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PedigreeNet: A Web-based Pedigree Viewer for Biological Databases

Abstract

Motivation

Plant breeding aims to improve current germplasm that can tolerate a wide range of biotic and abiotic stresses. To accomplish this goal, breeders rely on developing a deeper understanding of genetic makeup and relationships between plant varieties to make informed plant selections. Although rapid advances in genotyping technology generated a large amount of data for breeders, tools that facilitate pedigree analysis and visualization are scant, leaving breeders to use classical, but inherently limited, hierarchical pedigree diagrams for a handful of plant varieties. To answer this need, we developed a simple web-based tool that can be easily implemented at biological databases, called PedigreeNet, to create and visualize customizable pedigree relationships in a network context, displaying pre- and user-uploaded data.

Results

As a proof-of-concept, we implemented PedigreeNet at the maize model organism database, MaizeGDB. The PedigreeNet viewer at MaizeGDB has a dynamically-generated pedigree network of 4706 maize lines and 5487 relationships that are currently available as both a stand-alone web-based tool and integrated directly on the MaizeGDB Stock Pages. The tool allows the user to apply a number of filters, select or upload their own breeding relationships, center a pedigree network on a plant variety, identify the common ancestor between two varieties, and display the shortest path(s) between two varieties on the pedigree network. The PedigreeNet code layer is written as a JavaScript wrapper around Cytoscape Web. PedigreeNet fills a great need for breeders to have access to an online tool to represent and visually customize pedigree relationships.

Availability and implementation

PedigreeNet is accessible at https://www.maizegdb.org/breeders_toolbox. The open source code is publically and freely available at GitHub: https://github.com/Maize-Genetics-and-Genomics-Database/PedigreeNet.

Supplementary information

Supplementary data are available at Bioinformatics online.

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