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.
1,168
datasets available to search
ShareScore release 0.9.0
Dataset results
1,168 results for “metabolites”
Fig. 4 in Anticoccidial activity of the secondary metabolites in alpine plants frequently ingested by wild Japanese rock ptarmigans
Fig. 4. The inhibitory effects of the natural components derived from alpine plants on sporozoite cell invasion. The invasion rate of sporozoites treated with each natural component derived from alpine plants or lasalocid (positive control) with the viability of the DMSO-treated group set as 100%. Each compound was used at its maximum non-toxic concentration. Las: lasalocid. The student's t-test was utilized to compare the data with the DMSO-treated group as a control (**p <0.01, ***p <0.001, ****p <0.0001). Outliers were identified and removed using Thompson's test (p <0.05).
Analyses of metabolite profiling of Drosophila Parkinson's Disease model for identifying novel glial-based therapeutic targets
<p>Analysis for genetic screening and metabolomics identify glial adenosine metabolism as a therapeutic target in Parkinson’s disease</p> <p>This project contains the analysis of metabolite abundance measurements obtained with four different liquid chromatography mass spectrometry methods of synuclein expressing or control or fly brains in a wilde type or Adk1 knockout background.</p> <p> </p> <div> <h2>Table of contents</h2> <a href="https://github.com/jravilap/Olsen_Analyses#table-of-contents"></a></div> <div> <h3>Prerequisites</h3> <a href="https://github.com/jravilap/Olsen_Analyses#prerequisites"></a></div> <ul> <li>R (version 4.3.1 or higher)</li> <li>RStudio (optional, but recommended)</li> </ul> <div> <h3>R Packages</h3> <a href="https://github.com/jravilap/Olsen_Analyses#r-packages"></a></div> <p>The following R packages are required. You can install them using the commands below:</p> <div> <pre>install.packages(c(<span><span>"</span>readxl<span>"</span></span>, <span><span>"</span>calibrate<span>"</span></span>, <span><span>"</span>dplyr<span>"</span></span>, <span><span>"</span>ggplot2<span>"</span></span>))</pre> <div> </div> </div> <div> <h3>Package versions</h3> <a href="https://github.com/jravilap/Olsen_Analyses#package-versions"></a></div> <ul> <li>ggplot2_3.5.1</li> <li>dplyr_1.1.4</li> <li>yaml_2.3.8</li> <li>calibrate_1.7.7</li> <li>readxl_1.4.3</li> </ul> <div> <h2>Project Structure</h2> <a href="https://github.com/jravilap/Olsen_Analyses#project-structure"></a></div> <ul> <li><code>code/</code>: Contains the R scripts for the analysis.</li> <li><code>data/</code>: Processed data files. <ul> <li><code>22_0322_alphaSyn_fly_pilot_Classes.xlsx</code>: metabolite profiling data</li> <li><code>dup_metabs_decision.csv</code>: Table defining which metabolites profiled in more than one method should be used.</li> </ul> </li> <li><code>results/</code>: Output files, including plots and tables.</li> <li><code>common_functions/</code>: Custom R functions used in the analysis.</li> <li><code>config.yml</code>: Configuration file for setting paths.</li> </ul>
Integrated Omics-Based Discovery of Novel Genes, Secondary Metabolites Clusters, and Small Molecules in Penicillium spp. with Disparate Fungal Isolates
<p><em><span>Penicillium expansum</span></em><span> is a ubiquitous postharvest pathogen of pome fruit that causes blue mold decay of apple fruit while another member of the genus, <em>P. chrysogenum</em><span>,</span><em> </em>is a well-studied saprophyte used for antibiotic and small molecule production. While these two fungi have been investigated individually, the recent discovery of <em>P. chrysogenum </em>hindering <em>P. expansum</em> apple fruit infection has not been well studied. To shed light on this interaction between the two species, we conducted a comparative transcriptomic, metabolomic, and genomic study. Global transcriptional and metabolomic outputs were disparate between the species, nearly identical for the <em>P. chrysogenum </em>isolates, and different between <em>P. expansum </em>isolates. Further, the two <em>P. chrysogenum</em> genomes revealed secondary metabolite gene clusters that differed from <em>P. expansum</em>. This included the absence of an intact patulin gene cluster in <em>P. chrysogenum</em>, which corroborates the metabolomic data regarding the species’ inability to produce patulin. Additionally, <em>P. expansum </em>virulence gene homologues were identified in <em>P. chrysogenum </em>and were similarly transcriptionally regulated <em>in vitro</em>. Molecules with potential antimicrobial activity, and phytohormones like indole-3-acetic acid (IAA), were detected for the first time in <em>P. expansum</em> while pharmacological compounds like the well-studied antibiotic penicillin G were identified in <em>P. chrysogenum</em> culture supernatants. Our findings provide new omics-based resources that enable the study of small molecule production of interest, the potential of <em>Penicillium</em>-derived antimicrobials for postharvest decay control, and <em>P.</em> <em>expansum’s</em> metabolites roles in host-pathogen interactions. </span></p>
Supplementary table for "Deciphering the Relationship Between Circulating Metabolites and Osteoarthritis: A Comprehensive Genetic Correlation and Mendelian Randomization Studies"
Open the record for dataset details and reuse information.
