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150 results for “Toxicogenomics”
Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials
<p>Toxicogenomics (TGx) approaches are increasingly applied to gain insight into the possible toxicity mechanisms of engineered nanomaterials (ENMs). Omics data can be valuable to elucidate the mechanism of action of chemicals and develop predictive models in toxicology. While vast amounts of transcriptomics data from ENM exposures have already been accumulated, a unified, easily accessible and reusable collection of transcriptomics data for ENMs is currently lacking. In an attempt to improve the FAIRness of already existing transcriptomics data for nanomaterials, we curated a collection of homogenized transcriptomics data from human, mouse and rat ENM exposures <em>in vitro</em> and <em>in vivo</em>.</p>
RDF version of the data from Saarimaki et al. Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials (Version 1.0.0) [Zenodo Dataset] (2020)
<p>This is an RDFied version of the dataset published by Saarimaki et al. Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials (Version 1.0.0) [Zebodo Dataset] (2020)</p> <p>The original dataset publication DOI: <a href="http://doi.org/10.5281/zenodo.4146981">http://doi.org/10.5281/zenodo.4146981</a></p> <p>The Original publication authors: Saarimaki, Laura Aliisa, Federico, Antonio, Lynch, Iseult, Papadiamantis, Anastasios G., Tsoumanis, Andreas, Melagraki, Georgia, Afantitis, Antreas, Serra, Angela, & Greco, Dario</p>
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (TGGATEs human dataset)
<p>This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="https://github.com/bhklab/ToxicoGx">https://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html</a>), and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</p>
Toxicogenomic profiles of neuronal targeting insecticides in zebrafish embryos as non-target aquatic vertebrate model
<p>We have conducted semi-static exposure studies with six neuronal targeting insecticides on fertilized zebrafish (Danio rerio) eggs, similar to the OECD 236 guideline for the 96h zebrafish embryo toxicity test. The aim of these transcriptomic profiling experiments was to screen for ecotoxicogenomic fingerprints in zebrafish (Danio rerio) embryos as aquatic vertebrate non-target model exposed to sub lethal concentrations of pesticides. Data published in <a href="https://doi.org/10.1016/j.chemosphere.2021.132746">Reinwald et al. 2021</a> (PMID:<strong>34748799</strong>).</p> <p>For experimental details please refer to the publicly accessible experiment description and treatment protocols deposited in the <a href="https://www.ebi.ac.uk/arrayexpress/">ArrayExpress database </a>at EMBL-EBI (www.ebi.ac.uk/arrayexpress) under the following accession numbers: <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9852/">E-MTAB-9852</a> (Abamectin), <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9855/">E-MTAB-9855 </a>(Carbaryl),<a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9853/"> E-MTAB-9853</a> (Chlorpyrifos), <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9854/">E-MTAB-9854</a> (Fipronil), <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9859/">E-MTAB-9859</a> (Imidacloprid), <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9860/">E-MTAB-9860</a> (Methoxychlor).</p> <p>The uploaded data archives (<a href="https://www.7-zip.org/">7-zip</a> compressed) consists of three major data types:<br> 1. MultiQC reports from raw RNA-Seq read processing and QC (50bp SR) ( <a href="https://zenodo.org/api/files/5c06b1b3-0d96-4ab8-8925-a7419f0a379d/Neuotox_multiQCreports.7z">Neuotox_multiQCreports.7z </a>)<br> 2. Result tables of differential gene expression analysis (DGEA) with DESEq2 ( <a href="https://zenodo.org/api/files/5c06b1b3-0d96-4ab8-8925-a7419f0a379d/Neurotox_DESeq2_ResultTables.7z">Neurotox_DESeq2_ResultTables.7z </a>)<br> 3. Result tables of gene set enrichment analysis (GSEA) with clusterProfiler ( <a href="https://zenodo.org/api/files/5c06b1b3-0d96-4ab8-8925-a7419f0a379d/Neurotox_clusterProfiler_ResultTables.7z">Neurotox_clusterProfiler_ResultTables.7z </a>)<br> 4. Result tables of overrepresentation analysis (ORA) via <em>clusterProfiler::compareCluster() </em>( <a href="https://zenodo.org/api/files/c3fb14b4-6a90-4b12-a2be-ed5661d19683/Neurotox_clusterProfiler_ORA_on_core_DEGs.7z?versionId=2156d2be-b815-4157-b0f9-3eeed117b812">Neurotox_clusterProfiler_ORA_on_core_DEGs.7z </a>)</p> <p>Each data archive contains a README file describing