search
ShareScore release 0.9.0
Dataset results
1,108 results for “Metabolomics”
Unveiling the substrate-dependent dynamics of mycotoxin production in Fusarium verticillioides using an OSMAC-metabolomics approach
<p>UHPLC-HRMS raw data files from the article : "Unveiling the substrate-dependent dynamics of mycotoxin production in Fusarium verticillioides using an OSMAC-metabolomics approach"</p>
Intracellular Chiral Metabolome in Pediatric Leukemia
<p><span>Aberrations in the immunoglobulin heavy chain (IGH) locus are associated with poor prognosis in pediatric precursor B-cell acute lymphoblastic leukemia (BCP-ALL) patients. The primary objective of this pilot study is to enhance our understanding of the IGH phenotype by exploring the intracellular chiral metabolome. </span><span>Leukemia cells were isolated from the bone marrow of BCP-ALL pediatric patients at diagnosis and at the end of induction therapy (EIT). The samples’ metabolome and transcriptome were characterized using untargeted chiral metabolomic and next-generation sequencing transcriptomic analyses. </span><span>For the first time D- amino acids were identified in the leukemic cells' intracellular metabolome from the bone marrow niche. Chiral metabolic signatures at both diagnosis and EIT were indicative of a resistant phenotype. </span><span>Through integrated network analysis and Pearson correlation, confirmation was obtained regarding the association of the IGH phenotype with several genes linked to poor prognosis. </span><span>The findings of this study have contributed to the understanding that the chiral metabolome plays a role in the poor prognosis observed in an exceptionally rare patient cohort. </span><span>The findings include elevated D-amino acid incorporation in the IGH group, the emergence of several unknown, potentially enantiomeric, metabolites, and insights into metabolic pathways that all warrant further exploration. </span></p>
Figure 3. Designing a in Omics in Weed Science: A Perspective from Genomics, Transcriptomics, and Metabolomics Approaches
Figure 3. Designing a metabolomics study. (A) The various approaches for performing a metabolomics experimental study. GC-MS, gas chromatography–mass spectrometry; HILIC-LC-MS/MS, hydrophilic interaction chromatography for liquid chromatography–tandem mass spectrometry; LC-MS/MS, liquid chromatography–tandem mass spectrometry. (B) The general metabolomics workflow. It involves formulating a biological question, setting up an experimental design to test the hypothesis, sample treatment and harvest, metabolite extraction, clean-up, chromatographic separation, identification, statistical validation, and functional interpretation.
Figure 1 in Omics in Weed Science: A Perspective from Genomics, Transcriptomics, and Metabolomics Approaches
Figure 1. Classical systems biology concept and omics organization. The central dogma of molecular biology covers the progressive functionalization of the genotype to the phenotype. The omics techniques track and capture various molecular entities across the biological system.
Metabolomics of pediatric fatty liver disease
<p>Fecal and plasma metabolite profiles of non-alcoholic fatty liver disease (NAFLD) in a pediatric cohort</p>
Metabolomic Profiling of Zinc Accumulating Prostate Cancer Cells: dataset of metabolomics and transctiptomics
<p>In this study, we focused on the metabolism of prostate cancer cells forced to accumulate zinc. Because levels of metabolites involved in Krebs and methionine cycle can participate in non-metabolic processes such as changes in gene expression, a panel of 371 genes connected with key steps of carcinogenesis was designed and expression levels of these genes were assessed to determine, which pathways are changed due to the long-term zinc treatment.</p> <p>As a model of prostate cancerogenesis, wild-type and zinc accumulating cell lines PNT1A, 22Rv1, and PC-3 were used. Metabolite profiles were examined using liquid chromatography triple quadrupole mass spectrometry. Quantification of total intracellular zinc was performed by atomic absorption spectrometry and gene expression investigated by cDNA microarray. </p> <p>For description of creation of zinc-resistant cell lines see Holubova et al, Metallomics 2014, DOI 10.1039/C4MT00065J</p> <p> </p> <p><strong>Description of dataset</strong></p> <p>Total 6 files are included:</p> <p><em>Krebs.metabolites.csv</em>: table of metabolomic data (in ppm) of wild type/untreated/zinc-resistant cells.</p> <p><em>Krebs.metabolites.medium.csv</em>: table of metabolomic data (in fold change compared to medium) in cultivation media of abovementioned cells.</p> <p><em>RNA_array_fold_p.csv</em>: processed results of microarray displayed as mean log2 fold change (resistant - WT) and p level</p> <p><em>RNA_array_raw_22Rv1.csv</em>, <em>RNA_array_raw_PC-3.csv, RNA_array_raw_PNT1A.csv</em> raw data from microarray reader for WT and resistant cells.</p>
Antileishmanial compounds isolated from Psidium guajava L. using a metabolomic approach
<p>With an estimated annual incidence of 1 million cases, leishmaniasis is one of the top five vector-borne diseases. Currently available treatments involve side effects, including toxicity, non-specific targeting, and resistance development. Thus, new antileishmanial chemical entities are of the utmost interest to fight against this disease. The aim of this study was to obtain potential antileishmanial natural products from <em>Psidium guajava</em> leaves using a metabolomic workflow. Several crude extracts from <em>P. guajava</em> leaves harvested from different locations Lao People’s Democratic Republic (Lao PDR) were profiled by liquid chromatography coupled to high-resolution mass spectrometry and evaluated for their antileishmanial activities. The putative active compounds were highlighted by multivariate correlation analysis between the antileishmanial response and chromatographic profiles of <em>P. guajava</em> mixtures. The results showed that the pooled apolar fractions from <em>P. guajava</em> were the most active (IC<sub>50</sub> = 1.96±0.47 µg/mL). Multivariate data analysis of the apolar fractions highlighted a family of triterpenoid compounds, including jacoumaric acid (IC<sub>50</sub> = 1.318±0.59 µg/mL) and corosolic acid (IC<sub>50</sub> = 1.01±0.06 µg/mL). Our approach allowed the identification of antileishmanial compounds from the crude extracts in only a small number of steps steps and can be easily adapted for use in the discovery workflows of several other natural products.</p>
Stable-isotope resolved metabolomics in macrophages
Open the record for dataset details and reuse information.
