ChemPert: mapping between chemical perturbation and transcriptional response for noncancer cells
Menglin Zheng, Satoshi Okawa, Miren Bravo, Fei Chen, María-Luz Martínez Chantar, Antonio del Sol
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. Here we present ChemPert (https://chempert.uni.lu/), a database consisting of 82270 transcriptional signatures across 167 non-cancer cell types, enabling more accurate predictions of perturbation responses and drugs compared to cancer databases in non-cancer cells. In particular, ChemPert correctly predicted drug effects for treating non-alcoholic steatohepatitis and novel drugs for osteoarthritis. Overall, ChemPert provides a valuable resource for drug discovery in non-cancer diseases.
The source code cand be found on Gitlab
The Supplementary Information can also be found here.