Cloud-based relational database for multimodal animal data. (14th December 2018)

• Record Type: Journal Article
• Authors: Pallast, Niklas; Wieters, Frederique; Nill, Marieke; Fink, Gereon R; Aswendt, Markus
• Is Part Of: Database. Volume 2018(2018)
• Publication Date: 2018-12-14
• Subjects: Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/bay124
• Languages: English
• ISSNs: 1758-0463

Abstract

Abstract: Pre-clinical research builds on a large variety of in vivo and ex vivo tools such as non-invasive imaging, microscopy, and analysis of gene expression. To work efficiently with multimodal data and correlate results across scales, it is of particular importance to have easy access to all data points from different specimen, e.g. the magnetic resonance imaging (MRI) data from different time points, and the post-mortem histology. That requires an efficient data management, which is customizable and designed to relate all applied methods, raw data and analyses to one specific animal. Despite increasing demands to handle such complex data, most pre-clinical labs have not yet established such an electronic database. Here, we present a novel cloud-based relational database for multimodal animal data, which operates on commercial software. We have implemented data fields for various pre-clinical features such as MRI, histology and behaviour. Automated procedures replace manual and recurrent calculations. Pre-set plotting and printing features provide efficient analysis and documentation. The database template is useful for all labs working with laboratory animals and the adaption to specific research projects requires no prior scripting expertise. The database works operating-system independent through the web browser and allows multiple users to work simultaneously. The data entry is monitored and restricted for particular tests according to the user management in order to keep for example users during the experiment blinded for the experimental group. The database improves data accessibility, standardization of data recording and data handling efficiency in pre-clinical research.