Welcome to the IMOPAC webserver!
IMOPAC is a newly developed interactive and easy-to-use webserver for comprehensively characterizing the landscape of pharmacogenomic interactions and easily visualizing the pharmacogenomic profiles in preclinical cancer models, on the basis of multi-omics and drug response profiles originated from three public available databases, including:
6 omics, 992 cell lines from 28 cancer types, 173 drugs from GDSC;
12 omics, 1773 cell lines from 34 cancer types and 22 drugs from DepMap;
8 omics, 60 cell lines from 9 cancer types and 695 drugs from NCI60.
The IMOPAC webserver provides interactive and customizable functions including:
pancancer expression;
drug-omics/pathway correlation;
cancer subtypes;
omics-omics (cis-/trans-regulation) correlation;
and fusion query analysis;
With just simple clicks through IMOPAC, this webserver will significantly accelerate data mining in wide tumor biological research areas for biomarker discovery, drug repurposing, and precision treatment.
The outputs from all analyses including tables (csv) and high-resolution figures (pdf, tiff, svg, and eps) can be easily downloaded from IMOPAC.
This website is available freely for all users without any login requirement.
Features:
Advanced filters for model selection
Detailed annotations including cell lines, genes and drugs;
Detailed clinical and biological features, inluding age, gender, ploid, microsatellite status, mutational burden, culture type, culture medium and so on;
Models mapped to external databases and resources by weblinks;
1.Cancer Types Summary module allows users to assess the alteration frequency for omics with discrete settings (e.g. CNV or somatic mutation) or expression abundance for ones with continuous settings (e.g. mRNA and so on) of a specific input gene across multiple cancer types.
2.Drug-Omics module is aimed to investigate the correlation between the omics level of user-input gene and the response of all selected drugs across user-defined cell lines.
3.Drug-Pathway module is designed to explore the association between the activity of a specific pathway and the response of all selected drugs across user-defined cell lines.
4.Cancer Subtypes module provide a unsupervised clustering analysis for identifying cancer subtypes from raw data to result visualisation based on user-defined geneset.
5.Omics-Omics (cis-regulation) module is developed to investigate the correlation among the multi-omics level of a specific user-input gene.
6.Omics-Omics (trans-regulation) module is aimed to estimate the association among various user-input genes from same or different omics.
7.Fusions module provide a comprehensive gene fusion landscape of the user-defined cell line.