Raw data used in the manuscript titled "Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using 1H HR MAS NMR Spectroscopy "
<p>The folder contains raw data used in the manuscript titled "Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using <sup>1</sup>H HR MAS NMR Spectroscopy ".</p> <p> </p> <p> Raw data measured on Bruker Avance III 400 MHz NMR spectrometer:</p> <p>- 1D <sup>1</sup>H HR MAS NMR spectra (path: <em>Patient_code – Sample_code/500/fid</em>)</p> <p>- 2D <sup>1</sup>H-<sup>1</sup>H J-resolved HR MAS NMR spectra (path: <em>Patient_code – Sample_code/600/ser</em>).</p> <p> </p> <p>Metadata is included in <em>Metadata.xlsx</em> file.</p> <p>Each sample is described with the following parameters:</p> <p>- patient code (after anonymization),</p> <p>- sample code (the label <em>l</em> or <em>r</em> denotes the <em>left</em> or <em>right</em> ovary in patients from whom samples were obtained bilaterally),</p> <p>- sample weight,</p> <p>- clinic-pathological parameters (such as: age, BMI, menopausal status, diagnosis, FIGO stage),</p> <p>- percentage tissue content obtained from histopathological analysis after HR MAS NMR studies (cancer cells, epithelial compartment within benign tumors, necrosis, inflammation, fibrosis, calcification, normal ovary, vessels, fatty tissue).</p> <p> </p> <p>Some samples were considered representative of particular tissue components:</p> <p>- cancer (HGSOC) compartment,</p> <p>- fibrotic stroma within malignant (HGSOC) tumors,</p> <p>- fibrotic stroma within benign tumors,</p> <p>- normal ovary tissue (the samples collected from the control group),</p> <p>- normal ovary tissue (the samples collected from the cancer patients),</p> <p>- necrosis,</p> <p>- non-tumoral fibrous tissue / fibrous tumor capsule (obtained from the patients with benign non-neoplastic lesions)</p> <p>- corpus albicans</p> <p>The assignment of the samples to these categories is indicated in the column <em>Tissue components.</em></p> <p><em> </em></p> <p>The samples classified as outliers in PCA model 1 are indicated in the column <em>Outliers</em>.</p> <p>The samples included in multivariate models are indicted in the columns: <em>PCA 2, PCA 3, PCA 4, PCA 5, PCA 5a, PCA 6, OPLS-DA 1, OPLS-DA 2, OPLS-DA 3, OPLS-DA 4, OPLS-DA 5, OPLS-DA 6 and OPLSR.</em></p> <p><em> </em></p>
Fingerprint Matrix Files for "Machine Learning-based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics"
<p>These files are the necessary dataset to reproduce the observed machine learning metrics in the paper "Machine Learning-based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics" in review at the Journal of Natural Products. </p> <ul> <li>Multiclassifier_23_Drug_Class_Train-Test_Fingerprint_Matrix.tsv is the accumulated positive training set for the 23 different classes demonstrated in the training and testing sets.</li> <li>Negative_Train-Test_Fingerprint_Matrix.tsv is the negatives training and testing examples derived from the RIKEN NP Depo which represent a diverse set of natural product compounds that serve as the counter points to the positive examples.</li> <li>GNPS_23_Drug_Class_Fingerprints_Matrix.tsv is the dataset of fingerprints generated from the publically available GNPS MSMS dataset. These training examples serve to confirm the ability of the machine learning model to generalize to experimental data. </li> <li> Negative_Train-Test_Fingerprint_Matrix.tsv is the dataset of negative training examples derived from the publically available spectra from the GNPS dataset. It is composed of nearly 2,800 random MSMS spectra to compose a diverse negative evaluation set. </li> <li>Random_GNPS_Fingerprints.tsv is the dataset of fingeprints of 9,443 random spectra from GNPS used to evaluate the false positive rate of each model.</li> </ul>
Metabolomics in childhood obesity, insulin resistance, and related susceptibility factors
<p>Metabolomics data from plasma and red blood cell (RBC) samples collected from a population-based cohort of children with obesity and healthy controls</p>
Merging metabolomics and genomics provides a catalog of genetic factors that infuence molecular phenotypes in pigs linking relevant metabolic pathways
<h3>Content</h3> <p>Metabolites included in the study. Summary statistics of metabolite levels for the Large White and Duroc pig populations are provided.</p>
Plasma NMR metabolomic of rainbow trout fed diets with different levels of marine and plant ingredients.