the methods applied to generate the respective result tables / reports. For each tested substance and exposure condition a DGEA and GSEA result table is uploaded. The corresponding bash and R codes for each analysis step are available on github under:<br> <a href="https://github.com/hreinwal/zfeNeurotox">https://github.com/hreinwal/zfeNeurotox</a></p> <p>Gene count normalization and DGEA was conducted with DESeq2 (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8">Love et al., 2014</a>, DOI 10.1186/s13059-014-0550-8). Three biological replicates per condition, exposure treatments were compared with respect to the control group in a pairwise fashion, applying Wald’s t-test. P values were corrected for multiple testing with independent hypothesis weighting (IHW) (<a href="https://www.nature.com/articles/nmeth.3885">Ignatiadis et al., 2016</a>, DOI 10.1038/nmeth.3885) after Benjamini-Hochberg (BH). To improve the signal to statistical noise ratio, the obtained log<sub>2</sub>-fold change (lfc) values were shrunk with the apeglm method described by Zhu and colleagues (<a href="https://academic.oup.com/bioinformatics/article/35/12/2084/5159452?login=true">2019</a>, DOI 10.1093/bioinformatics/bty895) before DGEA result tables were subjected to GSEA via clusterProfiler (<a href="https://www.liebertpub.com/doi/10.1089/omi.2011.0118">Yu et al., 2012</a>, DOI 10.1089/omi.2011.0118)<br> and reactomePA (<a href="https://pubs.rsc.org/en/content/articlehtml/2015/mb/c5mb00663e">Yu and He, 2016</a>, DOI 10.1039/C5MB00663E). The linked ArrayExpress accession numbers above, provide access to the raw and DESeq2 normalized gene count matrices upon which these analysis were performed. Genes were annotated through the biomaRt package (<a href="https://www.nature.com/articles/nprot.2009.97.pdf?origin=ppub">Durinck et al., 2009</a>, DOI 10.1038/nprot.2009.97) in R (<a href="https://www.r-project.org/">R Core Team 2021</a>).</p>
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets
<p>This page links to the data associated with the publication "ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets ". The data have been curated and analyzed using our open-source R package, ToxicoGx (https://github.com/bhklab/ToxicoGx) and are available publicly in the ToxicoDB web application (www.toxicodb.ca). Please see the included DOIs below, or download the .csv file which contains the names, dates and DOIs of all datasets listed here.</p> <p>The TGGATES data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023.<br> <br> Data:</p> <ul> <li>TGGATEs humanldh (<a href="https://doi.org/10.5281/zenodo.3762812">https://doi.org/10.5281/zenodo.3762812</a>)</li> <li>TGGATEs humandna (<a href="https://doi.org/10.5281/zenodo.4024859">https://doi.org/10.5281/zenodo.4024859</a>)</li> <li>TGGATEs ratldh (<a href="https://doi.org/10.5281/zenodo.3762817">https://doi.org/10.5281/zenodo.3762817</a>)</li> <li>TGGATEs ratdna (<a href="https://doi.org/10.5281/zenodo.4024918">https://doi.org/10.5281/zenodo.4024918</a>)</li> </ul> <p>This Drug Matrix data was generated by Ganter B, Snyder RD, Halbert DN, Lee MD. Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics [Internet]. 2006 Oct;7(7):1025–1044. Available from: http://dx.doi.org/10.2217/14622416.7.7.1025 PMID: 17054413.</p> <p>Data:</p> <ul> <li>Drug Matrix (<a href="https://doi.org/10.5281/zenodo.3766569">https://doi.org/10.5281/zenodo.3766569</a>)</li> </ul>
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (TGGATEs rat dataset)
<p>This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="https://github.com/bhklab/ToxicoGx">https://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html</a>), and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</p>
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (TGGATEs human dataset)
<p>This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="http://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html">http://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html</a>), and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</p>
In-depth comparative toxicogenomics of glyphosate and Roundup herbicides: Histopathology, transcriptome and epigenome signatures, and DNA damage
