Thunor: visualization and analysis of high-throughput dose–response datasets

Abstract

High-throughput cell proliferation assays to quantify drug-response are becoming increasingly common and powerful with the emergence of improved automation and multi-time point analysis methods. However, pipelines for analysis of these datasets that provide reproducible, efficient, and interactive visualization and interpretation are sorely lacking. To address this need, we introduce Thunor, an open-source software platform to manage, analyze, and visualize large, dose-dependent cell proliferation datasets. Thunor supports both end-point and time-based proliferation assays as input. It provides a simple, user-friendly interface with interactive plots and publication-quality images of cell proliferation time courses, dose–response curves, and derived dose–response metrics, e.g. IC50, including across datasets or grouped by tags. Tags are categorical labels for cell lines and drugs, used for aggregation, visualization and statistical analysis, e.g. cell line mutation or drug class/target pathway. A graphical plate map tool is included to facilitate plate annotation with cell lines, drugs and concentrations upon data upload. Datasets can be shared with other users via point-and-click access control. We demonstrate the utility of Thunor to examine and gain insight from two large drug response datasets: a large, publicly available cell viability database and an in-house, high-throughput proliferation rate dataset. Thunor is available from www.thunor.net.

Document Details

Document Type
Pub Defense Publication
Publication Date
May 26, 2021
Source ID
10.1093/nar/gkab424

Entities

People

  • Alexander L R Lubbock
  • Carlos F. López
  • Darren R. Tyson
  • Leonard A. Harris
  • Vito Quaranta

Organizations

  • Defense Advanced Research Projects Agency
  • National Cancer Institute
  • National Science Foundation
  • United States National Library of Medicine
  • University of Arkansas
  • University of Arkansas for Medical Sciences
  • Vanderbilt University

Tags

Fields of Study

  • Biology

Readers

  • Database Systems and Applications
  • Distributed Systems and Data Platform Development
  • Molecular Biology and Genetics