Weight-loss & metabolite data of cachetic cancer patients over multiple time points
<p>This dataset contains weight-loss data and metabolite measurements from cachetic cancer patients. This data set is part of the publication "Biomarkers related to fatty acid oxidative capacity are predictive for continued weight loss in cachectic cancer patients" by Catanese et al., 2021 (<a href="https://doi.org/10.1002/jcsm.12817">LINK</a>). Data were collected over a extended time period starting from the day of hospital admission. Up to seven time points are available for individual patients. A metabolite profile consisting of amino acids and acylcarnitines measured via tandem MS/MS from both blood plasma and dried blood spots is available for each patient at each time point. Additional available phenotypes are the age, sex and historic weight of each patient. </p> <p>Abbreviations:</p> <ul> <li>HW - Historical weight</li> <li>TP - Time point</li> <li>WL - Weight loss</li> <li>DBS - Dried blood spot</li> <li>Metabolite abbreviations are explained in Supplementary Table S5 of the corresponding publication</li> </ul>
Online Supplemental Tables - An atlas of genome-wide gene expression and metabolite associations and possible mediation effects towards body mass index
<p>Summary statistics of metabolite-gene expression associations and mediation analyses of effects on body mass index.</p> <p>The corresponding publication is currently under revision.</p> <ul> <li><strong>Online Supplemental Table 1</strong>: Gene expression-metabolite association summary statistics from 97 metabolites and metabolite ratios and up to 15175 genes calculated seperately in the LIFE-Adult, LIFE-Heart, LIFE-AMI and the Sorb studies. Associations were adjusted for six covariates.</li> <li><strong>Online Supplemental Table 2</strong>: Random-effects meta-analyzed gene expression-metabolite association summary statistics. P-Values were adjusted for multiple testing using a hierarchical adjustment procedure both on local (within phenotypes) and on global (across phenotypes) level.</li> <li><strong>Online Supplemental Table 3</strong>: Single-study association results needed for checking mediation analysis assumptions and for calculating mediation statistics. Only gene expression probe-metabolite pairs that associated significantly at hierarchical FDR=5% in the gene-expression-metabolite association meta-analysis qualified for these associations.The following Associations were tested: gene expression probes ~ metabolites, log-BMI ~ metabolites log-BMI ~ gene expression, log-BMI ~ gene expression + metabolite. Associations were adjusted for six covariates. P-values</li> <li><strong>Online Supplemental Table 4</strong>: Meta-analyzed association results needed for checking mediation analysis assumptions and for calculation mediation statistics.</li> <li><strong>Online Supplemental Table 5: </strong>Mediation analysis summary statistics. Mediations of gene expression effects (exposure) via metabolite effects (mediatior) and of metabolite effects (exposure) via gene expression effects (mediator) on body mass index (outcome) were tested.</li> </ul> <p> </p>
Prediction of metabolites associated with somatic mutations in cancers
<ul> <li>GEMs_AML: 16 acute myeloid leukemia (AML) patient-specific genome-scale metabolic models (GEMs) reconstructed using their corresponding RNA-seq data and Recon 2M.2</li> <li>GEMs_PCAWG: 943 cancer patient-specific GEMs for 24 different cancer types reconstructed using the Pan-Cancer Analysis of Whole Genomes (PCAWG) RNA-seq data and generic human GEM 'Recon 2M.2'</li> <li>GEMs_RCC: 20 renal cell carcinoma (RCC) patient-specific GEMs reconstructed using their corresponding RNA-seq data and Recon 2M.2</li> <li>GEMs_TCGA_LAML: 113 AML patient-specific GEMs reconstructed using The Cancer Genome Atlas (TCGA) LAML RNA-seq data and Recon 2M.2</li> </ul>