<p>The NMR and biochemical datasets used in the manuscript draft entiteld "Intestinal microbiota in rainbow trout, <em>Oncorhynchus mykiss</em>, fed diets with different levels of marine and plant ingredients: A correlative approach with some plasma metabolites."</p> <p>François-Joël Gatesoupe1*, Benoît Fauconneau1, Catherine Deborde2,3**, Blandine Madji Hounoum1,2, Daniel Jacob2,3,Annick Moing2,3, Geneviève Corraze1, Françoise Médale1. 1 UMR 1419 NuMeA, INRA, Université de Pau et des Pays de l’Adour, Saint Pée sur Nivelle, France 2 Bordeaux Metabolome Facility, CGFB, MetaboHUB, Centre INRA Nouvelle -Aquitaine-Bordeaux, Villenave d'Ornon, France 3 UMR1332 Biologie du Fruit et Pathologie, Centre INRA Nouvelle -Aquitaine-Bordeaux, Villenave d'Ornon, France<br> *Corresponding author Joel.Gatesoupe@partenaire-exterieur.ifremer.fr</p> <p>**NMR dataset Corresponding author catherine.deborde@inra.fr</p> <p> </p> <p> </p> <p> </p>
Proteomics and metabolomics data associated with the end-of-life phenotype Smurf
<p>Data obtained from whole bodies of mated females of genotype Drs-GFP at 20 and 40 days for proteomics and 30 days for metabolomics.</p> <p>The data are cited in the following preprint :</p> <p>Smurfness-based two-phase model of ageing helps deconvolve the ageing transcriptional signature</p> <p>Flaminia Zane, Hayet Bouzid, Sofia Sosa Marmol, <a href="http://orcid.org/0000-0003-2448-4022"> View ORCID Profile</a>Savandara Besse, Julia Lisa Molina, <a href="http://orcid.org/0000-0002-9579-5250"> View ORCID Profile</a>Céline Cansell, Fanny Aprahamian, <a href="http://orcid.org/0000-0001-6356-1006"> View ORCID Profile</a>Sylvère Durand, Jessica Ayache, <a href="http://orcid.org/0000-0001-7709-2116"> View ORCID Profile</a>Christophe Antoniewski, <a href="http://orcid.org/0000-0002-6574-6511"> View ORCID Profile</a>Michael Rera</p> <p>doi: https://doi.org/10.1101/2022.11.22.517330</p>
Chromosome-scale genome assembly and insights into the metabolome and gene regulation of leaf color transition in an important oak species, Quercus dentata
<p><em>Quercus dentata</em> Thunb., a dominant forest tree species in northern China, has significant ecological and ornamental value due to its adaptability and beautiful autumn coloration, with color changes from green to yellow into red resulting from the autumnal shifts in leaf pigmentation. However, the key genes and molecular regulatory mechanisms for leaf color transition remain to be investigated. First, we presented a high-quality chromosome-scale assembly for <em>Q. dentata</em>. This 893.54 Mb sized genome (contig N50=4.21 Mb, scaffold N50=75.55 Mb; 2n=24) harbors 31,584 protein-coding genes. Second, our metabolome analyses uncovered pelargonidin-3-O-glucoside, cyanidin-3-O-arabinoside, and cyanidin-3-O-glucoside as the main pigments involved in leaf color transition. Third, gene co-expression further identified the MYB-bHLH-WD40 (MBW) transcription activation complex as central to anthocyanin biosynthesis regulation. Notably, transcription factor (TF) <em>QdNAC </em>(<em>QD08G038820</em>) was highly co-expressed with this MBW complex and may regulate anthocyanin accumulation and chlorophyll degradation during leaf senescence through direct interaction with another TF, <em>QdMYB </em>(<em>QD01G020890</em>), as revealed by our further protein-protein and DNA-protein interaction assays. Our high-quality genome assembly, metabolome and transcriptome resources further enrich <em>Quercus </em>genomics, and will facilitate upcoming exploration of ornamental values and environmental adaptability in this important genus.</p>
Dataset: 1H NMR metabolomic study of auxotrophic starvation in yeast using Multivariate Curve Resolution-Alternating Least Squares for Pathway Analysis
<p>This dataset contains the set of 1H NMR data used in https://doi.org/10.1038/srep30982.</p> <p>Yeast was grown in five different liquid media and their metabolism was characterized at 6 different time-points during 24 h.</p> <p>The media used were YSC (Yeast nitrogen base Synthetic Complete) and four Drop-Out (DM) medium that do not contain one of the following nutrients (L-histidine, L-leucine, L-methionine and uracil). Since the used yeast strain does not encode in its genome some genes relative to the biosynthesis of these four nutrients, some gene de-regulations process will occur, detectable at the metabolome level.</p> <p>In this study, we have characterized the metabolome using <sup>1</sup>H NMR spectroscopy, detecting more than 40 metabolites, and the evolution of this metabolome along the measured time-points was described by application of PCA, ASCA and MCR-ALS chemometric methods.</p>
