HTSplotter: an end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening Article Swipe
Carolina Nunes
,
Jasper Anckaert
,
Fanny De Vloed
,
Jolien De Wyn
,
Kaat Durinck
,
Jo Vandesompele
,
Frank Speleman
,
Vanessa Vermeirssen
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.09.06.459128
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.09.06.459128
In biomedical research, high-throughput screening is often applied as it comes with automatization, higher-efficiency, 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 process, but these typically focus on specific experimental set-ups and are unable to process data of several time points and genetic-chemical perturbagen screens. To meet these needs, we developed HTSplotter, available as web tool and Python module, which 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, including growth rate normalization, HTSplotter executes downstream analyses such as dose-response relationship and drug synergism assessment by the Bliss independence (BI), Zero Interaction Potency (ZIP) and Highest Single Agent (HAS) methods. 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 growth rate and perturbagen effect 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, efficiency and time over existing tools.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.09.06.459128
- https://www.biorxiv.org/content/biorxiv/early/2021/09/10/2021.09.06.459128.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3196758686
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3196758686Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2021.09.06.459128Digital Object Identifier
- Title
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HTSplotter: an end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screeningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-09-06Full publication date if available
- Authors
-
Carolina Nunes, Jasper Anckaert, Fanny De Vloed, Jolien De Wyn, Kaat Durinck, Jo Vandesompele, Frank Speleman, Vanessa VermeirssenList of authors in order
- Landing page
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https://doi.org/10.1101/2021.09.06.459128Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2021/09/10/2021.09.06.459128.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2021/09/10/2021.09.06.459128.full.pdfDirect OA link when available
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Computer science, Data mining, Visualization, Normalization (sociology), Throughput, Python (programming language), Machine learning, Operating system, Anthropology, Sociology, WirelessTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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