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HTSplotter: An end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro
Carolina Nunes1,2,3,4, Jasper Anckaert3,5, Fanny De Vloed2,3
1Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium.
Plos One
|January 5, 2024
Summary
HTSplotter automates data analysis and visualization for diverse high-throughput screening experiments, including complex genetic-chemical screens and real-time assays. This tool enhances efficiency and accuracy in processing large biological datasets.
Area of Science:
- Biomedical research
- Computational biology
- Drug discovery
Background:
- High-throughput screening (HTS) accelerates biomedical research with automated, efficient data generation.
- Current HTS data analysis tools often lack versatility, failing to support diverse experimental setups like real-time assays or combined genetic-chemical screens.
- Manual data processing for HTS is laborious, time-consuming, and prone to errors.
Purpose of the Study:
- To develop an automated, versatile tool for analyzing and visualizing data from various high-throughput screening experiments.
- To address the limitations of existing tools by enabling analysis of real-time assays and combined genetic-chemical screens.
- To provide researchers with an efficient and accurate solution for end-to-end HTS data processing.
Main Methods:
- Developed HTSplotter, a web tool and Python module for automatic HTS data analysis and visualization.
- Implemented an algorithm for automatic identification of experiment types and controls.
- Integrated data normalization (including growth rate) and downstream analyses (dose-response, synergism using Bliss independence, Zero Interaction Potency, Highest Single Agent methods).
Main Results:
- HTSplotter successfully processes endpoint and real-time assays from drug, drug combination, genetic perturbagen, and genetic-chemical perturbagen screens.
- The tool automatically performs data normalization and key downstream analyses.
- Results are exported as text files, and visualizations are saved in PDF format.
Conclusions:
- HTSplotter offers significant improvements in versatility and efficiency for high-throughput screening data analysis.
- The tool's ability to automatically analyze genetic-chemical perturbagen screens and real-time assays over time is a key advantage.
- HTSplotter streamlines the end-to-end workflow for in vitro cell culture screening data, saving time and reducing errors.
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01:16In vitro Mutagenesis
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