Supporting data for "Rice Galaxy: an open resource for plant science"

Dataset type: Genomic, Software
Data released on January 03, 2019

Rice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci (QTL) discovery and molecular marker development. Comparative sequence analyses across QTL regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers. We adopted the Galaxy framework to build the federated Rice Galaxy resource, with shared datasets, tools, and analysis workflows relevant to rice research. The shared datasets include high density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from nine published rice genomes. Rice Galaxy includes tools for designing single nucleotide polymorphism (SNP) assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented. Rice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science.

File NameSample IDData TypeFile FormatSizeRelease Date 
GitHub archivearchive523.57 KB2018-10-29
GitHub archivearchive32.06 MB2018-10-29
readmeTEXT3.13 KB2018-10-29
GitHub archivearchive160.66 MB2018-10-29
GitHub archivearchive8.62 MB2018-10-29
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Funding body Awardee Award ID Comments
National Science Foundation PRAGMA grant number:NSF OCI 1234983
MacArthur Foundation
AIST International Collaboration Grant
Taiwan Council of Agriculture Grant
CGIAR Excellence in Breeding Platform
Date Action
January 3, 2019 Dataset publish
February 4, 2019 External Link updated :
March 11, 2019 Manuscript Link added : 10.1093/gigascience/giz028
April 2, 2020 Manuscript Link added : 10.1093/gigascience/giz156