TeaCoN

TeaCoN provides a gene co-expression network database for Camellia sinensis to support functional characterization of genes involved in secondary metabolism, leaf yield, and stress responses.

Key Features:

• Data coverage: Integrates 261 high-quality RNA-Seq experiments covering 31,968 tea gene models (approximately 94% of the Camellia sinensis genome).
• Gene co-expression analysis: Reports co-expression relationships with Pearson correlation coefficients (PCC-values) and statistical P-values to identify co-expressed gene modules.
• Gene information repository: Stores gene ID, description, symbol, alias, chromosomal location, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations.
• Expression profile visualization: Displays heatmaps of expression profiles of co-expressed genes across seven primary tea tissues, including leaf, bud, stem, and root.
• Network visualization: Visualizes gene co-expression networks to examine gene interactions and module structure.
• Functional analysis support: Performs GO and KEGG enrichment analyses for gene modules.
• Sequence search and genome navigation: Provides BLAST sequence similarity searches and a genome browser for genomic context.

Scientific Applications:

• Candidate gene screening: Facilitates identification of candidate genes associated with traits of interest in Camellia sinensis through co-expression relationships.
• Functional characterization of metabolic genes: Enables exploration of genes involved in secondary metabolite biosynthesis and related pathways using co-expression and enrichment analyses.
• Trait-focused research and breeding support: Supports linking gene network information to studies of leaf yield, stress resistance, and other agronomic traits for molecular breeding and biotechnology research.

Methodology:

Constructs co-expression networks from 261 RNA-Seq experiments using Pearson correlation coefficients (PCC-values) and statistical P-values, and provides GO and KEGG enrichment analyses, BLAST searches, heatmaps, network visualization, and a genome browser.