{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zheng M"],"funding":["Fonds National de la Recherche Luxembourg","National Research Fund"],"pagination":["D877-D889"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9825489"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["51(D1)"],"pubmed_abstract":["Prior knowledge of perturbation data can significantly assist in inferring the relationship between chemical perturbations and their specific transcriptional response. However, current databases mostly contain cancer cell lines, which are unsuitable for the aforementioned inference in non-cancer cells, such as cells related to non-cancer disease, immunology and aging. Here, we present ChemPert (https://chempert.uni.lu/), a database consisting of 82 270 transcriptional signatures in response to 2566 unique perturbagens (drugs, small molecules and protein ligands) across 167 non-cancer cell types, as well as the protein targets of 57 818 perturbagens. In addition, we develop a computational tool that leverages the non-cancer cell datasets, which enables more accurate predictions of perturbat"],"journal":["Nucleic acids research"],"pubmed_title":["ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells."],"pmcid":["PMC9825489"],"funding_grant_id":["C15/BM/10397420","C19/BM/13624979"],"pubmed_authors":["Okawa S","Zheng M","Bravo M","Martinez-Chantar ML","Chen F","Del Sol A"],"additional_accession":[]},"is_claimable":false,"name":"ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells.","description":"Prior knowledge of perturbation data can significantly assist in inferring the relationship between chemical perturbations and their specific transcriptional response. However, current databases mostly contain cancer cell lines, which are unsuitable for the aforementioned inference in non-cancer cells, such as cells related to non-cancer disease, immunology and aging. Here, we present ChemPert (https://chempert.uni.lu/), a database consisting of 82 270 transcriptional signatures in response to 2566 unique perturbagens (drugs, small molecules and protein ligands) across 167 non-cancer cell types, as well as the protein targets of 57 818 perturbagens. In addition, we develop a computational tool that leverages the non-cancer cell datasets, which enables more accurate predictions of perturbat","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2025-04-04T09:30:42.751Z","creation":"2025-04-04T09:30:42.751Z"},"accession":"S-EPMC9825489","cross_references":{"pubmed":["36200827"],"doi":["10.1093/nar/gkac862"]}}