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Thunor: Visualization and Analysis of High-Throughput Dose-response Datasets

View ORCID ProfileAlexander L. R. Lubbock, View ORCID ProfileLeonard A. Harris, Vito Quaranta, View ORCID ProfileDarren R. Tyson, View ORCID ProfileCarlos F. Lopez
doi: https://doi.org/10.1101/530329
Alexander L. R. Lubbock
1Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
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Leonard A. Harris
1Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
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Vito Quaranta
1Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
2Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA
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Darren R. Tyson
1Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
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Carlos F. Lopez
1Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
2Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA
3Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
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  • For correspondence: c.lopez@vanderbilt.edu
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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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://www.thunor.net/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted February 06, 2021.
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Thunor: Visualization and Analysis of High-Throughput Dose-response Datasets
Alexander L. R. Lubbock, Leonard A. Harris, Vito Quaranta, Darren R. Tyson, Carlos F. Lopez
bioRxiv 530329; doi: https://doi.org/10.1101/530329
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Thunor: Visualization and Analysis of High-Throughput Dose-response Datasets
Alexander L. R. Lubbock, Leonard A. Harris, Vito Quaranta, Darren R. Tyson, Carlos F. Lopez
bioRxiv 530329; doi: https://doi.org/10.1101/530329

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