<p><span><span><span><span><span><span><span><span><span><span><span>Whether or not glyphosate activates cellular mechanisms involved in carcinogenesis remains controversial. We tested whether glyphosate and three glyphosate-based commercial herbicide formulations activate mechanisms known to be key characteristics of carcinogens. The mammalian stem cell-based genotoxicity ToxTracker assay showed that the representative EU formulation Roundup MON 52276 and the UK formulation Roundup MON 76473, but not glyphosate and the US Roundup formulation MON 76207, activated oxidative stress and unfolded protein responses. High-throughput molecular profiling of liver function was performed in female Sprague-Dawley rats exposed to glyphosate or MON 52276 (both at 0.5, 50, 175 mg/kg bw/day glyphosate equivalent concentration) for 90 days. Histopathology and serum biochemistry analysis showed that MON 52276 but not glyphosate treatment increased hepatic steatosis and necrosis. MON 52276 and glyphosate altered the expression of genes in liver reflecting TP53 activation by DNA damage and the regulation of circadian rhythms. The most affected genes in liver also had their expression similarly altered in kidneys. Small RNA profiling in liver showed miR-22 and miR-17 had their levels decreased by MON 52276, while mir-30 levels were decreased, whilst miR-10 levels were increased by glyphosate. DNA methylation profiling of liver revealed 5,727 and 4,496 differentially methylated CpG sites between the control and glyphosate and MON 52276 exposed groups of animals respectively. Direct DNA damage measurement by apurinic/apyrimidinic lesion formation in liver was increased with glyphosate exposure. Altogether, our results show that Roundup herbicide formulations are causing more biological changes than glyphosate alone, activating mechanisms involved in cellular carcinogenesis.</span></span></span></span></span></span></span></span></span></span></span></p>
In-depth comparative toxicogenomics of glyphosate and Roundup herbicides: Histopathology, transcriptome and epigenome signatures, and DNA damage
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ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (TGGATEs rat dataset)
<p>This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="http://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html">http://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html</a>) and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</p>
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (drug matrix dataset)
<p>This data was generated by Ganter B, Snyder RD, Halbert DN, Lee MD. Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics [Internet]. 2006 Oct;7(7):1025–1044. Available from: http://dx.doi.org/10.2217/14622416.7.7.1025 PMID: 17054413. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="https://cran.r-project.org/web/packages/ToxicoGx/">cran.r-project.org/web/packages/ToxicoGx</a>) and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</p> <p> </p> <p> </p>
Data from: A novel open access web portal for integrating mechanistic and toxicogenomic study results
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Toxicogenomics and phenotypic characterization of adult female zebrafish liver treated with arsenic [As(V)]
GEO Series GSE30062. Danio rerio. 24 samples. Type: Expression profiling by array.
Toxicogenomic Analysis of Caenorhabditis elegans Reveals Genes Involved in the Resistance to Cadmium Toxicity
GEO Series GSE7535. Caenorhabditis elegans. 36 samples. Type: Expression profiling by array.
Integration of metabolic activation with a predictive toxicogenomics signature to classify genotoxic versus nongenotoxic chemicals in human TK6 cells [BaP]
GEO Series GSE51172. Homo sapiens. 23 samples. Type: Expression profiling by array.
A Pipeline for High Throughput Concentration Response Modeling of Gene Expression for Toxicogenomics
GEO Series GSE105050. Homo sapiens. 48 samples. Type: Other.
Toxicogenomics and phenotypic characterization of adult female zebrafish liver treated with cadmium (II)
GEO Series GSE41622. Danio rerio. 22 samples. Type: Expression profiling by array.
Toxicogenomic Assessment of Liver Responses following subchronic exposure to furan in Fischer F344 rats [cRNA]
GEO Series GSE62806. Rattus norvegicus. 49 samples. Type: Expression profiling by array.
Interlaboratory study to determine the reproducibility of toxicogenomics datasets
GEO Series GSE25936. Homo sapiens. 24 samples. Type: Expression profiling by array.
Exposure of pregnant mice to carbon black by intratracheal instillation: toxicogenomics effects in dams and offspring
GEO Series GSE29764. Mus musculus. 38 samples. Type: Expression profiling by array.
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International Brain Laboratory public data
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