Tomato Functional Genomics Database (TFGD)

Tomato Functional Genomics Database (TFGD) provides centralized storage, querying, mining, analysis, visualization, and integration of tomato functional genomics datasets including microarray, metabolite profiles, and small RNA (sRNA) data to support exploration of gene function and regulatory relationships.

Key Features:

• Supported data types: Stores microarray, metabolite profiles, and small RNA (sRNA) datasets along with sRNA and mRNA sequence data.
• Storage and querying: Maintains processed datasets and enables structured querying and retrieval of functional genomics data.
• Data mining and analysis: Provides data mining tools and analytical capabilities for systematic interrogation of datasets.
• Visualization: Offers visualization functionalities for exploring processed expression and metabolite data.
• Specialized processing pipelines: Implements pipelines for processing microarray, metabolite profile, and sRNA datasets.
• Processed results availability: Stores processed results in the database to support downstream analysis and integration.
• Gene expression analysis: Enables analysis of gene expression patterns and identification of co-expressed genes.
• Pathway and process analysis: Supports identification of significantly affected biological processes and biochemical pathways.
• Array probe annotations: Includes improved array probe annotations to refine microarray-based analyses.
• miRNA target identification: Provides functionalities to pinpoint miRNA targets from sRNA and mRNA sequence data.
• Multi-omics integration: Facilitates integration of transcript and metabolite profiles with sRNA and mRNA sequences for combined analyses.
• Continuous dataset expansion: Supports mining and incorporation of newly released and expanding tomato genomics datasets.

Scientific Applications:

• Gene expression profiling: Characterizing expression patterns across tomato tissues, treatments, or developmental stages using microarray data.
• Co-expression discovery: Identifying co-expressed gene sets for inference of gene function and regulatory modules.
• miRNA–mRNA interaction analysis: Pinpointing miRNA targets to study post-transcriptional regulation in tomato.
• Pathway impact analysis: Detecting biological processes and biochemical pathways significantly affected under experimental conditions.
• Metabolite–transcript integration: Integrating metabolite profiles with transcript and sRNA/mRNA sequences for multi-omics interrogation of metabolic regulation.
• Microarray reannotation: Applying improved array probe annotations to revisit and refine previous microarray-based findings.
• Large-scale data mining: Mining expanding datasets to discover novel relationships and hypotheses in tomato functional genomics.

Methodology:

Specialized computational pipelines process microarray, metabolite profile, and small RNA (sRNA) datasets; array probe annotation and miRNA target identification workflows are applied and processed results are stored to enable querying, mining, analysis, visualization, and integration with transcript and metabolite profiles and sRNA/mRNA sequences.