Fungicides Cuprozin Progress and SWITCH Modulate Primary and Specialized Metabolites of Strawberry Fruits
<p>Numerous pesticides, including fungicides, are applied every year to crop plants. However, such application may affect the plant metabolism and thereby impact crop quality. Strawberry is an economically important crop, but the fruits are highly susceptible, especially to fungal diseases. In the present study, the effects of two fungicides on primary and specialized metabolites determining the fruit aroma were tested in two strawberry cultivars and the wild strawberry. Fruit extracts were analyzed using HPLC (amino acids), GC-MS (organic acids, sugars, flavor metabolites) and photometric quantification (total phenolics). Various metabolites were either significantly higher or lower in fruits of plants exposed to fungicides compared to those of control plants, with differences between strawberry cultivars and species, as well as used fungicides. Given the various changes in metabolites in response to the treatments, the taste and quality of the strawberries may pronouncedly change when plants are treated with fungicides.</p>
FIG. 15 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 15. — Cladonia novochlorophaea (Sipman) Brodo & Ahti: A, habit (BP[BP 9314]); B, spots of lichen secondary metabolites on chromatographic plates; C, distribution in Hungary. Abbreviations: H, homosekiaic acid; F, fumarprotocetraric acid; Z, zeorin; N, norstictic acid. Scale bar: A, 2 mm.
FIG. 8 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 8. — The mean diameter of soredia (µm) measured on podetia (n = 10). Abbreviations: asa, C. asahinae (n = 22); chlo, C. chlorophaea (n = 55); cry, C. cryptochlorophaea (n = 53); gra, C. grayi (n = 17); mero, C. merochlorophaea (n = 70); novo, C. novochlorophaea (n = 10). The lines represent the minimum and maximum values, the box represents the 25% and 75% of the data, the thick line represents the median. Means with the same letter are not significantly different at 95% confidence.
FIG. 5 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 5. — Height of cup (mm) in different species. Abbreviations: asa, C. asahinae (n = 22); chlo, C. chlorophaea (n = 55); cry, C. cryptochlorophaea (n = 53); gra, C. grayi (n = 17); mero, C. merochlorophaea (n = 70); novo, C. novochlorophaea (n = 10). The lines represent the minimum and maximum values, the box represents the 25% and 75% of the data, the thick line represents the median. Means with the same letter are not significantly different at 95% confidence.
FIG. 9 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 9. — Conditional inference tree presenting the five most important morphological variables separating species: CH, height of cup; CW, width of cup; PH, height of podetium; SW, width of podetium stalk. The order of the species at the end of the nodes is as follows: a, C. asahinae; c, C. chlorophaea; cr, C. cryptochlorophaea; g, C. grayi; m, C. merochlorophaea; n, C. novochlorophaea. Boxes represent the highest probability of a species occurrence on the tree node. A level of p <0.05 was considered for a significant difference.
FIG. 12 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 12. — Cladonia cryptochlorophaea Asahina: A, habit (BP[BP 48938]); B, spots of lichen secondary metabolites on chromatographic plates; C, distribution in Hungary. Abbreviations: c, cryptochlorophaeic acid; nR, norrangiformic acid; F, fumarprotocetraric acid; Z, zeorin; N, norstictic acid. Scale bar: A, 2 mm.
