Biomedical 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

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... ualisation, 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.