Supporting data for "KREAP: An automated Galaxy Platform to Quantify Re-Epithelialization Kinetics"

Dataset type: Software, Virtual-Machine
Data released on July 16, 2018

Fernandez-Gutierrez MM; van Zessen DBH; Baarlen Pv; Kleerebezem M; Stubbs AP (2018): Supporting data for "KREAP: An automated Galaxy Platform to Quantify Re-Epithelialization Kinetics" GigaScience Database. http://dx.doi.org/10.5524/100472

DOI10.5524/100472

In vitro scratch assays have been widely used to study the influence of bioactive substances on the processes of cell migration and proliferation that are involved in re-epithelialization. The development of high-throughput microscopy and image analysis has enabled scratch assays to become compatible with high-throughput research. However, effective processing and in-depth analysis of such high-throughput image-datasets is far from trivial and requires integration of multiple image processing and data extraction software tools.
We developed and implemented a Kinetic Re-Epithelialization Analysis Pipeline (KREAP) in Galaxy. The KREAP toolbox incorporates freely available image analysis tools and automatically performs image segmentation and feature extraction of each image series, followed by automatic quantification of cells inside and outside the scratched area over time. The enumeration of infiltrating cells over time is modelled to extract three biologically relevant parameters that describe re-epithelialization kinetics. The output of the tools is organized, displayed, and saved in the Galaxy environment for future consultation.
The Galaxy KREAP toolbox provides an open-source, easy-to-use, web-based platform for reproducible image processing and data analysis of high-throughput scratch assays. The KREAP toolbox could assist a broad scientific community in the discovery of compounds that are able to modulate re-epithelialization kinetics.





File NameSample IDData TypeFile FormatSizeRelease Date 
GitHub archivearchive6.62 MB2018-06-11
virtual machineUNKNOWN3.08 GB2018-06-11
virtual machinearchive2.95 GB2018-06-11
ReadmeTEXT2.56 KB2018-06-11
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July 16, 2018 Dataset publish