Kumar P1, Dinglasan E1
1University Of Queensland, St Lucia, Australia
Biography:
Pravesh Kumar is a Principal Research Software Engineer at the Research Computing Centre, The University of Queensland. He specialises in the design and development of research software, data platforms, and visual analytics systems that support data-driven discovery across scientific disciplines. His recent work focuses on building scalable digital infrastructure for agricultural genomics and breeding, integrating complex genomic, phenotypic, and literature-derived datasets into interactive decision-support tools that enable researchers and industry partners to translate research outputs into practical outcomes.
Abstract:
The purpose of this presentation is to demonstrate a web-based research platform that transforms complex genomic and phenotypic datasets into accessible visual analytics and decision-support tools for barley improvement. Designed to support researchers and breeders, the platform provides a unified environment for exploring genetic variation associated with disease resistance and other agronomically important traits.
The scope of the work encompasses the integration of heterogeneous datasets generated through breeding programs, genomic studies, literature resources, and emerging barley pangenome initiatives. These data are consolidated into a searchable knowledgebase containing haploblock–trait associations, phenotypic and genotypic summaries, haplotype profiles, genomic effect estimates, and curated trait knowledge linked to breeding outcomes.
A significant challenge in modern agricultural research is that valuable genomic information is often distributed across multiple databases, publications, and analytical outputs, limiting its accessibility and practical application. To address this issue, the platform combines scalable web technologies, containerised deployment, interactive visualisation, and analytical workflows within a single research infrastructure. A genome-wide Circos view and complementary dashboards enable intuitive exploration of relationships between genomic regions, traits, and germplasm collections, while breeding insight tools support the identification of favourable haplotypes and candidate donor lines.
The outcome is a reusable and scalable eResearch resource that improves the discoverability, accessibility, and application of genomic data. By integrating data management, visual analytics, and breeding decision support within a single platform, the system accelerates the translation of research outputs into breeding outcomes and provides a model for delivering domain-specific research infrastructure to agricultural research communities.
