Quantitative phenotype analysis to identify, validate and compare rat disease models. (2nd April 2019)

• Record Type: Journal Article
• Authors: Zhao, Yiqing; Smith, Jennifer R; Wang, Shur-Jen; Dwinell, Melinda R; Shimoyama, Mary
• Is Part Of: Database. Volume 2019(2019)
• Publication Date: 2019-04-02
• Subjects: Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/baz037
• Languages: English
• ISSNs: 1758-0463

Abstract

Abstract: The laboratory rat has been widely used as an animal model in biomedical research. There are many strains exhibiting a wide variety of phenotypes. Capturing these phenotypes in a centralized database provides researchers with an easy method for choosing the appropriate strains for their studies. Existing resources have provided some preliminary work in rat phenotype databases. However, existing resources suffer from problems such as small number of animals, lack of updating, web interface queries limitations and lack of standardized metadata. The Rat Genome Database (RGD) PhenoMiner tool has provided the first step in this effort by standardizing and integrating data from individual studies. Our work, mainly utilizing data curated in RGD, involves the following key steps: (i) we developed a meta-analysis pipeline to automatically integrate data from heterogeneous sources and to produce expected ranges (standardized phenotype ranges) for different strains and phenotypes under different experimental conditions; (ii) we created tools to visualize expected ranges for individual strains and strain groups. We developed a meta-analysis pipeline and an interactive web interface that summarizes and visualizes expected ranges produced from the meta-analysis pipeline. Automation of the pipeline allows for updates as additional data becomes available. The interactive web interface provides curators and researchers with a platform for identifying and validating expected ranges for a variety of quantitative phenotypes. The data analysis result and visualization tools will promote an understanding of rat disease models, guide researchers to choose optimal strains for their research needs and encourage data sharing from different research hubs. Such resources also help to promote research reproducibility. The interactive platforms created in this project will continue to provide a valuable resource for translational research efforts.