MtExpress

MtExpress provides a curated RNAseq-based gene expression atlas for Medicago truncatula by compiling published RNAseq and legacy GeneChip data to profile gene expression across tissues, conditions, and developmental stages.

Key Features:

• Comprehensive Data Integration: Aggregates published RNAseq datasets mapped to recent Medicago truncatula genome sequences and annotations.
• Advanced Methodology: Applies mapping, quality control, statistical analysis, and normalization to generate reliable expression profiles.
• Integration with Genomic Resources: Links expression data with other Medicago genomic resources and updated genome annotations.
• Flexible Query System: Supports searches by gene names and orthologous gene IDs from Arabidopsis and other legume species.
• Customizable Data Visualization: Produces customizable visual representations of gene expression results.
• Legacy Data Inclusion: Includes legacy GeneChip expression data from the Medicago Gene Expression Atlas alongside RNAseq datasets.
• Regular Updates: Uses a semi-automated pipeline to incorporate new RNAseq publications and genome annotation improvements.

Scientific Applications:

• Comparative Genomics: Enables cross-species comparisons using orthologous gene IDs from Arabidopsis and other legumes.
• Functional Genomics: Facilitates identification of expression patterns linked to gene function across tissues and conditions.
• Systems Biology: Supports integrative analyses of gene expression networks and regulatory responses in legumes.
• Developmental and Environmental Response Studies: Allows exploration of expression changes across developmental stages and environmental conditions.
• Symbiosis and Plant–Microbe Interaction Research: Provides expression data relevant to symbiotic interactions in Medicago truncatula.

Methodology:

Aggregation of published RNAseq datasets and legacy GeneChip data; mapping to updated Medicago truncatula genome sequences and annotations; mapping, quality control, statistical analysis, and normalization of RNAseq data; semi-automated pipelines with manual re-labeling and organization of samples; updates incorporating new RNAseq publications and annotation improvements.