FIG. 7 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 7. — Width of podetium stalk (mm) in different species. Abbreviations: asa, C. asahinae (n = 22); chlo, C. chlorophaea (n = 55); cry, C. cryptochlorophaea (n = 53); gra, C. grayi (n = 17); mero, C. merochlorophaea (n = 70); novo, C. novochlorophaea (n = 10). The lines represent the minimum and maximum values, the box represents the 25% and 75% of the data, the thick line represents the median. Means with the same letter are not significantly different at 95% confidence.
FIG. 11 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 11. — Cladonia chlorophaea (FlÖrke ex Sommerf.) Spreng.: A, habit (BP[BP 49014]); B, spots of lichen secondary metabolites on chromatographic plates; C, distribution in Hungary. Abbreviations: F, fumarprotocetraric acid; Z, zeorin; N, norstictic acid. Scale bar: A, 2 mm.
FIG. 6 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 6. — Width of cup (mm) in different species. Abbreviations: asa, C. asahinae (n = 22); chlo, C. chlorophaea (n = 55); cry, C. cryptochlorophaea (n = 53); gra, C. grayi (n = 17); mero, C. merochlorophaea (n = 70); novo, C. novochlorophaea (n = 10). The lines represent the minimum and maximum values, the box represents the 25% and 75% of the data, the thick line represents the median. Means with the same letter are not significantly different at 95% confidence.
FIG. 10. — Cladonia asahinae J.W in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 10. — Cladonia asahinae J.W.Thomson: A, habit (BP[BP 9421]); B, spots of lichen secondary metabolites on chromatographic plates; C, distribution in Hungary. Abbrevations: R, rangiformic acid; nR, norrangiformic acid; F, fumarprotocetraric acid; Z, zeorin; N, norstictic acid. Scale bar: A, 2 mm.
FIG. 3 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 3. — Conditional inference tree presenting the presence of the three most abundant lichen secondary metabolites occurring in more than one species of the C. chlorophaea species group. A level of p <0.05 was considered for a significant difference. Abbreviations: cch, cryptochlorophaeic acid; ran, rangiformic acid; tha, thamnolic acid; a, C. asahinae; ch, C. chlorophaea; cr, C. cryptochlorophaea; g, C. grayi; me, C. merochlorophaea; no, C. novochlorophaea. Boxes represent the highest probability of a species occurrence on the tree node.
FIG. 4 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 4. — Height of podetia (mm) of the different species. Abbreviations: asa, C. asahinae (n = 22); chlo, C. chlorophaea (n = 55); cry, C. cryptochlorophaea (n = 53); gra, C. grayi (n = 17); mero, C. merochlorophaea (n = 70); novo, C. novochlorophaea (n = 10). The lines represent the minimum and maximum values, the box represents the 25% and 75% of the data, the thick line represents the median. Means with the same letter are not significantly different at 95% confidence.
Advancements in QSAR modelling: Decision trees and rotation forest for prediction of Aspergillus anti-inflammatory metabolites
<p>This study presents applications of advancements in QSAR modelling for predicting nitric oxide (NO) inhibitors and anti-inflammatory metabolites from the <em>Aspergillus</em> genus. Inflammation-related diseases remain a pressing concern, necessitating the identification of effective anti-inflammatory compounds. Using decision trees, the Ranker method, and CorrelationAtrributeEval as a base classifier for attribute selection together with Rotation Forest and Adaboost as enhancers, we explored their potential with different classifiers including Artificial Neural Networks and J48 Trees. The proposed QSAR models employed an ensemble approach with Rotation Forest and Adaboost.M1, applying an automated KNIME workflow. Seven molecular descriptors were selected and trained on a comprehensive dataset of diverse anti-inflammatory <em>Aspergillus</em> specialised metabolites. Results showed that the Rotation Forest-enhanced version outperformed other models, capturing complex structure-activity relationships and improving predictive performance. Chemical characteristics of electrotopological state, topological distances, and functional groups including secondary amides and alcohols contribute to important anti-inflammatory effects. The developed QSAR model showed good predictive performance for anti-inflammatory <em>Aspergillus</em> metabolites, focusing on their NO inhibitory activity. These results can contribute to the discovery of novel anti-inflammatory drugs based on computational techniques.</p> <p> </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.