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ScreenGarden: a shinyR application for fast and easy analysis of plate-based high-throughput screens

Klemm, Cinzia; Howell, Rowan SM; Thorpe, Peter H; (2022) ScreenGarden: a shinyR application for fast and easy analysis of plate-based high-throughput screens. BMC Bioinformatics , 23 (1) , Article 60. 10.1186/s12859-022-04586-1. Green open access

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Abstract

BACKGROUND: Colony growth on solid media is a simple and effective measure for high-throughput genomic experiments such as yeast two-hybrid, synthetic dosage lethality and Synthetic Physical Interaction screens. The development of robotic pinning tools has facilitated the experimental design of these assays, and different imaging software can be used to automatically measure colony sizes on plates. However, comparison to control plates and statistical data analysis is often laborious and pinning issues or plate specific growth effects can lead to the detection of false-positive growth defects. RESULTS: We have developed ScreenGarden, a shinyR application, to enable easy, quick and robust data analysis of plate-based high throughput assays. The code allows comparisons of different formats of data and different sized arrays of colonies. A comparison of ScreenGarden with previous analysis tools shows that it performs, at least, equivalently. The software can be run either via a website or offline via the RStudio program; the code is available and can be modified by expert uses to customise the analysis. CONCLUSIONS: ScreenGarden provides a simple, fast and effective tool to analyse colony growth data from genomic experiments.

Type: Article
Title: ScreenGarden: a shinyR application for fast and easy analysis of plate-based high-throughput screens
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1186/s12859-022-04586-1
Publisher version: https://doi.org/10.1186/s12859-022-04586-1
Language: English
Additional information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Keywords: Science & Technology, Life Sciences & Biomedicine, Biochemical Research Methods, Biotechnology & Applied Microbiology, Mathematical & Computational Biology, Biochemistry & Molecular Biology, Genomics, Yeast, Automated data analysis, SYNTHETIC PHYSICAL INTERACTIONS, GENETIC INTERACTIONS, YEAST, FITNESS
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Pathology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
URI: https://discovery.ucl.ac.uk/id/eprint/10144041
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