Dataset: Comparative analysis of 1H NMR and 1H–13C HSQC NMR metabolomics to understand the effects of medium composition in yeast growth
<p>NMR datasets used in https://doi.org/10.1021/acs.analchem.8b01196.</p> <p>In the corresponding study, we have performed a comparative chemometric analysis between untargeted <sup>1</sup>H NMR and <sup>1</sup>H-<sup>13</sup>C HSQC NMR analyses of metabolomics samples from <em>Saccharomyces cerevisiae</em> (yeast) extracts. Specifically, yeast was grown in two different liquid media and their metabolism was characterized at 8 different time-points of a 3-day period. The two media used, YPD (Yeast Peptone Dextrose) and YSC (Yeast nitrogen base Synthetic Complete), are broadly used in yeast lab routines, and results from this analysis should be of interest for improving lab methodologies involving yeast.</p>
Proteomic and metabolomic analysis of COVID-19 nasal swabs
<p>The epithelial barrier's primary role is to protect against entry of foreign and pathogenic elements. Global and targeted approaches were applied to nasal swabs from healthy and COVID-19-confirmed cases within 24 hours post-positive-confirmation and at 3 weeks post-infection to observe changes in proteome and metabolome.</p> <p>We found that the tryptophan/kynurenine metabolism pathway is a pinch-point regulator of canonical and non-canonical transcription activation, macrophage release of cytokines and significant changes in the immune and metabolic status with increasing severity and disease course.</p>
Metabolomics data from Li et al., 2022 BioRxiv
<p>Metabolomics data from Li et al., 2022 BioRxiv</p> <p>Prerpint: https://www.biorxiv.org/content/10.1101/2022.07.04.498697v1 </p>
Molecular, biochemical and metabolomics analyses reveal constitutive and pathogen-induced defense responses of two sugarcane contrasting genotypes against leaf scald disease
<p>Leaf scald caused by the bacteria <em>Xanthomonas albilineans</em> is one of the major concerns to sugarcane production. To breed for resistance, mechanisms underlying plant-pathogen interaction need deeper investigations. Herein, we evaluated sugarcane defense responses against <em>X. albilineans</em> using molecular and biochemical approaches to assess pathogen-triggered ROS, phytohormones and metabolomics in two contrasting sugarcane genotypes from 0.5-144 h post-inoculation (hpi). In addition, the infection process was monitored using TaqMan-based quantification of <em>X. albilineans</em> and the disease symptoms were evaluated in both genotypes after 15 d post-inoculation (dpi) The susceptible genotype presented a response to the infection at 0.5 hpi, accumulating defense-related metabolites such as phenolics and flavonoids with no significant defense responses thereafter, resulting in typical symptoms of leaf scald at 15 dpi. The resistant genotype did not respond to the infection at 0.5 hpi but constitutively presented higher levels of salicylic acid and of the same metabolites induced by the infection in the susceptible genotype. Moreover, two subsequent pathogen-induced metabolic responses at 12 and 144 hpi were observed only in the resistant genotype in terms of amino acids, quinic acids, coumarins, polyamines, flavonoids, phenolics and phenylpropanoids together with an increase of hydrogen peroxide, ROS-related genes expression, indole-3-acetic-acid and salicylic acid. Multilevel approaches revealed that constitutive chemical composition and metabolic reprogramming hampers the development of leaf scald at 48 and 72 hpi, reducing the disease symptoms in the resistant genotype at 15 dpi. Phenylpropanoid pathway is suggested as a strong candidate marker for breeding sugarcane resistant to leaf scald.</p>
ScienceDex guides
Understand access before you commit
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.