TriticeaeExpDB

A Centralized Transcriptomic Resource for Triticeae Research

WELCOME

The Triticeae tribe, comprising globally vital cereal crops (wheat, barley, rye) and their wild relatives, plays an indispensable role in global food security and plant genetics research. Despite the exponential growth of transcriptomic data across public repositories, these resources remain fragmented and lack systematic integration. To address this challenge, we present TriticeaeExpDB, a unified platform aggregating 10,351 high-quality RNA-seq samples from 562 public projects covering 17 agriculturally and evolutionarily significant Triticeae species. This database provides standardized quantification metrics (Count, FPKM, TPM), enables cross-species homologous gene expression analysis, and integrates advanced bioinformatics tools including BLAST alignment, differential expression gene discovery, GO enrichment, weighted gene co-expression network analysis, and bulk data retrieval. Ultimately, TriticeaeExpDB serves as a foundational infrastructure for advancing genome function research, evolutionary insights, and sustainable crop improvement strategies worldwide.

Aegilops bicornis

  • Genome Coverage: Encompasses a curated collection of 17 Triticeae species genomes, representing comprehensive genetic diversity within the tribe.
  • Data Volume:Contains a large-scale RNA-seq dataset comprising 10,351 samples systematically curated from 562 public domain projects.
  • Functional Annotation:Provides multi-level functional and structural gene annotations to facilitate efficient sequence retrieval and interpretation.
  • Cross-Species Analysis:Enables comparative analysis of homologous gene expression patterns across different Triticeae species.
  • Quantitative Metrics:Offers standardized quantification values (Count, FPKM, TPM) through user-friendly search interfaces for downstream applications.
  • Bioinformatics Toolbox:Integrates advanced analytical tools including GO enrichment analysis, differential expression gene discovery, weighted gene co-expression network analysis, and BLAST sequence alignment.