Data on chemical-gene interactions and biological categories enriched with genes sensitive to chemical exposures
| Autor: | Saira Amir, Alexander Suvorov, Joseph McGaunn, Anthony Poluyanoff, Victoria Salemme |
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| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
Multidisciplinary Toxicity pathway Computational biology Computational toxicology Biology lcsh:Computer applications to medicine. Medical informatics Toxicogenomics Genome 03 medical and health sciences 0302 clinical medicine Adverse outcome pathway Regulatory toxicology lcsh:R858-859.7 Identification (biology) KEGG lcsh:Science (General) Gene 030217 neurology & neurosurgery 030304 developmental biology lcsh:Q1-390 Data Article |
| Zdroj: | Data in Brief Data in Brief, Vol 33, Iss, Pp 106398-(2020) |
| ISSN: | 2352-3409 |
| Popis: | A dataset of chemical-gene interactions was created by extracting data from the Comparative Toxicogenomics Database (CTD) with the following filtering criteria: data was extracted only from experiments that used human, rat, or mouse cells/tissues and used high-throughput approaches for gene expression analysis. Genes not present in genomes of all three species were filtered out. The resulting dataset included 591,084 chemical-gene interaction. All chemical compounds in the database were annotated for their major uses. For every gene in the database number of chemical-gene interactions was calculated and used as a metric of gene sensitivity to a variety of chemical exposures. The lists of genes with corresponding numbers of chemical-gene interactions were used in gene-set enrichment analysis (GSEA) to identify potential sensitivity to chemical exposures of molecular pathways in Hallmark, KEGG and Reactome collections. Thus, data presented here represent unbiased and searchable datasets of sensitivity of genes and molecular pathways to a broad range of chemical exposures. As such the data can be used for a diverse range of toxicological and regulatory applications. Approach for the identification of molecular mechanisms sensitive to chemical exposures may inform regulatory toxicology about best toxicity testing strategies. Analysis of sensitivity of genes and molecular pathways to chemical exposures based on these datasets was published in Chemosphere (Suvorov et al., 2021) [1] . |
| Databáze: | OpenAIRE |
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