VitViz

VitViz integrates transcriptomic and metabolomic data for grapevine (Vitis vinifera L.) to enable meta-analyses of gene-to-gene and gene-to-metabolite relationships relevant to fruit development, secondary metabolism, and responses to abiotic and biotic factors.

Key Features:

• Integrative omics analysis: Consolidates transcriptomics and metabolomics datasets for combined analysis.
• Meta-analysis across studies: Performs meta-analyses that integrate data from multiple studies to address variability in metabolomic research such as variations in compounds studied and inconsistent metadata.
• TransMetaDb integrated database: Consolidates transcriptomic and metabolomic data into a simple format via the TransMetaDb database developed under COST Action CA17111 INTEGRAPE.
• Gene relationship exploration: Enables exploration of gene-to-gene and gene-to-metabolite relationships within integrated datasets.
• A priori data mining and computational capacity: Supports high computational capacity for a priori data mining and navigation of meta-analyses across experiments.
• Focus on fruit quality traits: Supports analysis of berry physiology, secondary metabolism, accumulation of health-promoting compounds, and stress resistance.
• FAIR data promotion: Encourages generation of Findable, Accessible, Interoperable, and Reusable (F.A.I.R.) data and deposition of comprehensive datasets into public repositories.

Scientific Applications:

• Berry physiology and secondary metabolism: Investigates mechanisms driving berry physiology and secondary metabolite profiles.
• Fruit quality trait analysis: Studies accumulation of health-promoting compounds and traits related to stress resistance.
• Abiotic and biotic interaction studies: Analyzes grapevine responses to abiotic and biotic factors.
• Meta-analytical synthesis: Integrates heterogeneous metabolomic datasets to synthesize results across experiments.
• Breeding and crop improvement support: Informs grapevine breeding programs and crop improvement strategies by linking genetic and metabolic data.

Methodology:

Integration of transcriptomic and metabolomic datasets into the TransMetaDb database, combined meta-analysis across multiple studies, and a priori data mining for exploration of gene-to-gene and gene-to-metabolite relationships.