Rice Galaxy: an open resource for plant science

Abstract

Background 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 discovery and molecular marker development. Comparative sequence analyses across quantitative trait loci 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. Findings The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism 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. Conclusions 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.

Genomic Prediction: Progress and Perspectives for Rice Improvement

“…To help the breeders with data management, software solutions such as the Breeding Management System (https://bmspro.io), Breeding4Results (B4R) (https://riceinfo.atlassian.net/wiki/ spaces/ABOUT/pages/326172737/Breeding4Results+B4R), Breedbase (https://breedbase.org), or GOBii Genomic Data Management (https://gobiiproject.atlassian.net/wiki/spaces/GD/ overview) are available and used in different public organizations. Despite the significant efforts to develop analysis pipelines (like the RiceGalaxy, https://galaxy.irri.org, [141]) and the Breeding API project (https://brapi.org) designed to enable interoperability among plant breeding databases, no efficient end to end solution is publicly available to perform genomic prediction in the context of an applied breeding program. Indeed, several limitations are present among available software for implementing genomic prediction, including a lack of direct linkages between genotypic and phenotypic data, limited multi-environment or multi-trait analytical capability, no possibility to integrate dominance or epistasis effects into a prediction model, and no meaningful integration of weather data into an analytical pipeline.…”

RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci

“…Thus, the post-GWAS/QTL mapping task of prioritizing genes or identifying biological mechanisms that link them to the phenotype requires the integration of GWAS/QTL mapping results with genomic information from a host of other data sources. While tools for computational post-GWAS analysis have been reported for other species (e.g., [ 19 , 20 ]), there are a limited number of tools dedicated to rice, one of which is Rice Galaxy [ 21 ] (now folded into CropGalaxy [ 22 ]), which utilizes genome position information and lift-over across a few rice genomes.…”

NG-Circos: next-generation Circos for data visualization and interpretation

“…Other Circos-derived tools, such as Circoletto ( 2 ), CIRCUS ( 3 ), J-Circos ( 4 ), shinyCircos ( 5 ), Rcircos ( 6 ), Circleator ( 7 ), OmicCircos ( 8 ), ggbio ( 9 ) are either incapable to produce interactive Circos plots in a web browser or are limited to specific data types. Our previous developed tool, BioCircos.js ( 10 ), appears to be the only published software capable of producing interactive Circos plots and has become the state-of-the-art tool in the field ( 11 – 12 ). Nonetheless, BioCircos.js ( 10 ) implements only nine functional modules, limiting its scope to perform additional analytical tasks.…”