ArticleBioinformatics (Oxford, England)2019
DoRes within CellMissy: dose-response analysis on cell migration and related data.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Time-course transcriptome analysis reveals the mechanisms of Burkholderia sp. adaptation to high phenol concentrations.Applied microbiology and biotechnology · 2020Article
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4 authors.
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Abstract
summaryIn cancer research, cell-based assays are used to assess cell migration and invasion. The major bottleneck is the lack of automated tools to visualize and analyse the large amounts of biological dose-response data produced. To address this challenge, we have developed an automated and free software package for dose-response analyses, DoRes, which is released as an add-on of the freely available and open-source tool CellMissy, dedicated to the management and analysis of cell migration data. DoRes implements non-linear curve fitting functionality into a robust, user-friendly and flexible software package with the possibility of importing a tabular file or starting from a cell migration experiment. We demonstrate the ability of the software by analysing public dose-response data and a typical cell migration experiment, and show that the extracted dose-response parameters and the calculated statistical values are consistently comparable to those of the widely used, commercial software GraphPad Prism. AVAILABILITY AND IMPLEMENTATION: The software here presented is a new module in CellMissy, an open-source and cross-platform package dedicated to the management, storage and analysis of cell migration data. The new module is written in Java, and inherits the cross-platform support from CellMissy. Source code and binaries are freely available under the Apache2 open-source licence at https://github.com/compomics/cellmissy/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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