HTSplotter: an end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening

Abstract

AbstractBiomedical researchers are moving towards high-throughput screening, as this allows for automatization, better reproducibility and more and faster results. High-throughput screening experiments encompass drug, drug combination, genetic perturbagen or a combination of genetic and chemical perturbagen screens. These experiments are conducted in real-time assays over time or in an endpoint assay. The data analysis consists of data cleaning and structuring, as well as further data processing and visualisation, which, due to the amount of data, can easily become laborious, time consuming, and error-prone. Therefore, several tools have been developed to aid researchers in this data analysis, but they focus on specific experimental set-ups and are unable to process data of several time points and genetic-chemical perturbagen screens together. To meet these needs, we developed HTSplotter, available as web tool and Python module, that performs automatic data analysis and visualisation of either endpoint or real-time assays from different high-throughput screening experiments: drug, drug combination, genetic perturbagen and genetic-chemical perturbagen screens. HTSplotter implements an algorithm based on conditional statements in order to identify experiment type and controls. After appropriate data normalization, HTSplotter executes downstream analyses such as dose-response relationship and drug synergism by the Bliss independence method. All results are exported as a text file and plots are saved in a PDF file. The main advantage of HTSplotter over other available tools is the automatic analysis of genetic-chemical perturbagen screens and real-time assays where results are plotted over time. In conclusion, HTSplotter allows for the automatic end-to-end data processing, analysis and visualisation of various high-throughput in vitro cell culture screens, offering major improvements in terms of versatility, convenience and time over existing tools.Author summaryAcademic researchers are moving towards high-throughput screenings, where the experiments execution follows an automatic approach, such as robotic seeding, liquid dispensing and/or automatics readouts. This grants more flexible and reproducible experimental set ups. The type of high-throughput experiment can vary from drug, drug combination, genetic perturbagen to genetic-chemical perturbagen screens. These can be assessed through endpoint assays, measuring the effect at one time point, or real-time assays, assessing the phenotypic effect over time. High-throughput screening results in large amounts of data, requiring laborious, time consuming and error-prone data handling and analysis. These challenges hamper the biological interpretation of data and the fast progress in drug development programs. Hence, we developed HTSplotter, a web tool and Python module, to allow researchers to conduct a fast and flexible end-to-end data processing, analysis and visualisation of different high-throughput experiments, assessed either by endpoint or real-time assays, relieving the high-throughput analysis bottleneck. In this way, HTSplotter, directly contributes to a faster identification of drugs and drug combinations, with potential practical applications, or novel targets for therapies.