PLEXdb

PLEXdb provides an integrative resource for analysis and cross-species comparison of genome-scale gene expression data from plants and pathogens to support genotype-to-phenotype studies.

Key Features:

• Cross-Species Comparison: Supports comparison of gene expression datasets across plant and pathogen species and comparative analysis of uploaded genome-scale datasets against archived data.
• Gene Atlas Experiments: Hosts Gene Atlas experiments that profile gene expression across developmental stages, tissues, and conditions to reveal dynamic expression patterns.
• Model Genome Interrogator (MGI): Maps gene lists onto corresponding genes from model plants such as rice and Arabidopsis, predicts homologies, and displays gene structures with supporting information for annotated genes and full-length cDNAs.
• Gene List-Processing Wizard: Enables creation, annotation, management, and analysis of gene lists and supports higher-level analyses including ANOVA and clustering using uploaded or derived lists.
• Gene OscilloScope: Tracks changes in gene expression across multiple experiments to identify recurrent expression patterns (e.g., up-regulation) and to evaluate genes for use as steady-state controls.

Scientific Applications:

• Gene function and regulation: Enables exploration of gene expression patterns to infer gene function and regulatory responses across conditions and tissues.
• Plant–pathogen interaction studies: Facilitates analysis of expression datasets relevant to pathogen responses and host–pathogen interactions.
• Developmental biology: Supports investigation of stage- and tissue-specific expression to study developmental processes.
• Comparative genomics: Supports cross-species comparisons and homology-based mapping between crop species and model organisms such as rice and Arabidopsis.
• Hypothesis generation and experimental validation: Provides data and analyses to generate testable hypotheses, validate experimental results, and explore genetic networks.

Methodology:

Computational methods explicitly include comparative analysis of uploaded and archived genome-scale expression datasets, mapping gene lists to model genomes (rice, Arabidopsis), homology prediction, gene structure display with annotated genes and full-length cDNAs, ANOVA and clustering analyses, and tracking expression patterns across experiments.